Intelligent Optimization of QoS in Wireless Sensor Networks Using Multiobjective Grey Wolf Optimization Algorithm

Abstract

With the advancement of technology and the emergence of new types of communication networks, new solutions have emerged to protect the environment and monitor natural resources. Wireless sensor networks (WSNs) have revolutionized environmental science and research by embedding sensors in environments where constant access and monitoring by manpower is difficult. WSNs have a variety of uses in the military, environmental monitoring, medicine, robotics, and so on. With the advent of applications in WSNs, the fundamental problem of the network has also increased. The performance of WSNs is influenced by various parameters that are varied according to the applications. In general, the performance of a WSN is typically specified through its average energy use, which determines the lifespan of the grid. A WSN should acquire the ability for controlling the overall performance of the network for ensuring the transmission of information based on quality of service (QoS) parameters in order to maximize the satisfaction of the services for the application. Therefore, we provided a multiobjective grey wolf optimization algorithm (QAMO-GWO) in order to optimize routing and improve QoS in WSNs. In the proposed method, sensor nodes receive information about the environment over regular periods of time and send it to the cluster heads in each. The selection of cluster heads in each cluster is done using a multiobjective grey wolf optimization algorithm. MO-GWO algorithm via balancing QoS parameters tries for selecting the optimal cluster heads. Finally, simulation outputs showed that the proposed method has been able to improve QoS criteria due to balancing the goals in the network.

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

“…However, efficient power routing communication is also not enough. The researcher also intends to maximize the satisfaction of the services for the IoT WSN application [248], which is QoS (Quality of Service). Therefore, energy optimization and QoS maximization Computational Intelligence is still one of the biggest issues in IoT WSN applications.…”

Recent Advances and Future Prospects of Using AI Solutions for Security, Fault Tolerance, and QoS Challenges in WSNs

“…In order to enhance routing and maximize QoS in WSNs, the study in [157] proposes a multi-objective GWO (called QAMO-GWO). Nodes gather environment data periodically and deliver it to the respective CHs.…”

“…In [157], multiobjective grey wolf optimization algorithm (MO-GWO) balances the QoS parameters and focuses on selecting the optimal CHs. Simulation results show that MO-GWO has been able to improve QoS criteria by balancing the goals in the network.…”

English Classroom Teaching Evaluation System Based on Particle Swarm Optimization Algorithm

“…The method uses the fitness function of the swarm particle enrichment algorithm to select the optimal cluster head according to the quality of the working face. Simulation results show that compared with other methods, this method has lower power consumption and longer network lifetime due to the balanced QoS requirements [11]. Abolhoseini S modified PSO to fix internal site issues.…”