Al-Doghman, F, Chaczko, Z & Rakhi Ajayan, A 2020, 'Policy-Based Consensus Data Aggregation for the Internet of Things' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nsture, Cham, Switzerland, pp. 119-130.View/Download from: Publisher's site
The new trend of the Internet of Things brings a whole breed of opportunities and applications. Within it, a massive amount of data coming from heterogeneous sources travel in a bidirectional way. Data aggregation is one of the most efficient ways to mitigate Big Data. However, using one type of aggregation within a network at all times is not an optimal option. Various network situations require different aggregation functions at different times. We introduce a policy-based data aggregation framework that can handle this issue by referring to a policy when executing the aggregation strategy. An agreement process is used to reach consensus about the aggregation function that is to be applied on the network (or part of it) at a specific time. Participants are to negotiate the policy terms based on the current network status and the nature of the coming requests. The framework represents a promising scope for fully automated IoT.
Rakhi Ajayan, A, Al-Doghman, F & Chaczko, Z 2020, 'Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nature, Cham, Switzerland, pp. 1-4-118.View/Download from: Publisher's site
Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams' behavioural trends across various modalities, without
numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes.
Ajayan, AR, Al-Doghman, F & Chaczko, Z 2018, 'Visualizing Multimodal Big Data Anomaly Patterns in Higher-Order Feature Spaces', 2018 26th International Conference on Systems Engineering (ICSEng), International Conference on Systems Engineering, IEEE, Sydney, NSW, Australia, pp. 1-9.View/Download from: Publisher's site
The world today, as we know it, is profuse with information about humans and objects. Datasets generated by cyber-physical systems are orders of magnitude larger than their current information processing capabilities. Tapping into these big data flows to uncover much deeper perceptions into the functioning, operational logic and smartness levels attainable has been investigated for quite a while. Knowledge Discovery & Representation capabilities across mutiple modalities holds much scope in this direction, with regards to their information holding potential. This paper investigates the applicability of an arithmetic tool Tensor Decompositions and Factorizations in this scenario. Higher order datasets are decomposed for Anomaly Pattern capture which encases intelligence along multiple modes of data flow. Preliminary investigations based on data derived from Smart Grid Smart City Project are compliant with our hypothesis. The results proved that Abnormal patterns detected in decomposed Tensor factors encompass deep information energy content from Big Data as efficiently as other Pattern Extraction and Knowledge Discovery frameworks, while salvaging time and resources.
Al-Doghman, F, Chaczko, Z, Brooke, W & Gordon, LC 2019, 'Social Consensus-inspired Aggregation Algorithms for Edge Computing', 2019 3rd Cyber Security in Networking Conference, CSNet 2019, pp. 138-141.View/Download from: Publisher's site
© 2019 IEEE. The current interest about the∗nternet of Things (IoT) evokes the establishment of infinite services giving huge, active, and varied information sets. Within it, an enormous mass of heterogeneous data are generated and interchanged by billions of device which can yield to an enormous information traffic jam and affects network efficiency. To get over this issue, there's a necessity for an effective, smart, distributed, and in-network technique that uses a cooperative effort to aggregate data along the pathway from the network edge to its sink. we tend to propose an information organization blueprint that systematizes data aggregation and transmission within the bounds of the Edge domain from the front-end until the Cloud. A social consensus technique obtained by applying statistical analysis is employed within the blueprint to get and update a policy concerning a way to aggregate and transmit data according to the order of information consumption inside the network. The Propose technique, consensus Aggregation, uses statistical Machine Learning to consolidate the approach and appraise its performance. inside the normal operation of the approach, data aggregation is performed with the utilization of data distribution. A notable information delivery efficiency was obtained with a nominal loss in precision as the blueprint was tested inside a particular environment as a case study. The conclusion of the strategy showed that the consensus approach overcome the individual ones in several directions.
Al-Doghman, F, Chaczko, Z & Brookes, W 2018, 'Adaptive Consensus-based Aggregation for Edge Computing', 2018 26th International Conference on Systems Engineering (ICSEng), International Conference on Systems Engineering, IEEE, Sydney, Australia.View/Download from: Publisher's site
The swift expansion in employing IoT and the tendency to apply its application have encompassed a wide range of fields in our life. The heterogeneity and the massive amount of data produced from IoT require adaptive collection and transmission processes that function closed to front-end to mitigate these issues. In this paper, We introduced a method
of aggregating IoT data in a consensus way using Bayesian analysis and Markov Chain techniques. The aim is to enhance the quality of data traveling within IoT framework.
Al-Doghman, F, Chaczko, Z & Jiang, J 2017, 'A review of aggregation algorithms for the internet of things', Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017, International Conference on Systems Engineering, IEEE, Piscataway, USA, pp. 480-487.View/Download from: Publisher's site
© 2017 IEEE. The Internet of Things (IoT) epitomizes the upcoming eminent transition in the world's economy and human lifestyle where people and various objects are correlated within networks. Data Aggregation is a technique which can be used to mitigate Big Data challenges within IoT. This paper provides an overview of various approaches for aggregation of data in IoT infrastructure. A new class of reliable Data Aggregation algorithm is discussed as well. This new class of algorithm uses a consensus based aggregation with fault tolerance methodology in Fog Computing. The new approach allows promoting adaptive behavior and more efficient delivery of the aggregation outcomes to the ascendant node(s). The proposed method is fault tolerant and deals with nodes reliability issues.
Jiang, J, Chaczko, Z, Al-Doghman, F & Narantaka, W 2017, 'New LQR protocols with intrusion detection schemes for IOT security', Proceedings - 25th International Conference on Systems Engineering, ICSEng 2017, International Conference on Systems Engineering, IEEE, Las Vegas, NV, USA, pp. 466-474.View/Download from: Publisher's site
© 2017 IEEE. Link quality protocols employ link quality estimators to collect statistics on the wireless link either independently or cooperatively among the sensor nodes. Furthermore, link quality routing protocols for wireless sensor networks may modify an estimator to meet their needs. Link quality estimators are vulnerable against malicious attacks that can exploit them. A malicious node may share false information with its neighboring sensor nodes to affect the computations of their estimation. Consequently, malicious node may behave maliciously such that its neighbors gather incorrect statistics about their wireless links. This paper aims to detect malicious nodes that manipulate the link quality estimator of the routing protocol. In order to accomplish this task, MINTROUTE and CTP routing protocols are selected and updated with intrusion detection schemes (IDSs) for further investigations with other factors. It is proved that these two routing protocols under scrutiny possess inherent susceptibilities, that are capable of interrupting the link quality calculations. Malicious nodes that abuse such vulnerabilities can be registered through operational detection mechanisms. The overall performance of the new LQR protocol with IDSs features is experimented, validated and represented via the detection rates and false alarm rates.
Al-Doghman, F, Chaczko, Z, Ajayan, A & Klempous, R 2016, 'A Review on Fog Computing Technology', Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), IEEE International Conference on Systems, Man and Cybernetics, IEEE, Budapest, Hungary, pp. 1525-1530.View/Download from: Publisher's site
Out of the many computing and software oriented models that are being adopted by Computer Networking, Fog Computing has captured quite a wide audience in Research and Industry. There is a lot of confusion on its precise definition, position, role and application. The Internet of Things (IOT), todays' digitized intelligent connectivity domain, demands real time response in many applications and services. This renders Fog Computing a suitable platform for achieving goals of autonomy and efficiency. This paper is a justification of the concepts, interest, approaches, and practices of Fog Computing. It describes the need for adopting this new model and investigate its prime features by elucidating the scenarios for implementing it, thereby outlining its significance in the IoT world.