Deepak Puthal is an Assistant Professor (in UK, Lecturer) at School of Computing, Newcastle University, UK and Honorary Fellow at Faculty of Engineering and Information Technology at University of Technology Sydney, Australia. His research spans several areas in Cyber Security, with a special focus on user-centric security, blockchain, scalable security solution for IoT, edge/fog computing. He is on the editorial boards of 5 international journals, such as IEEE Transactions on Big Data, IEEE Consumer Electronics Magazine, Computers & Electrical Engineering (Elsevier), International Journal of Communication Systems (John Wiley & Sons), and Internet Technology Letters (John Wiley & Sons). He is the Program Chair of five international conferences/symposiums/workshops. He served as a Co-Guest Editor of several reputed journals including Future Generation Computer Systems, Concurrency and Computation: Practice and Experience, Wireless Communications and Mobile Computing, and Information Systems Frontier. He has been on over 20 TPCs of international conferences and workshops.
He is recipient of 2018 IEEE TCSC Award for Excellence in Scalable Computing (Early Career Researcher) and 2017 IEEE Distinguished Doctoral Dissertation Award (Awarded by IEEE Computer Society and STC on Smart Computing)
He has received two Best Paper Awards in 2018 from IEEE Consumer Electronics Society on his blockchain work published in IEEE Consumer Electronics Magazine and IETE Best Research Award -2018 for his work on green cloud computing.
One of his paper published in IEEE Cloud Computing appeared as technology spotlight/highlight paper.
- IEEE Transactions on Big Data
- Computers & Electrical Engineering (Elsevier)
- International Journal of Communication Systems (John Wiley & Sons)
- IEEE Consumer Electronics Magazine
- Internet Technology Letters (John Wiley & Sons)
- KSII Transactions on Internet and Information Systems (TIIS)
He is also reviewer of several international journals and IEEE Transactions.
Can supervise: YES
- Cyber Security
- Internet of Things (IoT)
- Distributed Computing
- Edge/Fog Computing
- Wireless Networks
- Internet of Things
- Fundamentals of Security
- Network Security
- Cyber Security
Puthal, D 2012, Secure Data Collection & Critical Data Transmission in Mobile Sink WSN: Secure and Energy efficient data collection technique, LAP LAMBERT Academic Publishing, Germany.
This book gives an excellent overview on secure and energy efficient data collection taking sink as the dynamic and other sensors are static in Wireless Sensor Networks. Mobile sink wireless sensor networks (MSWSN) Sensor nodes are low cost tiny devices with limited storage, computational capability and power except the sink node. Mobile sink has no resource limitation. Here we proposed energy efficient secure data collection techniques with mobile sink wireless sensor networks and secure the data collection process using symmetric key cryptography. we also proved an existing protocol Sensor Protocol for Information via Negation (SPIN) is efficient for critical data transmission to the mobile sink. Then we make it as the secured protocol by using symmetric key cryptography. All the simulation has been carried out with NS 2.34. This work is supported by the literature survey in the area of Mobile Sink Wireless Sensor Networks to make it complete.
Kumar, N, Puthal, D, Theocharides, T & Mohanty, SP 2019, 'Unmanned Aerial Vehicles in Consumer Applications', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 66-67.View/Download from: Publisher's site
Liu, M, Luo, Y, Yang, C, Pang, S, Puthal, D, Ren, K & Zhang, X 2019, 'Privacy-preserving matrix product based static mutual exclusive roles constraints violation detection in interoperable role-based access control', Future Generation Computer Systems.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier B.V. Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.
Luo, S, Wen, Y, Xu, W & Puthal, D 2019, 'Adaptive Task Offloading Auction for Industrial CPS in Mobile Edge Computing', IEEE Access, vol. 7, pp. 169055-169065.View/Download from: UTS OPUS or Publisher's site
© 2019 IEEE. The emerging intelligent applications in Industrial Cyber-Physical Systems (ICPS), such as product inspection by deep-learning-based image recognition technology, are highly computation-consuming. However, the smart devices without sufficient computing resources fail to handle this kind of applications. Moreover, the Internet has very high latency compared with the local network which fails to meet the requirements of time-sensitive tasks, therefore we can not offload these tasks over the cloud. Mobile Edge Computing (MEC) brings the opportunities to offload the tasks of ICPS to the MEC servers to satisfy strict latency requirements, as well as to meet the demand for security requirements. Considering MEC servers owned by the third parties, resource allocation in MEC should be solved jointly with network economics to maximize the utility of system. In this paper, we investigate the task offloading problem under the access capability, latency and security constraints. Specifically, we present a novel Adaptive Task Offloading (ATO) auction mechanism to determine which MEC server to offload with access capability and security constraints, and how to schedule tasks with various deadline constraints, which incentives the third party of MEC providers to share their computing resources with the maximum profit. According to our theoretical analysis, the proposed auction mechanism has the properties of individual rationality, computational efficiency and truthfulness. Extensive simulations have been conducted to evaluate the performance of ATO auction and the experimental results show our method provides better solutions with the classic greedy algorithms in terms of maximizing the utility of the MEC server.
Mishra, AK, Tripathy, AK, Puthal, D & Yang, LT 2019, 'Analytical Model for Sybil Attack Phases in Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 379-387.View/Download from: UTS OPUS or Publisher's site
IEEE The sybil attack in IoT commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into 3 phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on K-mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of Internet of Things.
IEEE Internet of Things (IoT) provides a promising opportunity to build powerful data analytics systems with real time event detection for smart health, and therefore wearable IoT has become a rising source of big data streams for smart health, for which security needs to be assured by detecting real-time event to avoid malicious activities, and meanwhile to control the information leakage of big sensing data streams. I refer to this as an information flow control problem. To address this problem, this paper proposes a static lattice model for information flow control over big sensing data streams. I initialize two static lattices i.e. sensor lattice for wearable sensors and user lattice for users, and then static lattices aim to process the flow control model faster, because I am dealing with high volume and velocity of data streams. The experimental evaluation and results of the information flow model show that it can excellently handle the incoming big data streams with low latency and buffer requirement.
© 2018 IEEE. This article introduces the concept of proof of authentication (PoAh) for the lightweight implementation of blockchains in the Internet of Things (IoT). The PoAh can replace existing consensus algorithms, such as proof of work (PoW), proof of stake (PoS), and proof of activity (PoA), for resource- and energyconstrained infrastructures, such as the IoT.
Puthal, D, Mohanty, SP, Bhavake, SA, Morgan, G & Ranjan, R 2019, 'Fog Computing Security Challenges and Future Directions', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 92-96.View/Download from: Publisher's site
Yanambaka, VP, Mohanty, SP, Kougianos, E & Puthal, D 2019, 'PMsec: Physical Unclonable Function-Based Robust and Lightweight Authentication in the Internet of Medical Things', IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, vol. 65, no. 3, pp. 388-397.View/Download from: Publisher's site
Yang, CT, Chen, ST, Liu, JC, Su, YW, Puthal, D & Ranjan, R 2019, 'A predictive load balancing technique for software defined networked cloud services', Computing, vol. 101, no. 3, pp. 211-235.View/Download from: UTS OPUS or Publisher's site
© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. With the advent of OpenFlow, the concept of Software-Defined Networking (SDN) becomes much popular. In the past, SDN had often been used for network virtualization; however, with the rise of OpenFlow, which speeds up network performance by separating the control layer from the data layer, SDN can be further used to manage physical network facilities. Currently, some OpenFlow controller providers have already provided users with load balancer packages in their controllers for virtual networks, such as the Neutron package in OpenStack; nevertheless, the existing load balancer packages work in the old fashion that causes extra delay since they poll controllers for every new coming connection. In this paper, we use the wildcard mask to implement the load balance method directly on switches or routers and add a user prediction mechanism to change the range of the wildcard mask dynamically. In this way, the load balance mechanism can be applied conforming to real service situations. In our experiment, we test the accuracies of flow prediction for different predicted algorithms and compare the delay times and balance situations of the proposed method with other load balancers. With the popularity of cloud computing, the demand for cloud infrastructure also increases. As a result, we also apply our load balance mechanism on cloud services and prove that the proposed method can be implemented to varieties of service platforms.
Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2019, 'Fuzzy knowledge based performance analysis on big data', Neurocomputing.View/Download from: Publisher's site
© 2019 Elsevier B.V. Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM)algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM)called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
Shit, RC, Sharma, S, Puthal, D, James, P, Pradhan, B, van Moorsel, A, Zomaya, AY & Ranjan, R 2019, 'Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World', IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, vol. 21, no. 4, pp. 3532-3564.View/Download from: Publisher's site
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.View/Download from: UTS OPUS or Publisher's site
As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Puthal, D, Ranjan, R, Nanda, A, Nanda, P, Jayaraman, PP & Zomaya, AY 2019, 'Secure authentication and load balancing of distributed edge datacenters', Journal of Parallel and Distributed Computing, vol. 124, pp. 60-69.View/Download from: UTS OPUS or Publisher's site
© 2018 Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.
Bhoi, SK, Puthal, D, Khilar, PM, Rodrigues, JJPC, Panda, SK & Yang, LT 2018, 'Adaptive routing protocol for urban vehicular networks to support sellers and buyers on wheels', Computer Networks, vol. 142, pp. 168-178.View/Download from: UTS OPUS or Publisher's site
© 2018 Marketing on wheels is an emerging area of research, where users can buy or sell items using inter vehicular communication and web is not required. For quicker delivery of items, the communication between the vehicles should be faster. In this paper, a routing protocol is proposed for urban vehicular ad hoc network (VANET) to send the messages from the buyer to the seller or vice versa in a minimum time to get faster service. Currently, many routing protocols are proposed for urban environment, which are based on the methods of shortest path, high density, minimum packet forwarding delay, and intermediate junction selection. However, these protocols lack knowledge about the communication gaps generated between the junctions before forwarding the data. In this method, the RSU at the junction calculates a path value for each path to choose the next path. The parameters used for path value calculation are number of network fragments between the junctions, delay to send the data from to other junction, and destination closeness. Then, the path which has a less path value is chosen as the next path. Results show that proposed protocol performs better than the existing city based routing protocols in terms of delay, number of network fragments or gaps encountered, path length, total service time, and packet delivery ratio. At last, the proposed routing protocol is validated by conducting an experiment in a small VANET platform designed in an indoor laboratory environment (https://www.youtube.com/watch?v=H3UmgI5AAH0).
Khan, F, ur Rehman, A, Usman, M, Tan, Z & Puthal, D 2018, 'Performance of Cognitive Radio Sensor Networks Using Hybrid Automatic Repeat ReQuest: Stop-and-Wait', Mobile Networks and Applications, vol. 23, no. 3, pp. 479-488.View/Download from: UTS OPUS or Publisher's site
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model.
Kumar Mishra, S, Puthal, D, Sahoo, B, Sharma, S, Xue, Z & Zomaya, AY 2018, 'Energy-efficient deployment of edge dataenters for mobile clouds in sustainable iot', IEEE Access, vol. 6, pp. 56587-56597.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. Achieving quick responses with limited energy consumption in mobile cloud computing is an active area of research. The energy consumption increases when a user's request (task) runs in the local mobile device instead of executing in the cloud. Whereas, latency become an issue when the task executes in the cloud environment instead of the mobile device. Therefore, a tradeoff between energy consumption and latency is required in building sustainable Internet of Things (IoT), and for that, we have introduced a middle layer named an edge computing layer to avoid latency in IoT. There are several real-time applications, such as smart city and smart health, where mobile users upload their tasks into the cloud or execute locally. We have intended to minimize the energy consumption of a mobile device as well as the energy consumption of the cloud system while meeting a task's deadline, by offloading the task to the edge datacenter or cloud. This paper proposes an adaptive technique to optimize both parameters, i.e., energy consumption and latency by offloading the task and also by selecting the appropriate virtual machine for the execution of the task. In the proposed technique, if the specified edge datacenter is unable to provide resources, then the user's request will be sent to the cloud system. Finally, the proposed technique is evaluated using a real-world scenario to measure its performance and efficiency. The simulation results show that the total energy consumption and execution time decrease after introducing an edge datacenters as a middle layer.
Mishra, SK, Puthal, D, Sahoo, B, Jayaraman, PP, Jun, S, Zomaya, AY & Ranjan, R 2018, 'Energy-efficient VM-placement in cloud data center', SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, vol. 20, pp. 48-55.View/Download from: UTS OPUS or Publisher's site
Mishra, SK, Puthal, D, Sahoo, B, Jena, SK & Obaidat, MS 2018, 'An adaptive task allocation technique for green cloud computing', Journal of Supercomputing, vol. 74, no. 1, pp. 370-385.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer Science+Business Media, LLC. The rapid growth of todays IT demands reflects the increased use of cloud data centers. Reducing computational power consumption in cloud data center is one of the challenging research issues in the current era. Power consumption is directly proportional to a number of resources assigned to tasks. So, the power consumption can be reduced by a demotivating number of resources assigned to serve the task. In this paper, we have studied the energy consumption in cloud environment based on varieties of services and achieved the provisions to promote green cloud computing. This will help to preserve overall energy consumption of the system. Task allocation in the cloud computing environment is a well-known problem, and through this problem, we can facilitate green cloud computing. We have proposed an adaptive task allocation algorithm for the heterogeneous cloud environment. We applied the proposed technique to minimize the makespan of the cloud system and reduce the energy consumption. We have evaluated the proposed algorithm in CloudSim simulation environment, and simulation results show that our proposed algorithm is energy efficient in cloud environment compared to other existing techniques.
Nanda, A, Puthal, D, Mohanty, SP & Choppali, U 2018, 'A Computing Perspective on Quantum Cryptography', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 7, no. 6, pp. 57-59.View/Download from: UTS OPUS or Publisher's site
Puthal, D & Zhang, X 2018, 'Secure Computing for the Internet of Things and Network Edges Protecting communication in the worldwide network of devices', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 7, no. 6, pp. 29-30.View/Download from: UTS OPUS or Publisher's site
The Internet of Things (IoT) is focused on architectures and protocols for the efficient interconnection of heterogeneous things, deployment of infrastructure, and creation of value-added services. The IoT has become an emerging technology, where more than 50 billion things will be connected to the Internet by 2020, and data in the network will grow by another 1,000 times. The framework of the IoT is the backbone for several applications, such as smart cities and smart homes, where most of the data move to the cloud for further evaluation because of the resource poverty of IoT devices.
Puthal, D, Malik, N, Mohanty, SP, Kougianos, E & Das, G 2018, 'Everything You Wanted to Know about the Blockchain: Its Promise, Components, Processes, and Problems', IEEE Consumer Electronics Magazine, vol. 7, no. 4, pp. 6-14.View/Download from: UTS OPUS or Publisher's site
© 2012 IEEE. In 2008, the emergence of the blockchain as the foundation of the first-ever decentralized cryptocurrency not only revolutionized the financial industry but proved a boon for peer-to-peer (P2P) information exchange in the most secure, efficient, and transparent manner. The blockchain is a public ledger that works like a log by keeping a record of all transactions in chronological order, secured by an appropriate consensus mechanism and providing an immutable record. Its exceptional characteristics include immutability, irreversibility, decentralization, persistence, and anonymity.
Puthal, D, Malik, N, Mohanty, SP, Kougianos, E & Yang, C 2018, 'The Blockchain as a Decentralized Security Framework', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 7, no. 2, pp. 18-21.View/Download from: UTS OPUS or Publisher's site
Rodrigues, JJPC, Sahoo, B & Dash, R 2018, 'An early detection of low rate DDoS attack to SDN based data center networks using information distance metrics', Future Generation Computer Systems, vol. 89, pp. 685-697.View/Download from: UTS OPUS or Publisher's site
© 2018 The primary innovations behind Software Defined Networks (SDN) are the decoupling of the control plane from the data plane and centralizing the network management through a specialized application running on the controller. In spite of many advantages, SDN based data centers’ security issues is still a matter of concern among the research communities. Although SDN becomes a valuable tool to defeat attackers, at the same time SDN itself becomes a victim of Distributed Denial-of-Service (DDoS) attacks due to the potential vulnerabilities exist across various SDN layer. The logically centralized controller is always an attractive target for DDoS attack. Hence, it is important to have a fast as well as accurate detection model to detect the control layer attack traffic at an early stage. We have employed information distance (ID) as a metric to detect the attack traffic at the controller. The ID metric can quantify the deviations of network traffic with different probability distributions. In this paper, taking the advantages of flow based nature of SDN, we proposed a Generalized Entropy (GE) based metric to detect the low rate DDoS attack to the control layer. The experimental results show that our detection mechanism improves the detection accuracy as compared to Shannon entropy and other statistical information distance metrics.
Roy, SS, Puthal, D, Sharma, S, Mohanty, SP & Zomaya, AY 2018, 'Building a Sustainable Internet of Things: Energy-Efficient Routing Using Low-Power Sensors Will Meet the Need', IEEE Consumer Electronics Magazine, vol. 7, no. 2, pp. 42-49.View/Download from: UTS OPUS or Publisher's site
© 2012 IEEE. The Internet of Things (IoT) is a framework built as a network of trillions of devices (called things) communicating with each other to offer innovative solutions to real-time problems. These devices monitor the physical environment and disseminate collected data back to the base station. In many cases, the sensor nodes have limited resources like energy, memory, low computational speed, and communication bandwidth. In this network scenario, sensors near the data collector drain energy faster than other nodes in the network. A mobile sink is a solution in sensor networks in which the network is balanced with node energy consumption by using a mobile sink in the sensing area. However, the position of the mobile sink instigates packet overhead and energy consumption. This article discusses a novel data-routing technique to forward data toward a base station using a mobile data collector, in which two data collectors follow a predefined path to collect data by covering the entire network. The proposed technique improves the network performance, including energy consumption and sensing area lifetime.
Sahoo, KS, Puthal, D, Obaidat, MS, Sarkar, A, Mishra, SK & Sahoo, B 2018, 'On the placement of controllers in software-Defined-WAN using meta-heuristic approach', Journal of Systems and Software, vol. 145, pp. 180-194.View/Download from: UTS OPUS or Publisher's site
© 2018 Elsevier Inc. Software Defined Networks (SDN) is a popular modern network technology that decouples the control logic from the underlying hardware devices. The control logic has implemented as a software entity that resides in a server called controller. In a Software-Defined Wide Area Network (SDWAN) with n nodes; deploying k number of controllers (k < n) is one of the challenging issue. Due to some internal or external factors, when the primary path between switch to controller fails, it severely interrupt the networks’ availability. In this regard, the proposed approach provides a seamless backup mechanism against single link failure with minimum communication delay based on the survivability model. In order to obtain an efficient solution, we have considered controller placement problem (CPP) as a multi-objective combinatorial optimization problem and solve it using two population-based meta-heuristic techniques such as: Particle Swarm Optimization (PSO) and FireFly Algorithm (FFA). For CPP, three metrics have been considered: (a) controller to switch latency, (b) inter-controller latency and (c) multi-path connectivity between the switch and controller. The performance of the algorithms is evaluated on a set of publicly available network topologies in order to obtain the optimum number of controllers, and controller positions. Then we present Average Delay Rise (ADR) metric to measure the increased delay due to the failure of the primary path. By comparing the performance of our scheme to competing scheme, it was found that our proposed scheme effectively improves the survivability of the control path and the performance of the network as well.
Shit, RC, Sharma, S, Puthal, D & Zomaya, AY 2018, 'Location of Things (LoT): A review and taxonomy of sensors localization in IoT infrastructure', IEEE Communications Surveys and Tutorials, vol. 20, no. 3, pp. 2028-2061.View/Download from: UTS OPUS or Publisher's site
© 1998-2012 IEEE. Internet of Things (IoT) is a novel design paradigm, intended as a network of billions to trillions of tiny sensors communicating with each other to offer innovative solutions to real time problems. These sensors form a network named as wireless sensor networks (WSNs) to monitor physical environment and disseminate collected data back to the base station through multiple hops. WSN has the capability to collect and report data for a specific application. The location information plays an important role for various wireless sensor network applications. A majority of the applications are related to location-based services. The development of sensor technology, processing techniques, and communication systems give rise to a development of the smart sensor for the adaptive and innovative application. So a single localization technique is not adequate for all application. In this paper, a recent extensive analysis of localization techniques and hierarchical taxonomy and their applications in the different context is presented. This taxonomy of the localization technique is classified based on presence of offline training in localization, namely self-determining and training dependent approaches. Finally, various open research issues related to localization schemes for IoT are compared and various directions for future research are proposed.
Tiwary, M, Puthal, D, Sahoo, KS, Sahoo, B & Yang, LT 2018, 'Response time optimization for cloudlets in Mobile Edge Computing', JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 119, pp. 81-91.View/Download from: UTS OPUS or Publisher's site
Mishra, SK, Puthal, D, Rodrigues, JJPC, Sahoo, B & Dutkiewicz, E 2018, 'Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications', IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4497-4506.View/Download from: UTS OPUS or Publisher's site
© 2005-2012 IEEE. Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.
El-Sayed, H, Sankar, S, Prasad, M, Puthal, D, Gupta, A, Mohanty, M & Lin, CT 2018, 'Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment', IEEE Access, vol. 6, pp. 1706-1717.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network de vices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.
Lin, CT, Prasad, M, Chung, CH, Puthal, D, El-Sayed, H, Sankar, S, Wang, YK, Singh, J & Sangaiah, AK 2018, 'IoT-Based Wireless Polysomnography Intelligent System for Sleep Monitoring', IEEE Access, vol. 6, pp. 405-414.View/Download from: UTS OPUS or Publisher's site
Polysomnography (PSG) is considered the gold standard in the diagnosis of obstructive sleep apnea (OSA). The diagnosis of OSA requires an overnight sleep experiment in a laboratory. However, due to limitations in relation to the number of labs and beds available, patients often need to wait a long time before being diagnosed and eventually treated. In addition, the unfamiliar environment and restricted mobility when a patient is being tested with a polysomnogram may disturb their sleep, resulting in an incomplete or corrupted test. Therefore, it is posed that a PSG conducted in the patient's home would be more reliable and convenient. The Internet of Things (IoT) plays a vital role in the e-Health system. In this paper, we implement an IoT-based wireless polysomnography system for sleep monitoring, which utilizes a battery-powered, miniature, wireless, portable, and multipurpose recorder. A Java-based PSG recording program in the personal computer is designed to save several bio-signals and transfer them into the European data format. These PSG records can be used to determine a patient's sleep stages and diagnose OSA. This system is portable, lightweight, and has low power-consumption. To demonstrate the feasibility of the proposed PSG system, a comparison was made between the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system. Several healthy volunteer patients participated in the PSG experiment and were monitored by both the standard PSG-Alice 5 Diagnostic Sleep System and the proposed system simultaneously, under the supervision of specialists at the Sleep Laboratory in Taipei Veteran General Hospital. A comparison of the results of the time-domain waveform and sleep stage of the two systems shows that the proposed system is reliable and can be applied in practice. The proposed system can facilitate the long-Term tracing and research of personal sleep monitoring at home.
El-Sayed, H, Sankar, S, Daraghmi, Y-A, Tiwari, P, Rattagan, E, Mohanty, M, Puthal, D & Prasad, M 2018, 'Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier.', Sensors (Basel, Switzerland), vol. 18, no. 6.View/Download from: UTS OPUS or Publisher's site
Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.
Lenka, RK, Rath, AK, Tan, Z, Sharma, S, Puthal, D, Simha, NVR, Prasad, M, Raja, R & Tripathi, SS 2018, 'Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors', IEEE Access, vol. 6, pp. 30162-30173.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.
Fan, X, He, X, Xiang, C, Puthal, D, Gong, L, Nanda, P & Fang, G 2018, 'Towards System Implementation and Data Analysis for Crowdsensing BasedOutdoor RSS Maps', IEEE Access, vol. 6.View/Download from: UTS OPUS or Publisher's site
With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., WiFi) has become a pervasive demand. Most mobile users expect seamless network connection with low cost. Indeed,
this can be achieved by using an accurate received signal strength (RSS) map of wireless access points. While existing methods are either costly or unscalable, the recently emerged mobile crowdsensing (MCS)
paradigm is a promising technique for building RSS maps. MCS applications leverage pervasive mobile devices to collaboratively collect data. However, the heterogeneity of devices and the mobility of users
could cause inherent noises and blank spots in collected dataset. In this paper, we study (1) how to tame the sensing noises from heterogenous mobile devices, and (2) how to construct accurate and complete RSS
maps with random mobility of crowdsensing participants. First, we build a mobile crowdsensing system called iMap to collect RSS measurements with heterogeneous mobile devices. Second, through observing
experimental results, we build statistical models of sensing noises and derive different parameters for each kind of mobile device. Third, we present the signal transmission model with measurement error model, and we propose a novel signal recovery scheme to construct accurate and complete RSS maps. The evaluation results show that the proposed method can achieve 90% and 95% recovery rate in geographic coordinate
system and polar coordinate system, respectively.
Puthal, D, Obaidat, MS, Nanda, P, Prasad, M, Mohanty, SP & Zomaya, AY 2018, 'Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing', IEEE Communications Magazine, vol. 56, no. 5, pp. 60-65.View/Download from: UTS OPUS or Publisher's site
Fog computing is a recent research trend to bring cloud computing services to network edges. EDCs are deployed to decrease the latency and network congestion by processing data streams and user requests in near real time. EDC deployment is distributed in nature and positioned between cloud data centers and data sources. Load balancing is the process of redistributing the work load among EDCs to improve both resource utilization and job response time. Load balancing also avoids a situation where some EDCs are heavily loaded while others are in idle state or doing little data processing. In such scenarios, load balancing between the EDCs plays a vital role for user response and real-time event detection. As the EDCs are deployed in an unattended environment, secure authentication of EDCs is an important issue to address before performing load balancing. This article proposes a novel load balancing technique to authenticate the EDCs and find less loaded EDCs for task allocation. The proposed load balancing technique is more efficient than other existing approaches in finding less loaded EDCs for task allocation. The proposed approach not only improves efficiency of load balancing; it also strengthens the security by authenticating the destination EDCs.
Yang, N, Fan, X, Puthal, D, He, X, Nanda, P & Guo, S 2018, 'A Novel Collaborative Task Offloading Scheme for Secure and Sustainable Mobile Cloudlet Networks', IEEE Access, vol. 6.View/Download from: UTS OPUS or Publisher's site
OAPA With the advancement of wireless networking technologies and communication infrastructures, mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity-limited mobile devices. More specifically, at the network edge, the resource-rich and trusted cloudlet system can provide in-proximity computing services by executing the workloads for nearby devices. Nevertheless, there are chances for malicious users to generate DDoS (Distributed Denial-of-Service) flooding tasks to overwhelm cloudlet servers and block computing services from legitimate users. Load balancing is one of the most effective methods to solve DDoS attacks in distributed networks. However, existing solutions require overall load information to achieve load balancing in cloudlet networks, making it costly in both communication and computation. To achieve more efficient and low-cost load balancing, we propose CTOM, a novel Collaborative Task Offloading scheMe to avoid DDoS attacks for secure and sustainable mobile cloudlet networks. The proposed solution is based on the balls-and-bins theory and it can balance the task loads with extremely limited information. The CTOM reduces the number of overloaded cloudlets smoothly, thus handling the potential DDoS attacks in mobile cloudlet networks. Extensive simulations and evaluation demonstrate that, the proposed CTOM outperforms the conventional random and proportional allocation schemes in reducing the task gaps between maximum load and minimum load among mobile cloudlets by 65% and 55%, respectively.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2017, 'A dynamic prime number based efficient security mechanism for big sensing data streams', Journal of Computer and System Sciences, vol. 83, no. 1, pp. 22-42.View/Download from: UTS OPUS or Publisher's site
Big data streaming has become an important paradigm for real-time processing of massive continuous data flows in large scale sensing networks. While dealing with big sensing data streams, a Data Stream Manager (DSM) must always verify the security (i.e. authenticity, integrity, and confidentiality) to ensure end-to-end security and maintain data quality. Existing technologies are not suitable, because real time introduces delay in data stream. In this paper, we propose a Dynamic Prime Number Based Security Verification (DPBSV) scheme for big data streams. Our scheme is based on a common shared key that updated dynamically by generating synchronized prime numbers. The common shared key updates at both ends, i.e., source sensing devices and DSM, without further communication after handshaking. Theoretical analyses and experimental results of our DPBSV scheme show that it can significantly improve the efficiency of verification process by reducing the time and utilizing a smaller buffer size in DSM.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2017, 'DLSeF: A dynamic key-length-based efficient real-time security verification model for big data stream', ACM Transactions on Embedded Computing Systems, vol. 16, no. 2.View/Download from: UTS OPUS or Publisher's site
© 2016 ACM. Applications in risk-critical domains such as emergency management and industrial control systems need near-real-time stream data processing in large-scale sensing networks. The key problem is how to ensure online end-to-end security (e.g., confidentiality, integrity, and authenticity) of data streams for such applications. We refer to this as an online security verification problem. Existing data security solutions cannot be applied in such applications as they cannot deal with data streams with high-volume and high-velocity data in real time. They introduce a significant buffering delay during security verification, resulting in a requirement for a large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key-Length-Based Security Framework (DLSeF) based on a shared key derived from synchronized prime numbers; the key is dynamically updated at short intervals to thwart potential attacks to ensure end-to-end security. Theoretical analyses and experimental results of the DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time and buffer usage without compromising security.
Sharma, S, Puthal, D, Jena, SK, Zomaya, AY & Ranjan, R 2017, 'Rendezvous based routing protocol for wireless sensor networks with mobile sink', Journal of Supercomputing, vol. 73, no. 3, pp. 1168-1188.View/Download from: UTS OPUS or Publisher's site
© 2016 Springer Science+Business Media New YorkIn wireless sensor networks, the sensor nodes find the route towards the sink to transmit data. Data transmission happens either directly to the sink node or through the intermediate nodes. As the sensor node has limited energy, it is very important to develop efficient routing technique to prolong network life time. In this paper we proposed rendezvous-based routing protocol, which creates a rendezvous region in the middle of the network and constructs a tree within that region. There are two different modes of data transmission in the proposed protocol. In Method 1, the tree is directed towards the sink and the source node transmits the data to the sink via this tree, whereas in Method 2, the sink transmits its location to the tree, and the source node gets the sink’s location from the tree and transmits the data directly to the sink. The proposed protocol is validated through experiment and compared with the existing protocols using some metrics such as packet delivery ratio, energy consumption, end-to-end latency, network life time.
Yang, C, Puthal, D, Mohanty, SP & Kougianos, E 2017, 'Big-Sensing-Data Curation for the Cloud is Coming: A Promise of Scalable Cloud-Data-Center Mitigation for Next-Generation IoT and Wireless Sensor Networks', IEEE Consumer Electronics Magazine, vol. 6, no. 4, pp. 48-56.View/Download from: UTS OPUS or Publisher's site
© 2012 IEEE. Modern sensing devices play a pivotal role in achieving data acquisition, communication, and dissemination for the Internet of Things (IoT). Naturally, IoT applications and intelligent sensing systems supported by sensing devices, such as wireless sensor networks (WSNs), are closely coupled. Modern intelligent sensing systems generate huge volumes of sensing data, well beyond the processing capabilities of common techniques and tools. As a result, collecting, managing, and processing IoT big sensing data within an acceptable time duration is a new challenge for both research and industrial applications. The massive size, extreme complexity, and high speed of big sensing data bring new technical requirements including data collection, data storage, data organization, data analysis, and data publishing in real time when deploying real-world IoT applications. To better facilitate these IoT applications, the convergent research of WSNs, big data, the IoT, and cloud computing is a natural scientific development trend. In this article, we concentrate on big-sensing-data curation and preparation issues with cloud computing under the theme of the IoT. There are three especially critical issues that need to be addressed: scalable big-sensing-data cleaning, scalable big-sensing-data compression, and cloud-based data curation response for IoT device optimization. Viewed from the IoT side, all IoT sensing devices are integrated together in an adaptive solution and upload their data onto the cloud. The automatic responses from both the cloud and intelligent sensors will change the status or behavior of sensing devices and, therefore, the status of the IoT itself.
Sharma, S, Puthal, D, Tazeen, S, Prasad, M & Zomaya, AY 2017, 'MSGR: A Mode-Switched Grid-Based Sustainable Routing Protocol for Wireless Sensor Networks', IEEE Access, vol. 5, pp. 19864-19875.View/Download from: UTS OPUS or Publisher's site
© 2013 IEEE. A Wireless Sensor Network (WSN) consists of enormous amount of sensor nodes. These sensor nodes sense the changes in physical parameters from the sensing range and forward the information to the sink nodes or the base station. Since sensor nodes are driven with limited power batteries, prolonging the network lifetime is difficult and very expensive, especially for hostile locations. Therefore, routing protocols for WSN must strategically distribute the dissipation of energy, so as to increase the overall lifetime of the system. Current research trends from areas, such as from Internet of Things and fog computing use sensors as the source of data. Therefore, energy-efficient data routing in WSN is still a challenging task for real-Time applications. Hierarchical grid-based routing is an energy-efficient method for routing of data packets. This method divides the sensing area into grids and is advantageous in wireless sensor networks to enhance network lifetime. The network is partitioned into virtual equal-sized grids. The proposed mode-switched grid-based routing protocol for WSN selects one node per grid as the grid head. The routing path to the sink is established using grid heads. Grid heads are switched between active and sleep modes alternately. Therefore, not all grid heads take part in the routing process at the same time. This saves energy in grid heads and improves the network lifetime. The proposed method builds a routing path using each active grid head which leads to the sink. For handling the mobile sink movement, the routing path changes only for some grid head nodes which are nearer to the grid, in which the mobile sink is currently positioned. Data packets generated at any source node are routed directly through the data disseminating grid head nodes on the routing path to the sink.
Nanda, P, Puthal, D, Mohanty, S & Choppali 2017, 'Building Security Perimeters to Protect Network Systems Against Cyberthreats', IEEE Consumer Electronics Magazine.View/Download from: UTS OPUS or Publisher's site
Due to the wide variety of devices
used in computer network
systems, cybersecurity plays a
major role in securing and
improving the performance of the network
or system. Although cybersecurity
has received a large amount of global
interest in recent years, it remains an
open research space. Current security
solutions in network-based cyberspace
provide an open door to attackers by
communicating first before authentication,
thereby leaving a black hole for an
attacker to enter the system before
authentication. This article provides an
overview of cyberthreats, traditional
security solutions, and the advanced
security model to overcome current
Za'in, C, Pratama, M, Prasad, M, Puthal, D, Lim, CP & Seera, M 2018, 'Motor fault detection and diagnosis based on a meta-cognitive random vector functional link network' in Fault Diagnosis of Hybrid Dynamic and Complex Systems, Springer, Switzerland, pp. 15-44.View/Download from: UTS OPUS or Publisher's site
© 2017 by Taylor & Francis Group, LLC. The leverage of high-throughput technologies in biology area brings the academia and industry an enormous amount of “omics” data. These data include genomics data and proteomics data. In this chapter we consider mostly on the genomics data. Benefited from the development of “Big Data” area and also the domain knowledge driven by genomics data, two subsequent areas including precision medicine and cancer genomics, are discussed in this chapter. Meanwhile, we consider genomics data from the “Big Data” landscape and give a comprehensive “life cycle” on these data. Two significant and state-of-the-art cases in genomics data study are also presented. These two cases, which are ENCODE and CGHub, show inspiring and interesting results by the integration of big data analytics technology in genomics data. As the life science, biomedicine and health care sectors are at a turning point into data intensive science. Since we could benefit from the overwhelming genomics data, big data analytics shows us a promising potential to deliver a better understanding and improvement of our life.
Rajora, S, Li, DL, Jha, C, Bharill, N, Patel, OP, Joshi, S, Puthal, D & Prasad, M 2018, 'A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance', Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, IEEE Symposium Series on Computational Intelligence, IEEE, Bangalore, India, India, pp. 1958-1963.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.
Tiwary, M, Sharma, S, Mishra, P, El-Sayed, H, Prasad, M & Puthal, D 2018, 'Building Scalable Mobile Edge Computing by Enhancing Quality of Services', 2018 International Conference on Innovations in Information Technology (IIT), International Conference on Innovations in Information Technology, IEEE, Al Ain, United Arab Emirates, pp. 141-146.View/Download from: UTS OPUS or Publisher's site
Puthal, D, Mohanty, SP, Nanda, P, Kougianos, E & Das, G 2019, 'Proof-of-Authentication for Scalable Blockchain in Resource-Constrained Distributed Systems', 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), IEEE International Conference on Consumer Electronics (ICCE), IEEE, Las Vegas, NV.View/Download from: UTS OPUS or Publisher's site
Bhoi, SK, Obaidat, MS, Puthal, D, Singh, M & Hsiao, K-F 2018, 'Software Defined Network Based Fault Detection in Industrial Wireless Sensor Networks', 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), IEEE Global Communications Conference (GLOBECOM), IEEE, Abu Dhabi, U ARAB EMIRATES.View/Download from: UTS OPUS
Dalai, AK, Jena, A, Sharma, S, Mohapatra, A, Sahoo, B, Obaidat, MS, Sadoun, B & Puthal, D 2018, 'A fingerprinting technique for identification of wireless devices', CITS 2018 - 2018 International Conference on Computer, Information and Telecommunication Systems, International Conference on Computer, Information and Telecommunication Systems, IEEE, Colmar, France.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. A fingerprinting of a physical device is useful in distinguishing a device from others. In security perspective device fingerprinting has many applications such as device authentication, network access management, withstanding attacks related to identity spoofing and tracing the malicious devices. In this paper, a technique of wireless devices fingerprinting has been presented. We have used the discrete wavelet transformation on the inter-arrival delay of network packets to extract a unique pattern embedded in their packet transmission. The performance evaluation of the model is done by using GTID dataset. The proposed method gives better accuracy and identifies more number of devices than the existing techniques.
Li, M, Puthal, D, Yang, C, Luo, Y, Zhang, J & Li, J 2018, 'Stock market analysis using social networks', Proceedings of the Australasian Computer Science Week Multiconference, Australasian Computer Science Week Multiconference, ACM, Brisbane, Queensland, Australia.View/Download from: UTS OPUS or Publisher's site
© 2018 ACM. Nowadays, the use of social media has reached unprecedented levels. Among all social media, with its popular micro-blogging service, Twitter enables users to share short messages in real time about events or express their own opinions. In this paper, we examine the effectiveness of various machine learning techniques on retrieved tweet corpus. A machine learning model is deployed to predict tweet sentiment, as well as gain an insight into the correlation between twitter sentiment and stock prices. Specifically, that correlation is acquired by mining tweets using Twitter's search API and process it for further analysis. To determine tweet sentiment, two types of machine learning techniques are adopted including Naïve Bayes classification and Support vector machines. By evaluating each model, we discover that support vector machine gives higher accuracy through cross validation. After predicting tweet sentiment, we mine historical stock data using Yahoo finance API, while the designed feature matrix for stock market prediction includes positive, negative, neutral and total sentiment score and stock price for each day. In order to capturing the correlation situation between tweet opinions and stock market prices, hence, evaluating the direct correlation between tweet sentiments and stock market prices, the same machine learning algorithm is implemented for conducting our empirical study.
Mishra, AK, Obaidat, MS, Puthal, D, Tripathy, AK & Choo, K-KR 2018, 'Graph-Based Symmetric Crypto-System for Data Confidentiality', 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), IEEE Global Communications Conference (GLOBECOM), IEEE, Abu Dhabi, U ARAB EMIRATES.View/Download from: UTS OPUS
Mishra, P, Tiwary, M, Yang, LT & Puthal, D 2018, 'S2R: Service trading based response time optimization in mobile edge computing', 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS, 16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, IEEE COMPUTER SOC, Melbourne, AUSTRALIA, pp. 684-691.View/Download from: UTS OPUS or Publisher's site
Mishra, S, Mishra, SK, Sahoo, B, Obaidat, MS & Puthal, D 2018, 'First score auction for pricing-based resource selection in vehicular cloud', CITS 2018 - 2018 International Conference on Computer, Information and Telecommunication Systems, International Conference on Computer, Information and Telecommunication Systems, IEEE, Colmar, France.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. Selecting vehicles to supply resources is a crucial research problem in the vehicular cloud and highly depends on the pricing of the resources. Subsequently, resource pricing is an intricate problem influenced by the market demand and quality of service provided. Widespread and autonomous vehicular network requires reputation as a medium for trusting the supplier vehicles. Taking into account the above factors, we design the utility of supplier and consumer vehicles. Subsequently, a 1st score auction mechanism is proposed and modeled for the consumer vehicles to obtain maximum utility. Additionally, the protocol enables the supplier vehicles to decide the optimal pricing of resources. The 1st auction protocol is then simulated and the experimental results indicate better performance of our protocol than other standard protocols.
Puthal, D, Ranjan, R, Nepal, S & Chen, J 2017, 'IoT and big data: An architecture with data flow and security issues', Cloud Infrastructures, Services, and IoT Systems for Smart Cities Second EAI International Conference, IISSC 2017 and CN4IoT 2017 Brindisi, Italy, April 20–21, 2017 Proceedings (LNICST 189), International Conference on ICT Infrastructures and Services for Smart Cities and International Conference on Cloud Networking for Internet of Things Systems, Springer, Italy, pp. 243-252.View/Download from: UTS OPUS or Publisher's site
© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. The Internet of Things (IoT) introduces a future vision where users, computer, computing devices and daily objects possessing sensing and actuating capabilities cooperate with unprecedented convenience and benefits. We are moving towards IoT trend, where the number of smart sensing devices deployed around the world is growing at a rapid speed. With considering the number of sources and types of data from smart sources, the sensed data tends to new trend of research i.e. big data. Security will be a fundamental enabling factor of most IoT applications and big data, mechanisms must also be designed to protect communications enabled by such technologies. This paper analyses existing protocols and mechanisms to secure the IoT and big data, as well as security threats in the domain. We have broadly divided the IoT architecture into several layers to define properties, security issues and related works to solve the security concerns.
Sahu, AK, Sharma, S, Puthal, D, Pandey, A & Shit, R 2017, 'Secure Authentication Protocol for IoT Architecture', Proceedings - 2017 International Conference on Information Technology, ICIT 2017, International Conference on Information Technology, IEEE, Bhubaneswar, India, pp. 220-224.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. In recent years, Internet of Things(IoT) gained popularity due to its enormous applications in many fields. IoT network comprises heterogeneous devices to a great scale, which creates numerous security threats. In this paper, a smart home based on IoT architecture is considered, where the IoT smart Hub(ISH) communicate with the cloud in one hand, and home appliances and smart devices on the other. The IoT smart Hub(ISH) receives commands from the smart phone which is connected to the cloud through the internet, where lies the possibility of an external attack. This paper proposes a secure authentication protocol between smart phone and ISH, which supports ISH for ensuring security in the smart home scenario.
Shit, RC, Sharma, S, Puthal, D & Panday, A 2017, 'Self Deployment Based on Circle Packing Algorithm for Movement Assisted Wireless Sensor Networks', Proceedings - 2017 International Conference on Information Technology, ICIT 2017, International Conference on Information Technology, IEEE, Bhubaneswar, India, pp. 240-245.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Mobile wireless sensor networks are gaining importance for diverse application in recent days. There are many design challenges arises with mobile wireless sensor network. The biggest challenge is to develop an efficient sensor deployment algorithm. These sensor networks relocate sensors to achieve a specific network performance goal. This work reviews the existing sensor node deployment algorithms for mobile nodes and their application area. The effect of sensor node deployment algorithms based on mobility of sensor nodes for obtaining the desired performance goal of the network is analyzed. A circle packing based movement assisted algorithms have been proposed and analyzed compared with the existing algorithms. Further open problems in this area is discussed.
Tiwary, M, Kumar, S, Agrawal, PK, Puthal, D, Rodrigues, JJPC, Sahoo, KS & Sahoo, B 2018, 'Introducing Network Multi-Tenancy for Cloud-Based Enterprise Resource Planning: An IoT Application', IEEE International Symposium on Industrial Electronics, International Symposium on Industrial Electronics, IEEE, Cairns, QLD, Australia, pp. 1263-1269.View/Download from: UTS OPUS or Publisher's site
© 2018 IEEE. The cloud service providers make a considerable investment in setting up the data centers backbone network with the aim to maximize the network resource. However, the actual utilization of the network resources is hard to predict. With the invent of Software Defined Networking (SDN) and OpenFlow protocol, the network control layer has got the capability to communicate with the applications or services which are offered by the service provider. Moreover, a Software Defined Data center suggests resource virtualization at computing, storage, and network layer. The multi-tenancy is a well-accepted architecture in cloud computing where a single instance of a software application serves multiple customers. This work is a first of its kind, which aims at maximizing the network resources with respect to multi-tenancy at the network layer. In this work, with network multitenancy, different customers IoT traffic flows are prioritized, and then network resources are allocated to the traffic flows dynamically based on the priority. We considered a scenario of Enterprise Resource Planning (ERP) solutions deployed in the cloud which offers services in the form of Software as a Service to the customers. The IoT devices deployed at the manufacturing site makes transactions on the cloud ERP. This work focuses on prioritizing the ERP- IoT traffic to meets the demands of a multi-tenant data center network. The ERP-IoT flows are prioritized using a regression based machine learning technique for predicting the response time for execution of a query caused by a traffic flow in the ERP backend server. Later, the ERP-IoT flows are assigned to multiple queues created on each network device in data center. This assignment is performed based on the traffic flow priority and Demand Supply scores, which aims at maximizing network resource utilization. During performance evaluation, we observed that the proposed work with network multi-tenancy shows more than 10% increase in ser...
Prasad, M, Zheng, DR, Mery, D, Puthal, D, Sundaram, S & Lin, CT 2018, 'A fast and self-adaptive on-line learning detection system', Procedia Computer Science, INNS Conference on Big Data and Deep Learning, Elsevier, Bali, Indonesia, pp. 13-22.View/Download from: UTS OPUS or Publisher's site
© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability.
Fan, X, He, X, Puthal, D, Chen, S, Xiang, C, Nanda, P & Rao, X 2018, 'CTOM: Collaborative Task Offloading Mechanism for Mobile Cloudlet Networks', International Conference on Communications, IEEE, Kansas City, MO, USA.View/Download from: UTS OPUS
Malik, N, Nanda, P, Arora, A, He, X & Puthal, D 2018, 'Blockchain Based Secured Identity Authentication and Expeditious Revocation Framework for vehicular Networks', IEEE Computer Society, IEEE International Conference On Trust, Security And Privacy In Computing And Communications, IEEE, New York.View/Download from: UTS OPUS or Publisher's site
Authentication and revocation of users in Vehicular Adhoc Networks (VANETS) are two vital security aspects. It is extremely important to perform these actions promptly and efficiently. The past works addressing these issues lack in mitigating the reliance on the centralized trusted authority and therefore do not provide distributed and decentralized security. This paper proposes a blockchain based authentication and revocation framework for vehicular networks, which not only reduces the computation and communication overhead by mitigating dependency on a trusted authority for identity verification, but also speedily updates the status of revocated vehicles in the shared blockchain ledger. In the proposed framework, vehicles obtain their Pseudo IDs from the Certificate Authority (CA), which are stored along with their certificate in the immutable authentication blockchain and the pointer corresponding to the entry in blockchain, enables the Road Side Units (RSUs) to verify the identity of a vehicle on road. The efficiency and performance of the framework has been validated using the Omnet++ simulation environment.
Nanda, A, Nanda, P, He, X, Puthal, D & Jamdagni, A 2018, 'A Novel Hybrid Authentication Model for Geo Location Oriented Routing in Dynamic Wireless Mesh Networks', Proceedings of the 51st Hawaii International Conference on System Sciences 2018, International Conference on System Sciences, Hawaii, USA, pp. 5532-5541.View/Download from: UTS OPUS
Authentication is an essential part of any network and plays a pivotal role in ensuring the security of a network by preventing unauthorised devices/users access to the network. As dynamic wireless mesh networks are evolving and being accepted in various fields, there is a strong need to improve the security of the network. It’s features like self-organizing and self-healing make it great but get undermined when rigid authentication schemes are used. We propose a hybrid authentication scheme for such dynamic mesh networks under three specified scenarios; full authentication, quick authentication and new node authentication. The proposed schemes are applied on our previous works on dynamic mesh routing protocol, Geo location Oriented Routing Protocol (GLOR Simulation results show our proposed scheme is efficient in terms of resource utilization as well as defending against security threats.
Liu, M, Zhang, X, Yang, C, Pang, S, Puthal, D & Ren, K 2017, 'Privacy-preserving detection of statically mutually exclusive roles constraints violation in interoperable role-based access control', Proceedings - 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and Engineering and 14th IEEE International Conference on Embedded Software and Systems, Trustcom/BigDataSE/ICESS 2017, 2017 IEEE Trustcom/BigDataSE/ICESS, IEEE, Sydney, Australia, pp. 502-509.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.
Mishra, SK, Khan, MA, Sahoo, B, Puthal, D, Obaidat, MS & Hsiao, KF 2017, 'Time efficient dynamic threshold-based load balancing technique for Cloud Computing', IEEE CITS 2017 - 2017 International Conference on Computer, Information and Telecommunication Systems, International Conference on Computer, Information and Telecommunication Systems, IEEE, Dalian, China, pp. 161-165.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. Cloud computing is a novel technology leads several new challenges to all organizations worldwide. Cloud computing supports virtual machines (VMs) to host multiple applications simultaneously. Balancing the large numbers of applications in the heterogeneous cloud environment becomes challenging as the hypervisor scheduling controls all VMs. When the scheduler allocates tasks to the overloaded VMs, the performance of the cloud system degrades. In this paper, we present a novel load balancing approach to organizing the virtualized resources of the data center efficiently. In our approach, the load to a VM scales up and down according to the resource capacity of the VM. The proposed scheme minimizes the makespan of the system, maximizes resource utilization and reduces the overall energy consumption. We have evaluated our approach in CloudSim simulation environment, and our devised approach has reduced the waiting time compared to existing approaches and optimized the makespan of the cloud data center.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2017, 'A Synchronized Shared Key Generation Method for Maintaining End-to-End Security of Big Data Streams', Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, IEEE, Hawai, USA, pp. 6011-6020.View/Download from: UTS OPUS
A large number of mission critical applications ranging from disaster management to smart city are built on the Internet of Things (IoT) platform by deploying a number of smart sensors in a heterogeneous environment. The key requirements of such applications are the need of near real-time stream data processing in large scale sensing networks. This trend gives birth of an area called big data stream. One of the key problems in big data stream is to ensure the end-to-end security. To address this challenge, we proposed Dynamic Prime Number Based Security Verification (DPBSV) and Dynamic Key Length Based Security Framework (DLSeF) methods for big data streams based on the shared key derived from synchronized prime numbers in our earlier works. One of the major shortcomings of these methods is that they assume synchronization of the shared key. However, the assumption does not hold when the communication between Data Stream Manager (DSM) and sensing devices is broken. To address this problem, this paper proposes an adaptive technique to synchronize the shared key without communication between sensing devices and DSM, where sensing devices obtain the shared key re-initialization properties from its neighbours. Theoretical analyses and experimental results show that the proposed technique can be integrated with our DPBSV and DLSeF methods without degrading the performance and efficiency. We observed that the proposed synchronization method also strengthens the security of the models.
Rasheed, A, Kenneth, A, Mahapatra, R & Puthal, D 2017, 'Private matching and set intersection computation in multi-agent and industrial control systems', ACM International Conference Proceeding Series, Annual Conference on Cyber and Information Security Research, ACM, Oak Ridge, Tennessee, USA.View/Download from: UTS OPUS or Publisher's site
© 2017 ACM. Distributed autonomous systems that rely on dataset matching and set intersection computation for decision making capabilities are vulnerable to datasets poisoning attacks. Among these systems, Industrial Control Systems (ICS) operating on critical infrastructures. Attacker with a compromised Programmable Logic Controllers (PLCs) can take advantage of the PLC-to-PLC information sharing process to construct and inject anomalous data that target the result of dataset matching and set intersection computation and hence bring the process operations into unstable state. We introduce a protocol that utilizes secure hamming distance computation from oblivious transfer to compute a joint set between two system's agents that hold private input datasets of length n. The proposed protocol achieves full security in the semi-honest model.
Sahoo, KS, Sarkar, A, Mishra, SK, Sahoo, B, Puthal, D, Obaidat, MS & Sadun, B 2017, 'Metaheuristic solutions for solving controller placement problem in SDN-based WAN architecture', ICETE 2017 - Proceedings of the 14th International Joint Conference on e-Business and Telecommunications, International Joint Conference on e-Business and Telecommunications, Madrid, Spain, pp. 15-23.View/Download from: UTS OPUS or Publisher's site
© Copyright 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Software Defined Networks (SDN) is a popular paradigm in the modern networking systems that decouples the control logic from the underlying hardware devices. The control logic has implemented as a software component and residing in a server called controller. To increase the performance, deploying multiple controllers in a large-scale network is one of the key challenges of SDN. To solve this, authors have considered controller placement problem (CPP) as a multi-objective combinatorial optimization problem and used different heuristics. Such heuristics can be executed within a specific time-frame for small and medium sized topology, but out of scope for large scale instances like Wide Area Network (WAN). In order to obtain better results, we propose Particle Swarm Optimization (PSO) and Firefly two population-based meta-heuristic algorithms for optimal placement of the controllers, which take a particular set of objective functions and return the best possible position out of them. The problem has been defined, taking into consideration both controllers to switch and inter-controller latency as the objective functions. The performance of the algorithms evaluated on a set of publicly available network topologies in terms execution time. The results show that the FireFly algorithm performs better than PSO and random approach under various conditions.
Sahoo, S, Mishra, SK, Sahoo, B, Puthal, D & Obaidat, MS 2017, 'Deadline-constraint services in cloud with heterogeneous servers', Proceedings of the IEEE CITS 2017 - 2017 International Conference on Computer, Information and Telecommunication Systems, International Conference on Computer, Information and Telecommunication Systems, IEEE, Piscataway, USA, pp. 20-24.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. The development of delay sensitive applications needs massive data storage and computing resources, especially in a typical cloud environment. The cloud computing paradigm provides a broad range of services viz. software, platform, and infrastructure for various applications (both real-time and non real-time) over the Internet. But, in the case of Infrastructure-as-a-Service (IaaS) cloud platform, either over provisioning or under-provisioning of resources becomes a challenging issue for time constraint applications. An accurate modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests. We present an analytical model to estimate the performance of the cloud center for deadline sensitive tasks. We used the model to find the number of task miss deadline, waiting time of a task, and response time of the service, among others.
Chou, KP, Prasad, M, Puthal, D, Chen, PH, Vishwakarma, DK, Sundarami, S, Lin, CT & Lin, WC 2017, 'Fast Deformable Model for Pedestrian Detection with Haar-like features', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, IEEE Symposium Series on Computational Intelligence, IEEE, Honolulu, HI, USA, pp. 1-8.View/Download from: UTS OPUS or Publisher's site
© 2017 IEEE. This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.
Cheng, EJ, Prasad, M, Puthal, D, Sharma, N, Prasad, OK, Chin, PH, Lin, CT & Blumenstein, M 2017, 'Deep Learning Based Face Recognition with Sparse Representation Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 665-674.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. Feature extraction is an essential step in solving real-world pattern recognition and classification problems. The accuracy of face recognition highly depends on the extracted features to represent a face. The traditional algorithms uses geometric techniques, comprising feature values including distance and angle between geometric points (eyes corners, mouth extremities, and nostrils). These features are sensitive to the elements such as illumination, variation of poses, various expressions, to mention a few. Recently, deep learning techniques have been very effective for feature extraction, and deep features have considerable tolerance for various conditions and unconstrained environment. This paper proposes a two layer deep convolutional neural network (CNN) for face feature extraction and applied sparse representation for face identification. The sparsity and selectivity of deep features can strengthen sparseness for the solution of sparse representation, which generally improves the recognition rate. The proposed method outperforms other feature extraction and classification methods in terms of recognition accuracy.
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2017, 'Secure-GLOR: An Adaptive Secure Routing Protocol for Dynamic Wireless Mesh Networks', 2017 IEEE Trustcom/BigDataSE/ICESS, 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, IEEE Computer Society, Sydney, Australia, pp. 269-276.View/Download from: UTS OPUS or Publisher's site
With the dawn of a new era, digital security has become one of the most essential part of any network. Be it a physical network, virtual network or social network, the demand for secure data transmission is ever increasing. Wireless mesh networks also stand the same test of security as the legacy networks. This paper presents a secure version of the Geo-Location Oriented Routing (GLOR) protocol for wireless mesh networks, incorporating a multilevel security framework. It implements authentication using the new features of the network model and enables encryption throughout the network to provide high levels of security
Nanda, P, Malik, N & Puthal, D 2017, 'An Overview of Security Challenges in Vehicular Ad-Hoc Networks', IEEE Explore, 16th International Conference on Information Technology (ICIT), Bhubaneswar, India.View/Download from: UTS OPUS
Vehicular Ad hoc Networks (VANET) is emerging as a promising technology of the Intelligent Transportation systems (ITS) due to its potential benefits for travel planning, notifying road hazards, cautioning of emergency scenarios, alleviating congestion, provisioning parking facilities and environmental predicaments. But, the security threats hinder its wide deployment and acceptability by users. In this paper, we give an overview of the security threats at the various layers of the VANET communication stack and discuss some of the existing solutions, thus concluding why designing a security framework for VANETS needs to consider these threats for overcoming security challenges in VANETS.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2016, 'A Secure Big Data Stream Analytics Framework for Disaster Management on the Cloud', Proceedings of the 18th IEEE International Conference on High Performance Computing and Communications (HPCC) / 14th IEEE International Conference on Smart City (Smart City) / 2nd IEEE International Conference on Data Science and Systems (DSS), IEEE International Conference on High Performance Computing and Communications (HPCC) / IEEE International Conference on Smart City (Smart City) / IEEE International Conference on Data Science and Systems (DSS), IEEE, Sydney, Australia, pp. 1218-1225.View/Download from: UTS OPUS or Publisher's site
Cloud computing and big data analysis are gaining lots of interest across a range of applications including disaster management. These two technologies together provide the capability of real-time data analysis not only to detect emergencies in disaster areas, but also to rescue the affected people. This paper presents a framework that supports emergency event detection and alert generation by analyzing the data stream, which includes efficient data collection, data aggregation and alert dissemination. One of the goals for such a framework is to support an end-to-end security architecture to protect the data stream from unauthorized manipulation as well as leakage of sensitive information. The proposed system provides support for both data security punctuation and query security punctuation. This paper presents the proposed architecture with a specific focus on data stream security. It also briefly describes the implementation of security aspects of the architecture.
Puthal, D, Nepal, S, Paris, C, Ranjan, R & Chen, J 2015, 'Efficient Algorithms for Social Network Coverage and Reach', Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, IEEE International Congress on Big Data, IEEE, New York, USA, pp. 467-474.View/Download from: Publisher's site
© 2015 IEEE. Social networks, though started as a software tool enabling people to connect with each other, have emerged in recent times as platforms for businesses, individuals and government agencies to conduct a number of activities ranging from marketing to emergency situation management. As a result, a large number of social network analytics tools have been developed for a variety of applications. A snapshot of social networks at any particular time, called a social graph, represents the connectivity of nodes and potentially the flow of information amongst the nodes (or vertices) in the graph. Understanding the flow of information in a social graph plays an important role in social network applications. Two specific problems related to information flow have implications in many social network applications: (a) finding a minimum set of nodes one has to know to recover the whole graph (also known as the vertex cover problem) and (b) determining the minimum set of nodes one required to reach all nodes in the graph within a specific number of hops (we refer this as the vertex reach problem). Finding an optimal solution to these problems is NP-Hard. In this paper, we propose approximation based approaches and show that our approaches outperform existing approaches using both a theoretical analysis and experimental results.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2015, 'A dynamic key length based approach for real-time security verification of big sensing data stream', Web Information Systems Engineering (LNCS), International Conference on Web Information Systems Engineering, Springer, Miami, USA, pp. 93-108.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2015 The near real-time processing of continuous data flows in large scale sensor networks has many applications in risk-critical domains ranging from emergency management to industrial control systems. The problem is how to ensure end-to-end security (e.g., integrity, and authenticity) of such data stream for risk-critical applications. We refer this as an online security verification problem. Existing security techniques cannot deal with this problem because they were not designed to deal with high volume, high velocity data in real-time. Furthermore, they are inefficient as they introduce a significant buffering delay during security verification, resulting in a requirement of large buffer size for the stream processing server. To address this problem, we propose a Dynamic Key Length Based Security Framework (DLSeF) based on the shared key derived from synchronized prime numbers; the key is dynamically updated in short intervals to thwart Man in the Middle and other Network attacks. Theoretical analyses and experimental results of DLSeF framework show that it can significantly improve the efficiency of processing stream data by reducing the security verification time without compromising the security.
Puthal, D, Nepal, S, Ranjan, R & Chen, J 2015, 'DPBSV - An efficient and secure scheme for big sensing data stream', 2015 IEEE Trustcom/BigDataSE/ISPA, IEEE/IFIP International Symposium on Trusted Computing and Communications, IEEE, Helsinki, Finland, pp. 246-253.View/Download from: UTS OPUS or Publisher's site
Stream processing has become an important paradigm for the massive real-time processing of continuous data flows in large scale sensor networks. While dealing with big data streams in sensor networks, Stream Processing Engines (SPEs) must always verify the authenticity, and integrity of the data as the medium of communication is untrusted, as malicious attackers could access and modify the data. Existing technologies for data security verification are not suitable for data streaming applications, as the verification in real time introduces significant overheads. In this paper, we propose a Dynamic Prime Number Based Security Verification (DPBSV) scheme for big data stream processing. Our scheme is based on a common shared key that is updated dynamically by generating synchronized pairs of prime numbers. Theoretical analyses and experimental results of our DPBSV scheme show that it can significantly improve the efficiency as compared to existing approaches by reducing the security verification overhead. Our approach not only reduces the verification time, but also strengthens the security of the data by constantly updating the shared keys.
Puthal, D, Sahoo, BPS, Mishra, S & Swain, S 2015, 'Cloud Computing Features, Issues and Challenges: A Big Picture', 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NETWORKS (CINE), International Conference on Computational Intelligence and Networks, IEEE, Bhubaneswar, INDIA, pp. 116-123.View/Download from: UTS OPUS or Publisher's site
Sahoo, BPS, Puthal, D, Swain, S & Mishra, S 2015, 'A comparative analysis of packet scheduling schemes for multimedia services in LTE networks', Proceedings - 1st International Conference on Computational Intelligence and Networks, CINE 2015, International Conference on Computational Intelligence and Networks, IEEE, Bhubaneswar, Odisha, India, pp. 110-115.View/Download from: UTS OPUS or Publisher's site
© 2015 IEEE. The revolution in high-speed broadband network is the requirement of the current time, in other words here is an unceasing demand for high data rate and mobility. Both provider and customer see, the long time evolution (LTE) could be the promising technology for providing broadband, mobile Internet access. To provide better quality of service (QoS) to customers, the resources must be utilized at its fullest impeccable way. Resource scheduling is one of the important functions for remanufacturing or upgrading system performance. This paper studies the recently proposed packet scheduling schemes for LTE systems. The study has been concentrated in implication to real-time services such as online video streaming and Voice over Internet Protocol (VOIP). For performance study, the LTE-Sim simulator is used. The primary objective of this paper is to provide results that will help researchers to design more efficient scheduling schemes, aiming to get better overall system performance. For the simulation study, two scenarios, one for video traffic and other for VoIP have been created. Various performances metric such as packet loss, fairness, end-to-end (E2E) delay, cell throughput and spectral efficiency has been measured for both the scenarios varying numbers of users. In the light of the simulation result analysis, the frame level scheduler (FLS) algorithms outperform other, by balancing the QoS requirements for multimedia services.
Drira, W, Puthal, D & Filali, F 2014, 'ADCS: An adaptive data collection scheme in vehicular networks using 3G/LTE', 2014 International Conference on Connected Vehicles and Expo, ICCVE 2014 - Proceedings, International Conference on Connected Vehicles and Expo, IEEE, Vienna, Austria, pp. 753-758.View/Download from: UTS OPUS or Publisher's site
© 2014 IEEE. Vehicular Ad-hoc Networks (VANETs) are special kind of Mobile Ad-hoc Networks (MANETs). The distinctive characteristics of the VANETs include high speed of vehicular nodes and high variability in node density. Collecting data from VANETs is important to monitor, control and manage road traffic. However, efficient collection of the needed data is quite challenging due to vehicles mobility and the tremendous amount of events and data generated. In this paper we focus on vehicle data collection using 3G/LTE. In the first step, we compare proactive and reactive data collection schemes using simulation. The results show that proactive gives the lowest delay and bandwidth usage but the most loss ratio. Interested in optimizing the bandwidth usage, an adaptive data collection scheme will be provided. It is based on the proactive scheme while it uses variable periods depending on the vehicle position and travel time to provide accurate traffic and travel time information to the Traffic Management Center (TMC). Emulation results, using taxi traces in Qatar, shows that our algorithm consumes an acceptable amount of mega bytes (≈ 31MB) per month when the basic reporting period is set to 10s.
Puthal, D, Mir, ZH, Filali, F & Menouar, H 2013, 'Cross-layer architecture for congestion control in Vehicular Ad-hoc Networks', 2013 International Conference on Connected Vehicles and Expo, ICCVE 2013 - Proceedings, International Conference on Connected Vehicles and Expo (ICCVE), IEEE, Las Vegas, NV, USA, pp. 887-892.View/Download from: UTS OPUS or Publisher's site
Vehicular Ad-hoc Networks (VANETs) are special kind of Mobile Ad-hoc Networks (MANETs). The distinctive characteristics of the VANETs include high speed of vehicular nodes and high variability in node density. Congestion detection and control protocols have been proved to be an efficient method for improving network performance and are well studied for the MANET environment. However, they often result in sub-optimal network performance for the vehicular network environment due to the specialized characteristics of VANET. In this paper we present an adaptive and distributed cross-layer congestion detection and control protocol for the VANET environment. During the congestion detection phase, information from each layer of the network protocol stack is combined and mapped on to congestion levels. In the subsequent congestion control phase parameters like contention window, transmission rate and transmit power are jointly adjusted to improve on the network performance. The effectiveness of the proposed model is evaluated through mathematical analysis and simulation-based studies. © 2013 IEEE.