Improving the Quality of Service by Enhancing the Network Lifetime in Wireless Multimedia Sensor Networks

Abstract

Data has become paramount in the 21st century, from major contributors including social media, smartphones, individual health records, etc. Megabytes and gigabytes used to be the standard units of data storage, but nowadays Petta bytes and Zetta bytes of data are being produced at an exceptional rate and in a wide range of formats. Wireless sensor networks (WSN) are a type of WSN in which each node is equipped with sensing, processing, and communication capabilities. This network's ability to monitor and sense has led to its widespread use in fields as diverse as surveillance, military, medicine, and even the household. Networks that evolved from WSN to address problems peculiar to multimedia transmission are known as Wireless Multimedia Sensing Networks (WMSN). Video, audio, and visual content, as well as numerical data, can all be retrieved by the multimedia nodes. Every node in a WMSN has an energy limit. The network's lifespan is shortened because it's difficult to recharge or replace a dead battery. Transmission of video, music, images, and scalar data by wireless multimedia sensor nodes will increase their power consumption. In addition, the efficient operation of the network is contingent upon the following parameters being met during the transfer of multimedia data: a large bandwidth, high transmission of packets ratio, excellent throughput, acceptable end to end a delay, tolerable jitter, less frames loss rate, and low computation time. The sum of these measurements is the network's Quality of Service (QoS). A network's lifetime is defined as the amount of time it is able to function normally and carry out its designated function. To extend the useful life of multimedia networks, we devised a novel splitting of the underlying multidimensional functional structure and integrated it with Machine Learning techniques.