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An adaptive energy efficient scheme for energy constrained wireless sensor networks

One of the most promising technologies of 21st century is the wireless sensor network (WSN). Due to its self-organizing, low cost, low power and sensing capabilities, it has been widely used in many domains such as transportation system, agriculture, medical care, surveillance, Internet of things (IoT) and many more. The use of sensors in IoT is getting bigger attention from researchers and industrialists, as people are using smart devices along with sensors to capture data and get connected round the clock anywhere they want. As these networks are energy constrained, so it is important to use the battery wisely and effectively. The IoT is mainly dependent on sensors and these devices have scare resources. Therefore, it is essential to prolong network lifetime by adopting energy efficient schemes. In this paper, we propose an adaptive energy efficient scheme for energy constrained networks such as WSN and IoT. This work is an extension to our previous work presented in [1]. The objective of this work is to make the scheme adaptive so that the parameters such as grid dimensions, lower bound, upper bound and inter bound gap are set according to the input parameters which are node density and network deployment area. The performance evaluation of the proposed scheme is carried in two ways. One is to check the mean squared error (MSE) by considering merge and split technique and normal distribution (without merge and split). Secondly, extensive experiments were carried out to make the scheme adaptive by considering different parameters and its impact on network lifetime. Results shows that the proposed merge and split technique has minimum MSE as compared to normal distribution (without merge and split) by considering different parameters such as grid dimension, lower bound (LB) and upper bound (UB). Minimum the value of MSE less will be the error thus resulting in better performance. Different values of LB, UB and grid dimension are suggested for input parameters (node density and network deployment area).