M. Haghighi, K. Maraslis, T. Tryfonas, G. Oikonomou, A. Burrows, P. Woznowski, "Game Theoretic Approach Towards Optimal Multi-tasking and Data-distribution in IoT", in Proc. IEEE World Forum on Internet of Things (WF-IoT), pp. 406-411, 2015
Current applications of Internet of Things (IoT) often require nodes to implement logical decision-making on aggregated data, which involves more processing and wider interactions amongst network peers, resulting in higher energy consumption and shorter node lifetime. This paper presents a game theoretic approach used in Sensomax, an agent-based WSN middleware that facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this context, we investigate game theoretic and auction-based techniques to optimise task distribution and energy consumption in IoT networks of multiple concurrent WSNs. We also demonstrate how our proposed game theoretic approach affects the performance of WSN applications with different operational paradigms.
K. Maraslis, T. Spyridopoulos, G. Oikonomou, T. Tryfonas, M. Haghighi, "Application of a Game Theoretic Approach in Smart Sensor Data Trustworthiness Problems", in Proc. 30th IFIP TC 11 International Conference (SEC), ser. IFIP Advances in Information and Communication Technology, 455, pp. 601-615, 2015
In this work we present an Intrusion Detection (ID) and an Intrusion Prevention (IP) model for Wireless Sensor Networks (WSNs). The attacker’s goal is to compromise the deployment by causing nodes to report faulty sensory information. The defender, who is the WSN’s operator, aims to detect the presence of faulty sensor measurements (ID) and to subsequently recover compromised nodes (IP). In order to address the conflicting interests involved, we adopt a Game Theoretic approach that takes into consideration the strategies of both players and we attempt to identify the presence of Nash Equilibria in the two games. The results are then verified in two simulation contexts: Firstly, we evaluate the model in a middleware-based WSN which uses clustering over a bespoke network stack. Subsequently, we test the model in a simulated IPv6-based sensor deployment. According to the findings, the results of both simulation models confirm the results of the theoretic one.
M. Haghighi, K. Maraslis, G. Oikonomou, T. Tryfonas, "Game Theoretic Approach Towards Energy - Efficient Task Distribution in Multitasking Wireless Sensor Networks", in Proc. IEEE Sensors 2015, 2015
WSNs have a wide variety of applications, and their usability for remote monitoring of various parameters of interest is growing dramatically. Conventional applications mostly involved a single WSN for collecting raw parameters with limited aggregation on the node side, whereby more sophisticated data mining was implemented by the end-users. Recent applications however, often require more intelligent functions, in which nodes are expected to implement logical decision-makings on the aggregated data. Implementing such functions often involves more processing, and wider interactions amongst network peers, hence resulting in higher energy consumption and shorter node lifetime. Sensomax is an agent-based WSN middleware, which facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this paper, we will investigate game theoretic and auction-based techniques in order to optimise task distribution and energy consumption in WSNs.
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