A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Yang, Liu; Lu, Yinzhi; Xiong, Lihua; Tao, Yang; Zhong, Yuanchang

doi:10.3390/s17112654

Cited by 34 publications

(35 citation statements)

References 32 publications

(42 reference statements)

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“…In Game theory, a non cooperative game contains a set of players N, set of actions U to take a decision for each player, and the payoff p is a reward or penalty for each player [ 57 ]. Nash equilibrium (NE) is a decision state, when the player reaches the outcome.…”

Section: Cluster Tree-based Routing Protocolmentioning

confidence: 99%

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Luhach

et al. 2020

Sensors

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Energy conservation is one of the most critical challenges in the Internet of Things (IoT). IoT devices are incredibly resource-constrained and possess miniature power sources, small memory, and limited processing ability. Clustering is a popular method to avoid duplicate data transfer from the participant node to the destination. The selection of the cluster head (CH) plays a crucial role in gathering and aggregating the data from the cluster members and forwarding the data to the sink node. The inefficient CH selection causes packet failures during the data transfer and early battery depletion nearer to the sink. This paper proposes a cluster tree-based routing protocol (CT-RPL) to increase the life span of the network and avoid the data traffic among the network nodes. The CT-RPL involves three processes, namely cluster formation, cluster head selection, and route establishment. The cluster is formed based on the Euclidean distance. The CH selection is accomplished using a game theoretic approach. Finally, the route is established using the metrics residual energy ratio (RER), queue utilization (QU), and expected transmission count (ETX). The simulation is carried out by using a COOJA simulator. The efficiency of a CT-RPL is compared with the Routing Protocol for Low Power and Lossy Networks (RPL) and energy-efficient heterogeneous ring clustering routing (E2HRC-RPL), which reduces the traffic load and decreases the packet loss ratio. Thus, the CT-RPL enhances the lifetime of the network by 30–40% and the packet delivery ratio by 5–10%.

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Section: Cluster Tree-based Routing Protocolmentioning

confidence: 99%

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Luhach

et al. 2020

Sensors

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“…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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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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“…Proof. According to Property 4 and Property 5, equation (19) can be simplified as follows: Each time that m i ðkÞ selects the next node, the drone always select some node with a higher probability [31]; it can be expressed as follows:…”

Section: Properties Of the Models Of Popularitymentioning

confidence: 99%

PB: A Message Transmission Method Based on Area Layer Division in UAV Networks

Wang

Zhu

Zhou³

et al. 2019

International Journal of Aerospace Engineering

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A search and rescue mission is a typical UAV network application scenario. In this case, it is necessary to deliver messages quickly and efficiently to the ground station through mutual cooperation between UAVs. Many methods used in this case have problems such as unbalanced popularity (the ratio of the relayed message number to the total message number) of nodes, large proportion of ping-pong effect, and long delay. In view of the above problems, this paper proposes a method named PB (Popularity Balance Method for UAVs in the same area layer) based on division of the whole search area. The method divides the search area into multiple area layers. Message transmission between area layers adopts a geographical routing manner, that is, messages are transmitted to the area layer closer to the ground station. The division of the search area changes the pattern of message transmission. Messages are delivered to the area layer closer to the destination node rather than the node closer to the destination. The pattern causes messages’ passing direction to be replaced by “point-to-point closer” to “point-to-face closer.” On the basis of message transmission at area layers, reasonable planning of UAVs’ distribution can effectively improve the network performance deterioration caused by a “hot spot.” Both analysis and experiments show that PB is superior to some existing methods in popularity balance of nodes and ping-pong effect. In addition, experiments also show that it gets better results in targets of delay, delivery rate, and hop count.

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A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

Cited by 34 publications

References 32 publications

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things

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

PB: A Message Transmission Method Based on Area Layer Division in UAV Networks

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