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One of the fundamental design challenges in designing a Wireless Sensor Network (WSN) is to maximize the network lifetime,as each sensor node of the network is equipped with a limited power battery.To overcome this challenge,different methods were developed in the last few years using such techniques as network protocols,data fusion algorithms using low power,energy efficient routing,and locating optimal sink position.This paper focuses on finding the optimal sink position.Relay nodes are introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and,thus,their batteries are quickly depleted.A Particle Swarm Optimization (PSO) based algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient.The relay nodes communicate with the sink instead of the sensor nodes.Tests show that this approach can save at least 40% of the energy and prolong the network lifetime.
One of the fundamental design challenges in designing a Wireless Sensor Network (WSN) is to maximize the network lifetime, as each sensor node of the network is equipped with a limited power battery. To overcome this challenge, different methods were developed in the last few years using such techniques as network protocols, data fusion algorithms using low power, energy efficient routing, and locating optimal sink position. This paper focuses on finding the optimal sink position. Relay nodes are introduced in conjunction with the sensor nodes to mitigate network geometric deficiencies since in most other approaches the sensor nodes close to the sink become heavily involved in data forwarding and, thus, their batteries are quickly depleted. A Particle Swarm Optimization (PSO) based algorithm is used to locate the optimal sink position with respect to those relay nodes to make the network more energy efficient. the relay nodes communicate with the sink instead of the sensor nodes. Tests show that th is approach can save at least 40% of the energy and prolong the network lifetime.