The Comparative Analysis Between LEACH and DEEC Based on The Number of Nodes and The Range of Coverage Area

Nugraha, Fazrial Amir; Sudiharto, Dodi Wisaksono; Karimah, Siti Amatullah

doi:10.1109/isemantic.2019.8884297

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…In order to verify the effectiveness of the algorithm for the lifetime of the sensor node, in this paper, MATLAB was used to simulate the low-energy adaptive clustering hierarchy (LEACH) [38][39][40][41][42], distributed energy efficient clustering (DEEC) [43][44][45][46], improved LEACH-centralized (LEACH-C) [47,48], localized game theoretical clustering algorithm (LGCA) [24,27], and our algorithm. The advantages of WSN sensor nodes in terms of lifetime, data transmission, and node survival state are then evaluated.…”

Section: Discussionmentioning

confidence: 99%

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Xiao

Huang

Gan

et al. 2022

Sensors

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Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime.

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Section: Discussionmentioning

confidence: 99%

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Xiao

Huang

Gan

et al. 2022

Sensors

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“…The initial idea has been expanded or applied to a far broader range of issues, and multiple methods have already been proposed based on various elements of ant behavior. Innovative plans are established using the ACO methods [31][32][33]. While existing approaches in their current incarnation may give satisfactory results in certain situations, they may perform poorly in others.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Gunigari¹,

Chitra²

2023

Intelligent Automation &Amp; Soft Computing

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Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network's lifespan. In the proposed system, a hybrid Energy Efficient and Reliable Ant Colony Optimization (ACO) based on the Routing protocol (E-RARP) and game theory-based energy-efficient clustering algorithm (GEC) were used. E-RARP is a new Energy Efficient, and Reliable ACO-based Routing Protocol for Wireless Sensor Networks. The suggested protocol provides communications dependability and high-quality channels of communication to improve energy. For wireless sensor networks, a game theory-based energy-efficient clustering technique (GEC) is used, in which each sensor node is treated as a player on the team. The sensor node can choose beneficial methods for itself, determined by the length of idle playback time in the active phase, and then decide whether or not to rest. The proposed E-RARP-GEC improves the network's lifetime and data transmission; it also takes a minimum amount of energy compared with the existing algorithms.

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“…The proposed method does improve the energy efficiency of the node, but it also increases the complexity of the algorithm. Considering the remaining energy of the node, DEEC proposed in [22] modified the probability of CHs to avoid the early death of the sensor node. In [14], when it comes to the selection of the CH, to make the load of the network more even, the distribution of the energy is considered.…”

Section: Routing Protocol Optimizationmentioning

confidence: 99%

Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks

Jiao

Tian

2020

Sensors

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In the wireless sensor network, the lifetime of the network can be prolonged by improving the efficiency of limited energy. Existing works achieve better energy utilization, either through node scheduling or routing optimization. In this paper, an efficient solution combining node scheduling with routing protocol optimization is proposed in order to improve the network lifetime. Firstly, to avoid the redundant coverage, a node scheduling scheme that is based on a genetic algorithm is proposed to find the minimum number of sensor nodes to monitor all target points. Subsequently, the algorithm prolongs the lifetime of the network through choosing redundant sleep nodes to replace the dead node. Based on the obtained minimum coverage set, a new routing protocol, named Improved-Distributed Energy-Efficient Clustering (I-DEEC), is proposed. When considering the energy and the distance of the sensor node to the sink, a new policy choosing the cluster head is proposed. To make the energy load more balanced, uneven clusters are constructed. Meanwhile, the data communication way of sensor nodes around the sink is also optimized. The simulation results show that the proposed sensor node scheduling algorithm can reduce the number of redundant sensor nodes, while the I-DEEC routing protocol can improve the energy efficiency of data transmission. The lifetime of the network is greatly extended.

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The Comparative Analysis Between LEACH and DEEC Based on The Number of Nodes and The Range of Coverage Area

Cited by 6 publications

References 6 publications

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks

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