Efficient configuration for multi-objective QoS optimization in wireless sensor network

Abstract

Wireless sensor network (WSN) is a self-organizing network consisting of many sensor nodes situated in the monitoring area. One of the most important challenges of WSNs is to configure the network in a way to improve the quality-of-service parameters, and thus enhance the network performance and reduce energy consumption. Energy consumption increases due to several reasons such as unsuccessful delivery of packets to the receiver, re-transmission of packets, delay in packet delivery to the sink, low received signal strength, inadequate link quality, noise level, etc. Each sensor network consists of different configuration parameters including the distance between receiver and transmitter, packet arrival time, packet size, maximum queue size, maximum number of transmissions, transmission power level, etc. All the mentioned parameters will affect several indicators such as latency, received signal strength, link quality, successful/fail packet delivery, noise level, and the number of re-transmissions. Finding the relationship between these two sets of parameters and optimizing them reduce energy consumption in the network and increase network lifetime. To achieve this, an efficient and new method based on support vector regression and genetic algorithm called MSOG is presented in this paper to model and optimize wireless sensor network indicators (such as delay, received signal strength, packet arrival time, successful packet delivery). The simulation results show that MSOG has better outcomes in optimizing network parameters and indicators in comparison with a similar algorithm.