Wireless Sensor Networks Routing Using Clustering Based on Multi-Objective Particle Swarm Optimization Algorithm

Subject Areas : Renewable energy

Seyed Reza Nabavi ¹ , Nafiseh Osati Eraghi ² , Javad Akbari Torkestani ³

1 - Department of Computer Engineering- Arak Branch, Islamic Azad University, Arak, Iran
2 - Department of Computer Engineering- Arak Branch, Islamic Azad University, Arak, Iran
3 - Department of Computer Engineering- Arak Branch, Islamic Azad University, Arak, Iran

Received: 2020-12-22 Accepted : 2021-03-14 Published : 2021-11-22

Keywords: Wireless Sensor Networks, Multi-Objective Particle Swarm Optimization Algorithm, Energy-Aware Routing,

Abstract :

With the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches.

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