Dr Hoang Dinh is a faculty member with the School of Electrical and Data Engineering, the University of Technology Sydney, Australia. He received his Ph.D. in Computer Science and Engineering from the Nanyang Technological University, Singapore, in 2016. His research interests include emerging topics in wireless communications and networking such as ambient backscatter communications, cognitive radios, wireless energy harvesting, IoT, mobile edge and 5G networks. He has been serving as an active member of technical program committees and a reviewer for many IEEE Transactions, Journals, Magazines, and Conferences. He received the best reviewer award from IEEE Transactions of Wireless Communications and he is currently an editor of IEEE Wireless Communications Letters and IEEE Transactions on Cognitive Communications and Networking.
- Editor, IEEE Transactions on Cognitive Communications and Networking (TCCN), (Jan 2019 - current).
- Editor, IEEE Wireless Communication Letters (2018 - )
- Track Co-Chair, IEEE Vehicular Technology Conference (VTC) 2018-Fall "Wireless Networks: Protocols, Security, and Services"
- Session Chair, IEEE GLOBECOM 2016
Can supervise: YES
- Blockchain technology, machine learning, and cybersecurity
- Cognitive radio, wireless energy harvesting, Internet-of-Things, and mobile cloud, and 5G networks
- Performance optimization, network security, efficient energy management, resource allocation, risk management and wireless security
- Operations research, engineering mathematics, control theory, game theory, and queuing theory
- Markov decision process, convex optimization, learning algorithms, and performance analysis
- Wireless Communications and Networking
- IoT Security. Mobile Security
Alsheikh, MA, Hoang, DT, Niyato, D, Leong, D, Wang, P & Han, Z 2020, 'Optimal Pricing of Internet of Things: A Machine Learning Approach', IEEE Journal on Selected Areas in Communications, vol. 38, no. 4, pp. 669-684.View/Download from: Publisher's site
© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.
Hoang, DT, Nguyen, DN, Alsheikh, MA, Gong, S, Dutkiewicz, E, Niyato, D & Han, Z 2020, '"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks', IEEE Wireless Communications Magazine, vol. 27, no. 3, pp. 79-87.View/Download from: Publisher's site
In this article, we introduce a novel deception strategy inspired by the "Borrowing Arrows with Thatched Boats" strategy, one of the most famous military tactics in history, in order to defeat reactive jamming attacks for low-power IoT networks. Our proposed strategy allows resource-constrained IoT devices to be able to defeat powerful reactive jammers by leveraging their own jamming signals. More specifically, by stimulating the jammer to attack the channel through transmitting fake transmissions, the IoT system can not only undermine the jammer's power, but also harvest energy or utilize jamming signals as a communication means to transmit data through using RF energy harvesting and ambient backscatter techniques, respectively. Furthermore, we develop a low-cost deep reinforcement learning framework that enables the hardware-constrained IoT device to quickly obtain an optimal defense policy without requiring any information about the jammer in advance. Simulation results reveal that our proposed framework can not only be very effective in defeating reactive jamming attacks, but also leverage a jammer's power to enhance system performance for the IoT network.
Lim, WYB, Luong, NC, Hoang, DT, Jiao, Y, Liang, YC, Yang, Q, Niyato, D & Miao, C 2020, 'Federated Learning in Mobile Edge Networks: A Comprehensive Survey', IEEE Communications Surveys and Tutorials.View/Download from: Publisher's site
IEEE In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
Lyu, B, Hoang, DT & Yang, Z 2020, 'Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks', IEEE Wireless Communications Letters.View/Download from: Publisher's site
IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.
Saputra, Y, Dinh, H, Nguyen, D & Dutkiewicz, E 2020, 'A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation', IEEE Transactions on Mobile Computing.View/Download from: Publisher's site
Mobile edge caching/computing has been emerging as a promising paradigm to provide new services (e.g., ultra-high rate, ultra-reliable, and/or low-latency communications) in future wireless networks. In this paper, we introduce a novel mobile edge caching network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs), namely JOCAD. Under this architecture, MENs cooperate with each other in both caching and delivering contents, aiming to simultaneously minimize the total average delay for the mobile users and mitigate the network traffic on the backhaul link. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40% compared with the most frequency-of-access policy, and up to 25% compared with locally optimal caching policy (i.e., without collaboration). Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network.
Gong, S, Gao, L, Xu, J, Guo, Y, Hoang, DT & Niyato, D 2019, 'Exploiting Backscatter-Aided Relay Communications with Hybrid Access Model in Device-to-Device Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 835-848.View/Download from: Publisher's site
© 2015 IEEE. The backscatter and active RF radios can complement each other and bring potential performance gain. In this paper, we envision a dual-mode radio structure that allows each device to make smart decisions on mode switch between backscatter communications (i.e., the passive mode) or RF communications (i.e., the active mode), according to the channel and energy conditions. The flexibility in mode switching also makes it more complicated for transmission control and network optimization. To exploit the radio diversity gain, we consider a wireless powered device-to-device network of hybrid radios and propose a sum throughput maximization by jointly optimizing energy beamforming and transmission scheduling in two radio modes. We further exploit the user cooperation gain by allowing the passive radios to relay for the active radios. As such, the sum throughput maximization is reformulated into a non-convex. We first present a sub-optimal algorithm based on successive convex approximation, which optimizes the relays' reflection coefficients by iteratively solving semi-definite programs. We also devise a set of heuristic algorithms with reduced computational complexity, which are shown to significantly improve the sum throughput and amenable for practical implementation.
Gong, S, Hoang, DT, Niyato, D, El Shafie, A, De Domenico, A, Strinati, EC & Hoydis, J 2019, 'Introduction to the special section on deep reinforcement learning for future wireless communication networks', IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 1019-1023.View/Download from: Publisher's site
Luong, NC, Hoang, DT, Gong, S, Niyato, D, Wang, P, Liang, YC, Kim, DI & Dinh, H 2019, 'Applications of Deep Reinforcement Learning in Communications and Networking: A Survey', IEEE Communications Surveys and Tutorials, vol. 21, no. 4, pp. 3133-3174.View/Download from: Publisher's site
© 1998-2012 IEEE. This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.
Lyu, B, Yang, Z & Dinh, H 2019, 'User Cooperation in Wireless-Powered Backscatter Communication Networks', IEEE Wireless Communications Letters, vol. 8, no. 2, pp. 632-635.View/Download from: Publisher's site
© 2012 IEEE. In this letter, we introduce new user-cooperation schemes for wireless devices in a wireless-powered backscatter communication network with the aim to improve communication and energy efficiency for the whole network. In particular, we consider two types of wireless devices which can support different communication modes, i.e., backscatter and harvest-then-transmit, and they can cooperate to deliver the information to the access point. To improve energy transmission efficiency for the devices, energy beamforming is deployed at the power beacon. We then formulate the weighted sum-rate maximization problem by jointly optimizing time schedule, power allocation, and energy beamforming. Due to the non-convex issue of the optimization problem, we employ the variable substitutions and semidefinite relaxation techniques to obtain the optimal solution. Simulation results show that the proposed cooperation framework can significantly improve the communication efficiency compared with non-cooperation approach.
Nguyen, CT, Dinh, H, Nguyen, DN, Niyato, D, Nguyen, HT & Dutkiewicz, E 2019, 'Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities', IEEE Access, vol. 7, pp. 85727-85745.View/Download from: Publisher's site
© 2013 IEEE. The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed.
Nguyen, H, Nguyen, D, Dinh, H & Dutkiewicz, E 2019, 'Jam Me If You Can: Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 11, pp. 2603-2620.View/Download from: Publisher's site
Nguyen, HV, Dinh, H, Nguyen, DN & Dutkiewicz, E 2019, 'Optimal and Fast Real-Time Resource Slicing with Deep Dueling Neural Networks', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 37, no. 6, pp. 1455-1470.View/Download from: Publisher's site
© 1983-2012 IEEE. Effective network slicing requires an infrastructure/network provider to deal with the uncertain demands and real-time dynamics of the network resource requests. Another challenge is the combinatorial optimization of numerous resources, e.g., radio, computing, and storage. This paper develops an optimal and fast real-time resource slicing framework that maximizes the long-term return of the network provider while taking into account the uncertainty of resource demands from tenants. Specifically, we first propose a novel system model that enables the network provider to effectively slice various types of resources to different classes of users under separate virtual slices. We then capture the real-time arrival of slice requests by a semi-Markov decision process. To obtain the optimal resource allocation policy under the dynamics of slicing requests, e.g., uncertain service time and resource demands, a Q-learning algorithm is often adopted in the literature. However, such an algorithm is notorious for its slow convergence, especially for problems with large state/action spaces. This makes Q-learning practically inapplicable to our case, in which multiple resources are simultaneously optimized. To tackle it, we propose a novel network slicing approach with an advanced deep learning architecture, called deep dueling, that attains the optimal average reward much faster than the conventional Q-learning algorithm. This property is especially desirable to cope with the real-time resource requests and the dynamic demands of the users. Extensive simulations show that the proposed framework yields up to 40% higher long-term average return while being few thousand times faster, compared with the state-of-the-art network slicing approaches.
Nguyen, HV, Dinh, H, Nguyen, DN, Dutkiewicz, E, Niyato, D & Wang, P 2019, 'Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning', IEEE Transactions on Communications, vol. 67, no. 8, pp. 5736-5752.View/Download from: Publisher's site
Saputra, YM, Hoang, DT, Nguyen, DN, Dutkiewicz, E, Niyato, D & Kim, DI 2019, 'Distributed Deep Learning at the Edge: A Novel Proactive and Cooperative Caching Framework for Mobile Edge Networks', IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1220-1223.View/Download from: Publisher's site
We propose two novel proactive cooperative caching approaches using deep learning (DL) to predict users' content demand in a mobile edge caching network. In the first approach, a content server (CS) takes responsibilities to collect information from all mobile edge nodes (MENs) in the network and then performs the proposed DL algorithm to predict the content demand for the whole network. However, such a centralized approach may disclose the private information because MENs have to share their local users' data with the CS. Thus, in the second approach, we propose a novel distributed deep learning (DDL)-based framework. The DDL allows MENs in the network to collaborate and exchange information to reduce the error of content demand prediction without revealing the private information of mobile users. Through simulation results, we show that our proposed approaches can enhance the accuracy by reducing the root mean squared error (RMSE) up to 33.7% and reduce the service delay by 47.4% compared with other machine learning algorithms.
Wang, W, Hoang, DT, Hu, P, Xiong, Z, Niyato, D, Wang, P, Wen, Y & Kim, DI 2019, 'A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks', IEEE Access, vol. 7, pp. 22328-22370.View/Download from: Publisher's site
© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.
Dinh, H, Niyato, D, Nguyen, D, Dutkiewicz, E, Wang, P & Zhu, H 2018, 'A Dynamic Edge Caching Framework for Mobile 5G Networks', IEEE Wireless Communications, vol. 25, no. 5, pp. 95-103.View/Download from: Publisher's site
Mobile edge caching has emerged as a new paradigm to provide computing, networking resources, and storage for a variety of mobile applications. That helps achieve low latency, high reliability, and improve efficiency in handling a very large number of smart devices and emerging services (e.g., IoT, industry automation, virtual reality) in mobile 5G networks. Nonetheless, the development of mobile edge caching is challenged by the decentralized nature of edge nodes, their small coverage, limited computing, and storage resources. In this article, we first give an overview of mobile edge caching in 5G networks. After that, its key challenges and current approaches are discussed. We then propose a novel caching framework. Our framework allows an edge node to authorize the legitimate users and dynamically predicts and updates their content demands using the matrix factorization technique. Based on the prediction, the edge node can adopt advanced optimization methods to determine optimal content to store so as to maximize its revenue and minimize the average delay of its mobile users. Through numerical results, we demonstrate that our proposed framework provides not only an effective caching approach, but also an efficient economic solution for the mobile service provider.
Van Huynh, N, Hoang, DT, Lu, X, Niyato, D, Wang, P & Kim, DI 2018, 'Ambient Backscatter Communications: A Contemporary Survey', Communications Surveys and Tutorials, IEEE Communications Society, vol. 20, no. 4, pp. 2889-2922.View/Download from: Publisher's site
IEEE Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.
Van Huynh, N, Hoang, DT, Niyato, D, Wang, P & Kim, DI 2018, 'Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks', IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 820-823.View/Download from: Publisher's site
© 2012 IEEE. This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF signals, the sensors are able to backscatter the RF signals to transmit data to the gateway and/or harvest energy from the RF signals for their operations. By integrating backscattering and energy harvesting techniques, we can optimize the network throughput of the system. In particular, we first formulate the time scheduling problem for the system, and then propose an optimal solution using convex optimization to maximize the overall network throughput. Numerical results show a significant throughput gain achieved by our proposed design over two other baseline schemes.
Wang, W, Hoang, DT, Niyato, D, Wang, P & Kim, DI 2018, 'Stackelberg game for distributed time scheduling in RF-powered backscatter cognitive radio networks', IEEE Transactions on Wireless Communications, vol. 17, no. 8, pp. 5606-5622.View/Download from: Publisher's site
© 2002-2012 IEEE. In this paper, we study the transmission strategy adaptation problem in an RF-powered cognitive radio network, in which hybrid secondary users are able to switch between the harvest-then-transmit mode and the ambient backscatter mode for their communication with the secondary gateway. In the network, a monetary incentive is introduced for managing the interference caused by the secondary transmission with imperfect channel sensing. The sensing-pricing-transmitting process of the secondary gateway and the transmitters is modeled as a single-leader-multi-follower Stackelberg game. Furthermore, the follower sub-game among the secondary transmitters is modeled as a generalized Nash equilibrium problem with shared constraints. Based on our theoretical discoveries regarding the properties of equilibria in the follower sub-game and the Stackelberg game, we propose a distributed, iterative strategy searching scheme that guarantees the convergence to the Stackelberg equilibrium. The numerical simulations show that the proposed hybrid transmission scheme always outperforms the schemes with fixed transmission modes. Furthermore, the simulations reveal that the adopted hybrid scheme is able to achieve a higher throughput than the sum of the throughput obtained from the schemes with fixed transmission modes.
Hoang, DT, Niyato, D, Wang, P, Kim, DI & Han, Z 2017, 'Ambient Backscatter: A New Approach to Improve Network Performance for RF-Powered Cognitive Radio Networks', IEEE Transactions on Communications, vol. 65, no. 9, pp. 3659-3674.View/Download from: Publisher's site
© 1972-2012 IEEE. This paper introduces a new solution to improve the performance for secondary systems in radio frequency (RF) powered cognitive radio networks (CRNs). In a conventional RF-powered CRN, the secondary system works based on the harvest-then-transmit protocol. That is, the secondary transmitter (ST) harvests energy from primary signals and then uses the harvested energy to transmit data to its secondary receiver (SR). However, with this protocol, the performance of the secondary system is much dependent on the amount of harvested energy as well as the primary channel activity, e.g., idle and busy periods. Recently, ambient backscatter communication has been introduced, which enables the ST to transmit data to the SR by backscattering ambient signals. Therefore, it is potential to be adopted in the RF-powered CRN. We investigate the performance of RF-powered CRNs with ambient backscatter communication over two scenarios, i.e., overlay and underlay CRNs. For each scenario, we formulate and solve the optimization problem to maximize the overall transmission rate of the secondary system. Numerical results show that by incorporating such two techniques, the performance of the secondary system can be improved significantly compared with the case when the ST performs either harvest-then-transmit or ambient backscatter technique.
Hoang, DT, Niyato, D, Wang, P, Kim, DI & Le, LB 2017, 'Optimal Data Scheduling and Admission Control for Backscatter Sensor Networks', IEEE Transactions on Communications, vol. 65, no. 5, pp. 2062-2077.View/Download from: Publisher's site
© 2017 IEEE. This paper studies the data scheduling and admission control problem for a backscatter sensor network (BSN). In the network, instead of initiating their own transmissions, the sensors can send their data to the gateway just by switching their antenna impedance and reflecting the received RF signals. As such, we can reduce remarkably the complexity, the power consumption, and the implementation cost of sensor nodes. Different sensors may have different functions, and data collected from each sensor may also have a different status, e.g., urgent or normal, and thus we need to take these factors into account. Therefore, in this paper, we first introduce a system model together with a mechanism in order to address the data collection and scheduling problem in the BSN. We then propose an optimization solution using the Markov decision process framework and a reinforcement learning algorithm based on the linear function approximation method, with the aim of finding the optimal data collection policy for the gateway. Through simulation results, we not only show the efficiency of the proposed solution compared with other baseline policies, but also present the analysis for data admission control policy under different classes of sensors as well as different types of data.
Hoang, DT, Wang, P, Niyato, D & Hossain, E 2017, 'Charging and discharging of plug-in electric vehicles (PEVs) in vehicle-to-grid (V2G) systems: A cyber insurance-based model', IEEE Access, vol. 5, pp. 732-754.View/Download from: Publisher's site
© 2013 IEEE. In addition to being environment friendly, vehicle-to-grid (V2G) systems can help the plug-in electric vehicle (PEV) users in reducing their energy costs and can also help stabilizing energy demand in the power grid. In V2G systems, since the PEV users need to obtain system information (e.g., locations of charging/discharging stations, current load, and supply of the power grid) to achieve the best charging and discharging performance, data communication plays a crucial role. However, since the PEV users are highly mobile, information from V2G systems is not always available for many reasons, e.g., wireless link failures and cyber attacks. Therefore, in this paper, we introduce a novel concept using cyber insurance to 'transfer' cyber risks, e.g., unavailable information, of a PEV user to a third party, e.g., a cyber-insurance company. Under the insurance coverage, even without information about V2G systems, a PEV user is always guaranteed the best price for charging/discharging. In particular, we formulate the optimal energy cost problem for the PEV user by adopting a Markov decision process framework. We then propose a learning algorithm to help the PEV user make optimal decisions, e.g., to charge or discharge and to buy or not to buy insurance, in an online fashion. Through simulations, we show that cyber insurance is an efficient solution not only in dealing with cyber risks, but also in maximizing revenue for the PEV user.
Luong, NC, Hoang, DT, Wang, P, Niyato, D & Han, Z 2017, 'Applications of Economic and Pricing Models for Wireless Network Security: A Survey', Communications Surveys and Tutorials, IEEE Communications Society, vol. 19, no. 4, pp. 2735-2767.View/Download from: Publisher's site
© 1998-2012 IEEE. This paper provides a comprehensive literature review on applications of economic and pricing theory to security issues in wireless networks. Unlike wireline networks, the broadcast nature and the highly dynamic change of network environments pose a number of nontrivial challenges to security design in wireless networks. While the security issues have not been completely solved by traditional or system-based solutions, economic and pricing models recently were employed as one efficient solution to discourage attackers and prevent attacks to be performed. In this paper, we review economic and pricing approaches proposed to address major security issues in wireless networks including eavesdropping attack, denial-of-service (DoS) attack such as jamming and distributed DoS, and illegitimate behaviors of malicious users. Additionally, we discuss integrating economic and pricing models with cryptography methods to reduce information privacy leakage as well as to guarantee the confidentiality and integrity of information in wireless networks. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to wireless security issues.
Niyato, D, Hoang, DT, Wang, P & Han, Z 2017, 'Cyber Insurance for Plug-In Electric Vehicle Charging in Vehicle-To-Grid Systems', IEEE Network, vol. 31, no. 2, pp. 38-46.View/Download from: Publisher's site
© 1986-2012 IEEE. V2G systems bring many benefits to power systems in stabilizing energy demand and supply fluctuations as well as to PEV users in reducing energy costs. To achieve the maximum efficiency of V2G systems, data communication plays an important role. However, it is subject to cyber attack and failure, which hinder the effectiveness of V2G systems. In this article, we introduce a novel concept of using cyber insurance to transfer cyber risk from a user to a third party in PEV charging. We first introduce V2G systems and briefly discuss the cyber risks. Additionally, the basic concepts of cyber insurance are presented. We then introduce the use of cyber insurance to remove the risk of paying high energy costs of PEV charging due to the unavailability of data communication. We show that the PEV user can achieve the maximum benefit in deciding to charge its PEV and to buy insurance.
Nguyen, CL, Hoang, DT, Wang, P, Niyato, D, Kim, DI & Han, Z 2016, 'Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey', IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, vol. 18, no. 4, pp. 2546-2590.View/Download from: Publisher's site
Niyato, D, Dinh, TH, Nguyen, CL, Wang, P, Kim, DI & Han, Z 2016, 'Smart Data Pricing Models for the Internet of Things: A Bundling Strategy Approach', IEEE NETWORK, vol. 30, no. 2, pp. 18-25.View/Download from: Publisher's site
Abu Alsheikh, M, Dinh, TH, Niyato, D, Tan, H-P & Lin, S 2015, 'Markov Decision Processes With Applications in Wireless Sensor Networks: A Survey', IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, vol. 17, no. 3, pp. 1239-1267.View/Download from: Publisher's site
Dinh, TH, Niyato, D, Wang, P & Kim, DI 2015, 'Performance Optimization for Cooperative Multiuser Cognitive Radio Networks with RF Energy Harvesting Capability', IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 14, no. 7, pp. 3614-3629.View/Download from: Publisher's site
Hoang, DT, Lu, X, Niyato, D, Wang, P, Kim, DI & Han, Z 2015, 'Applications of Repeated Games in Wireless Networks: A Survey', IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, vol. 17, no. 4, pp. 2102-2135.View/Download from: Publisher's site
Hoang, DT, Niyato, D, Wang, P & Kim, DI 2015, 'Performance Analysis of Wireless Energy Harvesting Cognitive Radio Networks under Smart Jamming Attacks', IEEE Transactions on Cognitive Communications and Networking, vol. 1, no. 2, pp. 200-216.View/Download from: Publisher's site
© 2015 IEEE. In cognitive radio networks with wireless energy harvesting, secondary users are able to harvest energy from a wireless power source and then use the harvested energy to transmit data opportunistically on an idle channel allocated to primary users. Such networks have become more common due to pervasiveness of wireless charging, improving the performance of the secondary users. However, in such networks, the secondary users can be vulnerable to jamming attacks by malicious users who can also harvest wireless energy to launch the attacks. In this paper, we first formulate the throughput optimization problem for a secondary user under the attacks by jammers as a Markov decision process (MDP). We then introduce a new solution based on the deception tactic to deal with smart jamming attacks. Furthermore, we propose a learning algorithm for the secondary user to find an optimal transmission policy and extend to the case with multiple secondary users in the same environment. Through the simulations, we demonstrate that the proposed learning algorithms can effectively reduce adverse effects from smart jammers even when they use different attack strategies.
Dinh, TH, Niyato, D, Wang, P & Kim, DI 2014, 'Opportunistic Channel Access and RF Energy Harvesting in Cognitive Radio Networks', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 32, no. 11, pp. 2039-2052.View/Download from: Publisher's site
Dinh, HT, Lee, C, Niyato, D & Wang, P 2013, 'A survey of mobile cloud computing: architecture, applications, and approaches', WIRELESS COMMUNICATIONS & MOBILE COMPUTING, vol. 13, no. 18, pp. 1587-1611.View/Download from: Publisher's site
Long, BL, Niyato, D, Hossain, E, Kim, DI & Dinh, TH 2013, 'QoS-Aware and Energy-Efficient Resource Management in OFDMA Femtocells', IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 12, no. 1, pp. 180-194.View/Download from: Publisher's site
Nguyen, CT, Saputra, YM, Huynh, NV, Nguyen, N-T, Khoa, TV, Tuan, BM, Nguyen, DN, Hoang, DT, Vu, TX, Dutkiewicz, E, Chatzinotas, S & Ottersten, B, 'Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey'.
Social distancing is crucial for preventing the spread of viral diseases
illnesses such as COVID-19. By minimizing the closely physical contact between
people, we can reduce chances of catching the virus and spreading it to the
community. This paper aims to provide a comprehensive survey on how emerging
technologies, e.g., wireless and networking, AI can enable, encourage, and even
enforce social distancing. To that end, we provide a fundamental background of
social distancing including basic concepts, measurements, models and propose
practical scenarios. We then discuss enabling wireless technologies which are
especially effective and can be widely adopted in practice to keep distance and
monitor people. After that, emerging technologies such as machine learning,
computer vision, thermal, ultrasound, etc., are introduced. These technologies
open many new solutions and directions to deal with problems in social
distancing, e.g., symptom prediction, detection and monitoring quarantined
people, and contact tracing. Finally, we provide important open issues and
challenges (e.g., privacy-preserving, cybersecurity) in implementing social
distancing in practice.
Saputra, Y, Dinh, H, Nguyen, D, Dutkiewicz, E & Niyato, D 2020, 'Wireless Edge Caching for Mobile Social Networks' in Vu, T, Chatzinotas, S, Baştuğ, E & Quek, TQS (eds), Wireless Edge CachingModeling, Analysis, and Optimization, Cambridge University Press.
Dinh, H & Dusit, N 2017, 'RF-Based Energy Harvesting Cognitive Cellular Networks' in Wei, Z (ed), Handbook of Cognitive Radio, Springer.
Hoang, DT & Niyato, D 2017, 'RF-Based Energy Harvesting Cognitive Cellular Networks' in Handbook of Cognitive Radio, Springer Singapore, pp. 1-43.View/Download from: Publisher's site
Dinh, H 2016, 'Backscattering Wireless-Powered Communications' in Dusit, N, Ekram, H, Dong, K, Vijay, B & Lotfollah, S (eds), Wireless-Powered Communication Networks: Architectures, Protocols, and Applications, Cambridge University Press.
Dinh, H 2016, 'Cognitive Radio Networks with Wireless Energy Harvesting' in Dusit, N, Ekram, H, Dong, K, Vijay, B & Lotfollah, S (eds), Wireless-Powered Communication Networks: Architectures, Protocols, and Applications, Cambridge University Press.
Nguyen, C, Nguyen, D, Dinh, H, Pham, HA, Nguyen, H & Dutkiewicz, E 2020, 'Blockchain and stackelberg game model for roaming fraud prevention and profit maximization', IEEE Wireless Communications and Networking Conference (WCNC), Seoul.
Nguyen, H, Nguyen, D, Dinh, H, Dutkiewicz, E, Mueck, M & Srikanteswara, S 2020, 'Defeating Jamming Attacks with Ambient Backscatter Communications', IEEE International Conference on Computing, Networking and Communications (ICNC), Hawaii.
Saputra, Y, Dinh, H, Nguyen, D & Dutkiewicz, E 2019, 'JOCAR: A Jointly Optimal Caching and Routing Framework for Cooperative Edge Caching Networks', 2019 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, Hawaii.View/Download from: Publisher's site
We propose a jointly optimal caching and routing framework (JOCAR) for a cooperative mobile edge caching network. This novel network architecture enables mobile edge servers/nodes (MENs) to collaborate in not only caching but also routing contents to users, in order to simultaneously minimize the total content-access delay for all mobile users and reduce the traffic on the backhaul network. To that end, we first formulate an access- delay minimization problem by jointly optimizing the content caching and routing decisions while accounting for various network configurations. Solving this problem requires us to deal with a nested dual optimization due to the strong mutual dependence between content caching and routing decisions. To tackle it, we first transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) problem. Then, we design a branch-and-bound based algorithm with the interior-point method to find the near-optimal policy for the MINLP problem. Extensive simulations show that JOCAR can reduce the total average delay and increase the cache hit rate for the whole network by more than 40% and by four times, respectively, compared with other conventional policies.
Saputra, Y, Dinh, H, Nguyen, D, Dutkiewicz, E, Mueck, M & Srikanteswara, S 2019, 'Energy Demand Prediction with Federated Learning for Electric Vehicle Networks', 2019 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, Hawaii.View/Download from: Publisher's site
In this paper, we propose novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicle (EV) networks. These methods can learn and find the correlation of complex hidden features to improve the prediction accuracy. First, we propose an energy demand learning (EDL)-based prediction solution in which a charging station provider (CSP) gathers information from all charging stations (CSs) and then performs the EDL algorithm to predict the energy demand for the considered area. However, this approach requires frequent data sharing between the CSs and the CSP, thereby driving communication overhead and privacy issues for the EVs and CSs. To address this problem, we propose a federated energy demand learning (FEDL) approach which allows the CSs sharing their information without revealing real datasets. Specifically, the CSs only need to send their trained models to the CSP for processing. In this case, we can significantly reduce the communication overhead and effectively protect data privacy for the EV users. To further improve the effectiveness of the FEDL, we then introduce a novel clustering- based EDL approach for EV networks by grouping the CSs into clusters before applying the EDL algorithms. Through experimental results, we show that our proposed approaches can improve the accuracy of energy demand prediction up to 24.63% and decrease communication overhead by 83.4% compared with other baseline machine learning algorithms.
Tran, VK, Saputra, Y, Dinh, H, Nguyen, LT, Nguyen, D, Nguyen, H & Dutkiewicz, E 2020, 'Collaborative learning model for cyberattack detection systems in IoT industry 4.0', IEEE Wireless Communications and Networking Conference (WCNC),, Seoul.
Vu, TV, Nguyen, D, Dinh, H & Dutkiewicz, E 2019, 'QoS-Aware Fog Computing Resource Allocation using Feasibility-Finding Benders Decomposition', 2019 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, Hawaii.View/Download from: Publisher's site
We investigate a joint offloading and resource allocation under a multi-layer cooperative fog and cloud computing architecture, aiming to minimize the total energy consumption of mobile devices while meeting users' QoS requirements, e.g., delay, security, and application compatibility. Due to the mutual coupling amongst offloading decision and resource allocation variables, the resulting optimization is a mixed integer non- linear programming problem that is NP-hard. Such problem often requires exponential time to find the optimal solution. In this work, we propose a distributed approach, namely feasibility-finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. These (simpler) subproblems can be solved in parallel at fog nodes, thereby reducing both the complexity and the computational time. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., in comparing with the branch-and-bound method).
Xie, Y, Xu, Z, Gong, S, Xu, J, Hoang, DT & Niyato, D 2019, 'Backscatter-assisted hybrid relaying strategy for wireless powered IoT communications', 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.View/Download from: Publisher's site
© 2019 IEEE. In this work, we consider multiple energy harvesting relays to assist information transmission from a hybrid access point (HAP) to a distant receiver. The multi-antenna HAP also beamforms RF power to the relays by using a power-splitting protocol. We aim to maximize the throughput by jointly optimizing the HAP's beamforming strategy as well as individual relays' energy harvesting and collaborative beamforming strategies. With dense user devices, the throughput maximization takes account of the direct links from the HAP to the receiver as they are short and contribute considerably to the overall throughput. Moreover, we introduce the concept of hybrid relaying communications which allows the energy harvesting relays to switch between two radio modes. In particular, the relays can operate either in RF communications or backscatter communications, depending on their channel conditions and energy status. This results in a non-convex and combinatorial throughput maximization problem. With the fixed relay mode, we can find a feasible lower performance bound via convex approximation, which further motivates our algorithm design to update the relay mode in an iterative manner. Simulation results verify that the proposed hybrid relaying strategy can achieve significant performance improvement compared to the conventional relaying strategy with all relays operating in the RF communications mode.
Dinh, H, Niyato, D, Wang, P, Domenico, AD & Strinati, EC 2018, 'Optimal Cross Slice Orchestration for 5G Mobile Services', 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), IEEE Vehicular Technology Conference, IEEE, Chicago, IL, USA, USA.View/Download from: Publisher's site
5G mobile networks encompass the capabilities of hosting a variety of
services such as mobile social networks, multimedia delivery, healthcare,
transportation, and public safety. Therefore, the major challenge in designing
the 5G networks is how to support different types of users and applications
with different quality-of-service requirements under a single physical network
infrastructure. Recently, network slicing has been introduced as a promising
solution to address this challenge. Network slicing allows programmable network
instances which match the service requirements by using network virtualization
technologies. However, how to efficiently allocate resources across network
slices has not been well studied in the literature. Therefore, in this paper,
we first introduce a model for orchestrating network slices based on the
service requirements and available resources. Then, we propose a Markov
decision process framework to formulate and determine the optimal policy that
manages cross-slice admission control and resource allocation for the 5G
networks. Through simulation results, we show that the proposed framework and
solution are efficient not only in providing slice-as-a-service based on the
service requirements, but also in maximizing the provider's revenue.
Dinh, TH, Alsheikh, MA, Gong, S, Niyato, D, Han, Z & Liang, Y-C 2019, 'Defend Jamming Attacks: How to Make Enemies Become Friends', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE.View/Download from: Publisher's site
Nguyen, MH, Hà, MH, Hoang, DT, Nguyen, DN, Dutkiewicz, E & Tran, TT 2019, 'An efficient algorithm for the k-dominating set problem on very large-scale networks (extended abstract)', Computational Data and Social Networks (LNCS), International Conference on Computational Data and Social Networks, Springer, Ho Chi Minh City, Vietnam, pp. 74-76.View/Download from: Publisher's site
© Springer Nature Switzerland AG 2019. The minimum dominating set problem (MDSP) aims to construct the minimum-size subset $$D \subset V$$ of a graph $$G = (V, E)$$ such that every vertex has at least one neighbor in D. The problem is proved to be NP-hard . In a recent industrial application, we encountered a more general variant of MDSP that extends the neighborhood relationship as follows: a vertex is a k-neighbor of another if there exists a linking path through no more than k edges between them. This problem is called the minimum k-dominating set problem (MkDSP) and the dominating set is denoted as $$D:k$$. The MkDSP can be used to model applications in social networks  and design of wireless sensor networks . In our case, a telecommunication company uses the problem model to supervise a large social network up to 17 millions nodes via a dominating subset in which k is set to 3.
Nguyen, MH, Ha, MH, Nguyen, D, Dinh, H, Dutkiewicz, E & Tran, TT 2019, 'An Efficient Algorithm for the k-Dominating Set Problem on Very Large-Scale Networks', International Conference on Computational Data and Social Networks (CSoNet 2019), Ho Chi Minh city.
Nguyen, N-T, Van Huynh, N, Hoang, DT, Nguyen, DN, Nguyen, N-H, Nguyen, Q-T & Dutkiewicz, E 2019, 'Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai.View/Download from: Publisher's site
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2019, 'Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai.View/Download from: Publisher's site
Vu, L, Cao, VL, Nguyen, QU, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2019, 'Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai.View/Download from: Publisher's site
Dinh, H, Niyato, D, Wang, P, Wang, S, Nguyen, D & Dutkiewicz, E 2018, 'A Stochastic Programming Approach for Risk Management in Mobile Cloud Computing', IEEE Wireless Communications and Networking Conference, IEEE, Barcelona, Spain.View/Download from: Publisher's site
The development of mobile cloud computing has brought many benefits to mobile users as well as cloud service providers. However, mobile cloud computing is facing some challenges, especially security-related problems due to the growing number of cyberattacks which can cause serious losses. In this paper, we propose a dynamic framework together with advanced risk management strategies to minimize losses caused by cyberattacks to a cloud service provider. In particular, this framework allows the cloud service provider to select appropriate security solutions, e.g., security software/hardware implementation and insurance policies, to deal with different types of attacks. Furthermore, the stochastic programming approach is adopted to minimize the expected total loss for the cloud service provider under its financial capability and uncertainty of attacks and their potential losses. Through numerical evaluation, we show that our approach is an effective tool in not only dealing with cyberattacks under uncertainty, but also minimizing the total loss for the cloud service provider given its available budget.
Dong, L, Niyato, D, Kim, DI & Hoang, DT 2017, 'A joint scheduling and content caching scheme for energy harvesting access points with multicast', 2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings, IEEE Global Communications Conference, Singapore, Singapore, pp. 1-6.View/Download from: Publisher's site
© 2017 IEEE. In this work, we investigate a system where users are served by an access point that is equipped with energy harvesting and caching mechanism. Focusing on the design of an efficient content delivery scheduling, we propose a joint scheduling and caching scheme. The scheduling problem is formulated as a Markov decision process and solved by an on-line learning algorithm. To deal with large state space, we apply the linear approximation method to the state-Action value functions, which significantly reduces the memory space for storing the function values. In addition, the preference learning is incorporated to speed up the convergence when dealing with the requests from users that have obvious content preferences. Simulation results confirm that the proposed scheme outperforms the baseline scheme in terms of convergence and system throughput, especially when the personal preference is concentrated to one or two contents.
Hoang, DT, Niyato, D & Wang, P 2017, 'Optimal cost-based cyber insurance policy management for mobile services', IEEE Vehicular Technology Conference, Vehicular Technology Conference, Toronto, ON, Canada, pp. 1-5.View/Download from: Publisher's site
© 2017 IEEE. This paper introduces a cyber insurance policy management for the mobile networks in which if a mobile user agrees to purchase an insurance policy from an insurer, the loss of the mobile user, i.e., the insured, will be covered by the insurance policy when the risks happen. To protect mobile users from cyber attacks, the insurer can deploy security protection solutions, e.g., anti-virus software or personal firewall, to the insureds, thereby reducing the risks for mobile users. However, when the solutions are deployed, they will incur a certain cost to the insurer. Therefore, we propose a stochastic optimization based on the reserve state of the insurer and the number of active mobile users to determine whether the protection solutions should be deployed or not to maximize the revenue for the insurer. The performance evaluation reveals that the optimal policy can achieve significantly higher revenue than those of baseline schemes for the insurer. Alternatively, the coalitional game is studied to share the reward among the insurers, and we show that the insurers can gain higher individual rewards through the cooperation.
Nguyen, H, Dinh, H, Nguyen, D, Dutkiewicz, E, Niyato, D & Wang, P 2018, 'Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter', 2018 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, UAE.View/Download from: Publisher's site
Nguyen, K, Dinh, H, Niyato, D, Wang, P, Nguyen, D & Dutkiewicz, E 2018, 'Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach', IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain.
Nguyen, T, Nguyen, H, Dinh, H, Nguyen, D & Dutkiewicz, E 2018, 'Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks', 2018 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates.View/Download from: Publisher's site
Suankaewmanee, K, Dinh, H, Niyato, D, Sawadsitang, S, Wang, P & Han, Z 2018, 'Performance Analysis and Application of Mobile Blockchain', 2018 International Conference on Computing, Networking and Communications, ICNC 2018, International Conference on Computing, Networking and Communications, IEEE, Maui, HI, USA, pp. 642-646.View/Download from: Publisher's site
© 2018 IEEE. Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.
Vu, L, Thuy, HV, Nguyen, QU, Ngoc, TN, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2018, 'Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach', ISCIT 2018 - 18th International Symposium on Communication and Information Technology, International Symposium on Communications and Information Technologies, IEEE, Bangkok, Thailand, pp. 121-126.View/Download from: Publisher's site
© 2018 IEEE. We develop a novel time series feature extraction technique to address the encrypted traffic/application classification problem. The proposed method consists of two main steps. First, we propose a feature engineering technique to extract significant attributes of the encrypted network traffic behavior by analyzing the time series of receiving packets. In the second step, we develop a deep learning-based technique to exploit the correlation of time series data samples of the encrypted network applications. To evaluate the efficiency of the proposed solution on the encrypted traffic classification problem, we carry out intensive experiments on a raw network traffic dataset, namely VPN-nonVPN, with three conventional classifier metrics including Precision, Recall, and F1 score. The experimental results demonstrate that our proposed approach can significantly improve the performance in identifying encrypted application traffic in terms of accuracy and computation efficiency.
Wang, H, Nguyen, D, Dinh, H, Dutkiewicz, E & Cheng, Q 2018, 'Real-Time Crowdsourcing Incentive for Radio Environment Maps: A Dynamic Pricing Approach', 2018 IEEE Global Communications Conference (GLOBECOM) Proceedings, IEEE Global Communications Conference, IEEE, UAE.View/Download from: Publisher's site
Hoang, DT, Niyato, D, Wang, P & Kim, DI 2017, 'Optimal time sharing in RF-powered backscatter cognitive radio networks', IEEE International Conference on Communications, IEEE International Conference on Communications (ICC), IEEE, Paris, France.View/Download from: Publisher's site
© 2017 IEEE. In this paper, we propose a novel network model for RF-powered cognitive radio networks and ambient backscatter communications. In the network under consideration, each secondary transmitter is able to backscatter primary signals to the gateway for data transfer or to harvest energy from the primary signals and then use that energy to transmit data to the gateway. To maximize overall network throughput of the network, we formulate an optimization problem with the aim of finding not only an optimal tradeoff between data backscattering time and energy harvesting time, but also time sharing among multiple secondary transmitters. Through the numerical results, we demonstrate that the solution of the optimization problem always achieves the best performance compared with two other baseline schemes.
Hoang, DT, Niyato, D, Wang, P, Kim, DI & Le, LB 2017, 'Overlay RF-powered backscatter cognitive radio networks: A game theoretic approach', IEEE International Conference on Communications, IEEE International Conference on Communications (ICC), IEEE, Paris, France.View/Download from: Publisher's site
© 2017 IEEE. In this paper, we study an overlay RF-powered cognitive radio network with ambient backscatter communications. In the network, when the channel is occupied, the secondary transmitter (ST) can perform either energy harvesting or data transmission using ambient backscattering technique to a gateway. We consider the case that the gateway charges the ST a certain price if the ST transmits information. This leads to questions of how to determine the best price for the gateway and how to find the optimal backscatter time. To address this problem, we propose a Stackelberg game in which the gateway is the leader adapting the price to maximize its profit in the first stage. Meanwhile, the ST chooses its backscatter time to maximize its utility in the second stage. To analyze the game, we apply the backward induction technique. We show that the game always has a unique subgame perfect Nash equilibrium. Additionally, our results provide insights on the impact of the competition on the players' profit and utility.
Dinh, TH & Niyato, D 2016, 'Information Service Pricing Competition in Internet-of-Vehicle (IoV)', 2016 INTERNATIONAL CONFERENCE ON COMPUTING, NETWORKING AND COMMUNICATIONS (ICNC), International Conference on Computing, Networking and Communications (ICNC), IEEE, Kauai, HI.
Hoang, DT, Niyato, D, Wang, P, Kim, DI & Han, Z 2016, 'The Tradeoff Analysis in RF-Powered Backscatter Cognitive Radio Networks', Washington, DC, USA.View/Download from: Publisher's site
In this paper, we introduce a new model for RF-powered cognitive radio
networks with the aim to improve the performance for secondary systems. In our
proposed model, when the primary channel is busy, the secondary transmitter is
able either to backscatter the primary signals to transmit data to the
secondary receiver or to harvest RF energy from the channel. The harvested
energy then will be used to transmit data to the receiver when the channel
becomes idle. We first analyze the tradeoff between backscatter communication
and harvest-then-transmit protocol in the network. To maximize the overall
transmission rate of the secondary network, we formulate an optimization
problem to find time ratio between taking backscatter and harvest-then-transmit
modes. Through numerical results, we show that under the proposed model can
achieve the overall transmission rate higher than using either the backscatter
communication or the harvest-then-transmit protocol.
Dinh, TH, Niyato, D & Nguyen, TH 2015, 'Optimal Energy Allocation Policy for Wireless Networks in the Sky', 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE International Conference on Communications (ICC), IEEE, London, ENGLAND, pp. 3204-3209.
Dinh, TH, Niyato, D & Kim, DI 2014, 'Cooperative Bidding of Data Transmission and Wireless Energy Transfer', 2014 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Istanbul, TURKEY, pp. 1597-1602.
Dinh, TH, Niyato, D & Le, LB 2014, 'Simulation-Based Optimization for Admission Control of Mobile Cloudlets', 2014 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE International Conference on Communications (ICC), IEEE, Sydney, AUSTRALIA, pp. 3764-3769.
Dinh, TH, Niyato, D, Wang, P & Kim, DI 2014, 'Optimal Decentralized Control Policy for Wireless Communication Systems with Wireless Energy Transfer Capability', 2014 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE International Conference on Communications (ICC), IEEE, Sydney, AUSTRALIA, pp. 2835-2840.
Dinh, TH & Niyato, D 2012, 'Performance Analysis of Cognitive Machine-to-Machine Communications', 2012 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS (IEEE ICCS 2012), IEEE International Conference on Communication Systems (ICCS), IEEE, Singapore, SINGAPORE, pp. 245-249.
Dinh, TH, Niyato, D & Wang, P 2012, 'Optimal Admission Control Policy for Mobile Cloud Computing Hotspot with Cloudlet', 2012 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Paris, FRANCE, pp. 3145-3149.
Long, BL, Dinh, TH, Niyato, D, Hossain, E & Kim, DI 2012, 'Joint Load Balancing and Admission Control in OFDMA-Based Femtocell Networks', 2012 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), IEEE International Conference on Communications (ICC), IEEE, Ottawa, CANADA.
Hung, NT, Thanh, NH, Nam, NG, Lan, TN & Hoang, DT 2011, 'IMS IPTV: An experimental approach', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 573-576.View/Download from: Publisher's site
IMS has been widely recognized as the control and signaling framework for delivering of the rich communication & multimedia services to broadband users. Amongst others, it's deploying as the service (middleware) platform for interactive and personalized IPTV services. The goal of this paper is to provide a short description and analysis of the (IPTV) use cases that have been selected for design and implementation at Hanoi University of Technology (HUT) in scope of its initiatives for NGN researching program. Major use cases, or we called intelligent features, are the advanced electronic service guide, video on demand (VoD), (IPTV) session continuity, and parental control. Development results for each of the use case are depicted. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2011.