A Dynamic Game with Adaptive Strategies for IEEE 802.15.4 and IoT

Abstract

The problem of selfishness and misbehaviour in wireless networks is well known, as are the associated solutions that have been proposed for it in IEEE 802.11 Wireless Local Area Network (WLAN) and Wireless Sensory Network (WSN). However, tackling such problem in relation to the Internet of Things (IoT) is relatively new since the IoT is still under development. The central communication infrastructure of IoT is the IEEE 802.15.4 standard which defines low-rate and low energy wireless personal area networks. In order to share the medium fairly and efficiently in a beacon-enabled mode, the standard uses Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) in the Contention Access Period (CAP), and Guarantee Time Slot (GTS) in the Contention Free Period (CFP) of a super-frame. These channel sharing mechanisms are known to be vulnerable to selfishness, misbehaviour and channel capture as a result of nodes disobeying the communication rules. Most of the existing game theoretic solutions were designed for IEEE 802.11 WLAN and WSN. In this work, we present a dynamic game in which nodes can select and adapt their strategies of play according to the 'state of the game' and their energy level in order to increase their utility whenever their utility declined. Our model enables resources constrained nodes to optimised their strategies individually based upon the current state of the game and their available resources. Our analysis and simulation results suggest an improvement in utility, and fairness in channel sharing, as well as efficiency in energy usage in our dynamic model and hence performance and security in our scheme over the default IEEE 802.15.4 access mechanism.

Enhancing Channel Contention Efficiency in IEEE 802.15.4 Wireless Networks

“…Z. Tao et al [6] used a Markov chain to model the CSMA-CA mechanism in IEEE 802.15.4 networks and pointed out that a slight modification to the protocol can considerably improve throughput and delay. J. Abegunde et al [7] presented a game theory-based solution to 802.15.4 channel contention, which reduces energy consumption and increases fairness. Z. Zhang et al [8] applied a priority mechanism and virtual carrier sensing to the traditional CSMA-CA algorithm to improve throughput and reduce energy consumption.…”

Design of a Dynamic and Self-Adapting System, Supported with Artificial Intelligence, Machine Learning and Real-Time Intelligence for Predictive Cyber Risk Analytics in Extreme Environments – Cyber Risk in the Colonisation of Mars

“…Network Security and Breach Detection [61] Game theory and NB classifier [62], [63] Deep learning algorithms [64], [65] Algorithms: supervised, unsupervised and reinforcement learning [66] Table 1: AI/ML algorithm application for descriptive, predictive, and prescriptive risk analytics in edge computing Table 1 confirms that by integrating AI/ML in the risk analytics, we can devise a new approach for cognitive data analytics, creating a stronger resilience of systems through cognition in their physical, digital and social dimensions. This approach resolves around understanding how and when compromises happen, to enable systems to adapt and continue to operate safely and securely when they have been compromised.…”

“…Optimising and balancing resource constrains in edge computing has been investigated with 'dynamic game' [61] and 'game theory' [62] strategies. Such optimisation is primarily theoretical, but highly relevant for red teaming of edge computing risks.…”

Identifying Misbehaving Greedy Nodes in IoT Networks

“…In [ 18 ], the authors propose a countermeasure to misbehaviour attacks and optimized energy consumption. They propose a model of the IEEE 802.15.4 standard in the form of a dynamic game, where the different nodes of the network represent players able to assess the state of the game, then select and adapt their game strategies while optimizing energy consumption.…”