Energy Balanced Threshold Using Game Theory Algorithm for Wireless Sensor Networks Optimization

Hendrarini, Nina; Asvial, Muhamad; Sari, Riri Fitri

doi:10.1145/3378936.3378952

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…In [55] the author presented an improvement to the distributed energy-efficient clustering (DEEC) approach by utilizing the threshold game theory algorithm (TGDEEC), in order to create an energy-balanced and optimized WSN. Theory is a calculation based on mathematical that simplifies problems involving accord.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

Preethi R

2023

IJRITCC

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In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

Preethi R

2023

IJRITCC

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“…Distributed Energy-Efficient Clustering (DEEC) optimizes WSN and better energy balancing achieved by [50]. A game theory inspired Threshold Game Theory Algorithm based DEEC considered weighted CH and member energy consumption for threshold [51]. Whale Optimization Algorithm optimally search the nodes with highest fitness value based on hunting behavior of humpback whales for CH election [52].…”

Section: Pure Heterogeneous Wsn (Hwsn) In Terms Of Different Level Of...mentioning

confidence: 99%

PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things

Sharma,

Chawla

2023

Wireless Pers Commun

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A metaheuristic approach based hybrid data routing algorithm is proposed in this paper for energy-efficient wireless sensor network (WSN) applications in Internet of Things (IoT). High speed generation of network demands of more IoT enabled services and platforms. Swarm optimization in route finding is integrated with energy-efficient heuristic method to obtain improved data routing algorithm for heterogeneous WSN. Particle swarm optimized Residual Energy based Stable Election Protocol (PRESEP) simulation yields multi objective solutions for use of residual node energy, prolonged network lifetime and stable cluster formation. Protocol minimizes the cluster head (CH) selection cycle as an outcome of reactive protocol benefits of easy global search of swarm optimization for sustainable data routing in energy-efficient cluster based heterogeneous WSN. Scaling of network is supported by the algorithm with varied heterogeneity factor. Support for heterogeneous WSN with various network sizes are evaluated and found it outperformed previous heterogeneous algorithm in terms of network lifetime, alive nodes, reduced energy consumption with minimum number of repeated CH selection process.

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“…To have an energy-balanced and optimized WSN, the authors in [98] proposed an enhancement to the distributed energy-efficient clustering (DEEC) method by using the threshold game theory algorithm (TGDEEC). Game theory is a mathematical computation that facilitates decisionmaking scenarios.…”

Section: Single Hop Data Transmissionmentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

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Energy Balanced Threshold Using Game Theory Algorithm for Wireless Sensor Networks Optimization

Cited by 7 publications

References 9 publications

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

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