Game theory based distributed clustering approach to maximize wireless sensors network lifetime

Kassan, Sara; Gaber, Jaafar; Lorenz, Pascal

doi:10.1016/j.jnca.2018.09.004

Cited by 36 publications

(23 citation statements)

References 29 publications

(30 reference statements)

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“…The mode switch between sleep and active can help save energy [67] . To save energy, minimizing the energy consumption [68] or maximizing lifetime [69,70] can be the objective, and EH can be used to obtain energy. Energy will be consumed at different states: monitoring, processing, communication, and state transferring [71] .…”

Section: Energy Savingmentioning

confidence: 99%

The development of green wireless mesh network: A survey

Chai¹,

Zeng²

2021

JSEGC

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Wireless mesh network (WMN) is a type of self-healing, self-configuration, and peer-to-peer wireless network. Without expensive and fixed base stations, WMN can be established fast, easily, and flexibly at a low cost. The convenience and flexibility of establishing WMN brings a wide range of applications. The request of high bandwidth, high coverage and high transmission rate can be satisfied. Current research on WMN involves routing, resource allocation, mobility control, security and so on. With the increasing concern of green communication, improving energy efficiency is more and more important nowadays. As energy harvesting can overcome energy constraints and extend the network lifetime, it has attracted the great attention from many researchers when designing network. Further, WMN is an important networking mode in next-generation communication to guarantee the quality of service and reduce the networking complexity. Due to such importance, a survey of the research and development in WMN including the future research direction and opportunities is given in this paper.

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Section: Energy Savingmentioning

confidence: 99%

The development of green wireless mesh network: A survey

Chai¹,

Zeng²

2021

JSEGC

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“…Combining with (11) and (13), the fitness function of the cluster head selection optimization can be expressed as:…”

Section: Step1: Derivation Of the Fitness Functionmentioning

confidence: 99%

“…The main application scenario of clustering protocols has changed from ad-hoc networks to the WSNs in recent years. Clustering segments the WSN into different clusters [11]. Each cluster consists of one cluster head (CH) and several cluster members (CM).…”

Section: Introductionmentioning

confidence: 99%

Clustering the Wireless Sensor Networks: A Meta-Heuristic Approach

et al. 2020

IEEE Access

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Lifetime is one of the most critical indexes of the Wireless Sensor Network (WSN). In this paper, we propose a clustering protocol based on the meta-heuristic approach (CPMA). CPMA takes the network lifetime as the primary consideration and consists of two parts. The first part focuses on the online cluster head selection and network communication coordination. The selection is based on the Harmony Search (HS) Algorithm, which aims to reduce the total energy dissipation and smooth the energy distribution throughout the network. Currently, most clustering protocols cannot automatically tune the corresponding protocol parameters according to the diversity of different WSNs. To solve such issue, the second part of CPMA uses the Artificial Bee Colony (ABC) algorithm to optimize its crucial parameters. The optimization is offline and will be executed only once before the network is working. We make a detailed comparison of CPMA with classical clustering protocols. The results show that CPMA can better prolong the network lifetime and improve network throughput under almost all the network conditions. Furthermore, our simulation also exhibits that CPMA has good adaptability and performs well under different network lifetime definitions. All the results prove that CPMA has the advantages of being suitable and efficient for a wide number of WSN applications.

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“…heads were elected based on the set of nodes with the highest residual energy, and clusters are formed by using coalitional game. In [34], the authors investigated a new clustering approach by mixing a non-cooperative game theory technique with a decentralized clustering algorithm in WSNs to maximize the network lifetime. Specifically, the game theory approach was used to limit the number of the forwarding messages as well as to maximize the lifetime of sensors.…”

Section: A Related Work and Motivationsmentioning

confidence: 99%

A Game Theory Based Clustering Protocol to Support Multicast Routing in Cognitive Radio Mobile Ad Hoc Networks

et al. 2020

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The frequent movement of nodes in cognitive radio mobile ad hoc networks (CRAHNs) causes challenges in scalability, stability, channel sensing and channel access problems that can be solved by using clustering technique. Game theory is a feasible approach to solve such problems by casting clustering problems as distributed optimization problems. The main contributions of this paper are as follows. Firstly, we propose a minimum connected weighted inner edge spanning tree (MWIEST) game to find an approximate solution of a MWIEST problem in CRAHNs. In this game, a link-weight function of each link is designed based on a combination of link-stability and link-connectivity ratio functions. Secondly, we prove that the MWIEST game is an exact potential game that exists at least one Nash equilibrium (NE) point which is an approximate solution of the MWIEST problem. Besides, we also prove that best responses (BRs) of the game converge to a NE in finite iterations. Thirdly, based on the MWIEST game, we propose four algorithms including the node information exchange (NIE), the best response selection (BRS), the intermediate nodes selection (INS) and the forming cluster (FC). Specifically, the algorithms NIE, BRS and INS provide a set of intermediate nodes (SetIN) which supports the FC algorithm to form clusters. Finally, we propose the game theory based clustering (GBC) protocol which is combination of the FC algorithm and the proposed cluster maintenance algorithm to construct high stable clusters supporting multicast routing in CRAHNs. Moreover, each obtained cluster includes most members having the same receiving channel which avoids the affected regions of licensed channels. For the performance evaluation, we implement the GBC protocol in OMNET++ platform to demonstrate its performance improvement over the state-of-the-art protocols in terms of network stability and control overheads. INDEX TERMS Cognitive radio mobile ad hoc network, clustering, multicast routing, game theory, network layer, OMNET++.

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Game theory based distributed clustering approach to maximize wireless sensors network lifetime

Cited by 36 publications

References 29 publications

The development of green wireless mesh network: A survey

The development of green wireless mesh network: A survey

Clustering the Wireless Sensor Networks: A Meta-Heuristic Approach

A Game Theory Based Clustering Protocol to Support Multicast Routing in Cognitive Radio Mobile Ad Hoc Networks

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