An optimal data aggregation technique for physics-based applications

Abstract

In wireless sensor network (WSN), most of the devices function on batteries. These nodes or devices have inadequate amount of initial energy which are consumed at diverse rates, based on the power level and intended receiver. In sleep scheduling algorithms, most of the sensor nodes are turned to sleep state to preserve energy and improve the network lifetime (NL). In this paper, an energy-efficient dynamic cluster-based protocol is proposed for WSN especially for physics-based applications. Initially, the network is divided into small clusters using adaptive clustering. The clusters are managed by the cluster heads. The cluster heads are elected based on the novel dynamic threshold. Afterwards, general variable neighborhood search is used to obtain the energy-efficient paths for inter-cluster data aggregation which is used to communicate with the sink. The performance of the proposed method is compared with competitive energy-efficient routing protocols in terms of various factors such as stable period, NL, packets sent to base station and packets sent to cluster head. Extensive experiments prove that the proposed protocol provides higher NL than the existing protocols.

Quantum Particle Swarm Optimization and Compressive Sensing-Based Clustering Protocol for Wireless Sensor Networks

“…WSNs determine the environmentally situation such as range of temperature, noise, degree of air pollution, moisture, wind flow, and so on. This kinds of act like wireless ad hoc network meaning which they depend upon wireless connection as well as natural development of networks so that sensor information may be sent wirelessly [7]. In some cases, these are known as dust network; mention to moment sensors no more than dust.…”