Energy-efficient clustering algorithm based on game theory for wireless sensor networks

Liu, Qingwei; Liu, Mandan

doi:10.1177/1550147717743701

Cited by 28 publications

(19 citation statements)

References 37 publications

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“…GEEC reduces energy consumed in routing control messages in CH election. In an energy‐efficient clustering algorithm based on game theory (ECAGT), CH is elected based on number of neighbors in its communication radius and residual energy available at each node.…”

Section: Related Workmentioning

confidence: 99%

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An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

Thandapani

Arunachalam

Durairaj

2020

Int J Communication

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Most of the current generation sensor nodes of mobile wireless sensor network (MWSN) are designed to have heterogeneous mobility to adapt itself in the applied environment. Energy optimization in MWSN with heterogeneous mobility is very challenging task. In this paper, a heterogeneous game theoretical clustering algorithm called mobile clustering game theory-1 (MCGT-1) is proposed for energy optimization in a heterogeneous mobile sensor environment. Energy optimization is achieved through energy-efficient cluster head election and multipath routing in the network. A heterogeneous clustering game is modelled with varying attributes and located an asymmetric equilibrium condition for a symmetric game with mixed strategies. The real-time parameters, namely, predicted remaining energy, distance between a base station and nodes, distance between nodes, and mobility speed, were used to calculate the probability to elect the cluster head (CH). The efficient multipath routing is achieved through prior energy prediction strategy. It has mitigated the generation of "hot spots," reducing its delay and improving the overall residual energy of the network. Simulation results showed that the average lifetime of MCGT-1 has increased by 6.33 %, 13.1% and 14.2% and the PDR has improved by 4.8%,11.8%, and 17.2% than MCGT, LEACH-ME and LEACH-M respectively. The hot spot delay is reduced to 0.063025 seconds, improving the efficiency of the network. K E Y W O R D Sclustering, mobility, predicted remaining energy, hot spot problem it is essential to use the battery power in an optimal way. Secondly, the battery energy gets depleted rapidly during active communication if it is not properly monitored and controlled. Therefore, a better energy optimization in a sensor network is inevitable, and hence, a clustering architecture is proposed.Many protocols for mobile sensing environment works with constant mobile speed or with homogeneous mobility. 4 In the existing protocols, the node speed and energy dissipation remain constant irrespective of the distance moved. These phenomena lead to waste of the attained bandwidth and timeslot and affect the overall network performance, lifetime, and quality of service (QoS). In practice, it is not possible to attain the constant mobility speed throughout thedeployment and in real-time, the nodes may move with different speeds either arbitrarily or naturally called-heterogeneous mobility. 4 According to Osborne, 5 a game is defined as a method of competitive play by-law. Game theory (GT) is a mathematical branch that deals with competitive situations while analyzing the strategies of every player and produce an outcome based on actions of other participants. In recent years, GT is applied in clustering of sensor network. Since then, all the algorithms are proposed for a static environment where the nodes are fixed without frequent clustering. The game of these algorithms is not defined from the view of sensors and its topology. Further, these algorithms do not provide a solution for hot spot problem i...

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Section: Related Workmentioning

confidence: 99%

“…The proposed algorithm uses first‐order radio model for handling energy dissipation in the network . Energy spent on transmitting and receiving “ l ” bits of data to and from distance “ d ” is given in Equations , , and , respectively.…”

Section: System Modelmentioning

confidence: 99%

An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

Thandapani

Arunachalam

Durairaj

2020

Int J Communication

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“…The EGT was used to reduce signaling overhead, complexity and increase cluster stability in large scale VANETs. In [29], the authors proposed an energy-efficient clustering algorithm based on game theory. In this algorithm, each node competes to be a cluster head by joining a localized clustering game in order to achieve equilibrium probability.…”

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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“…Also, the network lifetime is greatly enhanced. Energy‐efficient clustering algorithm based on game theory (ECAGT) was put forward by Liu and Liu [9]. Based on game theory, the ECAGT also can extend network life span and improve data transmission.…”

Section: Introductionmentioning

confidence: 99%

“…However, Yang et al . [8] and Liu and Liu [9] did not consider the residual energy of different nodes, which may cause some nodes to die quickly. So, Luo et al [10] introduced a residual energy collection mechanism of neighbouring nodes.…”

Section: Introductionmentioning

confidence: 99%

Distributed resource allocation optimisation algorithm based on particle swarm optimisation in wireless sensor network

et al. 2020

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Energy-efficient clustering algorithm based on game theory for wireless sensor networks

Cited by 28 publications

References 37 publications

An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

An energy‐efficient clustering and multipath routing for mobile wireless sensor network using game theory

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

Distributed resource allocation optimisation algorithm based on particle swarm optimisation in wireless sensor network

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