A Study on Lifetime Enhancement of LR WPAN using Mobile Data Collection Agent

Abstract

Escalation in the real world applications using the Internet of Things (IoT) now demands low cost newlineand minimum power consuming devices having sensing, communicating, and computing capabilities. newlineLow-rateWireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these newlinecapabilities with low data rate and less complexity. The longevity and connectivity of the network is newlinea critical issue during the data collection in LR-WPAN. Due to the resource constraints in low power newlinewireless devices, it is very essential to design energy efficient data collection strategies to improve the newlinenetwork lifetime in LR-WPAN. The data collection point (Sink node) can be stationary, but it will create newlinehotspot problem in the network. To mitigate the hotspot problem, different strategies for data collection newlinewith the support of Mobile Sink (MS) (also known as Mobile Data Collection Agent (MDCA)) is newlineproposed in this research work. newlineThe main focus of all the algorithms is to find the optimal set of clusters and to find the data collection newlinepoints to be visited by MDCA. These spots are known as Rendezvous points (RPs). Three methods newlinenamely Min-Max, Max-Min, and Cluster based RP Identification (C-RPI) is proposed to select the RPs newlineand to find the path of MS. In Max-Min algorithm, the position of node with maximum neighbours is newlinechosen as the first RP by the MS. After removing the node and their neighbours from the list, next node newlinehaving most number of neighbours is chosen as the second RP and so on. Min-Max algorithm works in newlinereverse order, in which the nodes having the least number of neighbours are selected as the first RP. This newlineprocess is repeated after removing the selected RPs and its neighbours from the list. C-RPI is a range newlinebased approach for forming rectangular clusters of devices. The centre of each rectangular cluster region newlineis taken as an RP. After selecting the RPs, Travelling Sales Person (TSP) method is applied to select the newlineoptimal path of the MS. newlineAn energy efficient two level clustering scheme with an optimal.