Design and performance analysis of energy efficient technique for wireless multimedia sensor networks using machine learning algorithm

Wireless multimedia sensor networks (WMSNs) are developed from wireless sensor networks (WSNs) for acquiring and transmitting multimedia data such as images, audio and video streams and scalar data. Energy is the most critical factor in sensor networks. Its power requirement is satisfied by low capacity and low power battery. Reduction of communicated multimedia volume is an important step to reduce energy consumption in WMSNs because of the relatively huge amount of data collected by the nodes compared with scalar sensors. One of the algorithms in machine learning which can reduce the dimensionality is unsupervised learning Self Organizing Map (SOM) which typically performs dimensionality reduction through pattern clustering. In this work, an attempt has been made to reduce the amount of transmitted information in WMSNs using SOM in order to increase the lifetime of the network. The proposed design has shown an increase of 158.03 % in network lifetime compared to JPEG.