A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Zheng, Weimin; Meng, Fanying; Liu, Ning; Huang, Shuo

doi:10.3390/s23198055

Cited by 4 publications

(3 citation statements)

References 52 publications

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“…MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency [46,47].…”

Section: Related Workmentioning

confidence: 98%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these restrictions. Clustering may, therefore, be thought of as the best way to solve such issues. Consequently, it is crucial to analyze effective Cluster Head (CH) selection to maximize efficiency throughput, extend the network lifetime, and minimize energy consumption. This paper proposed an Accelerated Particle Swarm Optimization (APSO) algorithm based on the Low Energy Adaptive Clustering Hierarchy (LEACH), Neighboring Based Energy Efficient Routing (NBEER), Cooperative Energy Efficient Routing (CEER), and Cooperative Relay Neighboring Based Energy Efficient Routing (CR-NBEER) techniques. With the help of APSO in the implementation of the WSN, the main methodology of this article has taken place. The simulation findings in this study demonstrated that the suggested approach uses less energy, with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH, 1.003 to 0.0521 for 10 CH, and 0.1734 to 0.0911 for 15 CH. The sending packets ratio was also raised for all three CH selection scenarios, increasing from 659 to 1730. The number of dead nodes likewise dropped for the given combination, falling between 71 and 66. The network lifetime was deemed to have risen based on the results found. A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol. Similar to underwater, WSN can make use of the proposed protocol. The overall results have been evaluated and compared with the existing approaches of sensor networks.

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Section: Related Workmentioning

confidence: 98%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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“…In contrast, the attack detection at the smart grid data level is very similar to the principle of network attack and defence game [19]. Aiming at the shortcomings of the existing intrusion detection systems that consume high energy and can only detect specific attacks, Game theory is combined with autoregressive models for attack detection, and the game problem between intruders and detectors is ingeniously transformed into a static game model with complete information and non-cooperation [20]. The results show that the proposed model can reduce the energy consumption in the intrusion detection process to predict attack patterns accurately.…”

Section: Introductionmentioning

confidence: 99%

Analysis of safe electricity consumption on load side based on attack and defence game model

Wang,

Gong,

Chen

et al. 2024

The Journal of Engineering

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With the improvement of digitalization and intelligence in the power system, the safe operation of the power system is facing enormous challenges. The safe use of electricity on the load side is the key to achieving safe and reliable power system operation. The detection party needs amounts of human and material resources when the power network is attacked. In response to the current difficulties of low detection ability and high detection costs, this paper proposes an attack and defence game model that considers the differences between different nodes, ensuring the safety and economy of electricity consumption while reducing energy waste. At first, the structure of smart meters and the attack characteristics of intruders are summarized, and a basic attack and defence game model is constructed. The Nash equilibrium is then solved, and the optimal strategy for the game between the defender and the intruder is given to balance the relation between detection performance and energy consumption. In response to the differences generated by each node, strategies for attackers to launch attacks on different nodes and the setting of optimal thresholds for other nodes in the defence system are explored. Finally, case studies verify that the proposed model could reduce the cost of intruder detection while ensuring a specific detection rate.

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“…The game-playing process can be significantly influenced by the player's cognition regarding the possibilities of different choices and the suitability of these choices, since a player makes his/her choices based on such a cognition. To model and explain the various interactive decision-making scenarios in social and economic activities [12,13], different game-theory models have been studied. A typical game model is extensive games [14][15][16][17]; this model is used in sequential decision-making (SDM) scenarios [18].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Playing Extensive Games with Learning of Opponent’s Cognition

Liu,

Cong,

Yao

et al. 2024

Sensors

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Decision-making is a basic component of agents’ (e.g., intelligent sensors) behaviors, in which one’s cognition plays a crucial role in the process and outcome. Extensive games, a class of interactive decision-making scenarios, have been studied in diverse fields. Recently, a model of extensive games was proposed in which agent cognition of the structure of the underlying game and the quality of the game situations are encoded by artificial neural networks. This model refines the classic model of extensive games, and the corresponding equilibrium concept—cognitive perfect equilibrium (CPE)—differs from the classic subgame perfect equilibrium, since CPE takes agent cognition into consideration. However, this model neglects the consideration that game-playing processes are greatly affected by agents’ cognition of their opponents. To this end, in this work, we go one step further by proposing a framework in which agents’ cognition of their opponents is incorporated. A method is presented for evaluating opponents’ cognition about the game being played, and thus, an algorithm designed for playing such games is analyzed. The resulting equilibrium concept is defined as adversarial cognition equilibrium (ACE). By means of a running example, we demonstrate that the ACE is more realistic than the CPE, since it involves learning about opponents’ cognition. Further results are presented regarding the computational complexity, soundness, and completeness of the game-solving algorithm and the existence of the equilibrium solution. This model suggests the possibility of enhancing an agent’s strategic ability by evaluating opponents’ cognition.

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A Game Model for Analyzing Wireless Sensor Networks of 5G Environment Based on Adaptive Equilibrium Optimizer Algorithm

Cited by 4 publications

References 52 publications

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Analysis of safe electricity consumption on load side based on attack and defence game model

Playing Extensive Games with Learning of Opponent’s Cognition

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