Maximizing Throughput in Wireless Multimedia Sensor Network using Soft Computing Techniques

Wireless Multimedia Sensor Networks (WMSN) provides valuable information for scalar data, images, audio, and video processing in monitoring and surveillance applications. Multimedia streaming, however, is highly challenging for networks as energy restriction sensor nodes limit the potential data transmission bandwidth and lead to reduced throughput. WMSN's two key design challenges, which can be achieved by the clustering process, are energy efficiency and throughput maximization. The use of the clustering technique helps to organise the sensor nodes into clusters, and between each cluster a cluster head (CH) will be chosen. This paper introduces a new Artificial Fish Swarm Optimization Algorithm (AFSA) with a Clustering Technique for Throughput Maximization in WMSN, called AFSA-HC, based on Hill Climbing (HC). The proposed AFSA-HC algorithm includes four key processes to optimise network throughput, namely node initialization, node clustering based on AFSA-HC, data aggregation based on the deflate algorithm, and transmission of hybrid data. To check the adequate performance of the presented AFSA-HC technique, a thorough experimental review will be carried out. The results of the simulation showed that the AFSA-HC approach achieved optimum results for various steps, namely energy consumption, throughput, network life, network stability and packet loss.