Smart data collection in large scale sparse WSNs

Abstract

Mobile wireless sensor networks (MWSNs), a special class of WSN in which one or more elements of the network is mobile, has been extensively studied over the past 15 years. In MWSNs, mobility plays an important role in improving the performance of the network in terms of connectivity, lifetime, and data collection. We exploit controlled mobility to develop a smart data collection scheme in MWSNs. We consider a sensor network where static nodes are deployed and organized into clusters on the basis of distance from each other. These nodes sense their environment and transmit data to their corresponding cluster heads, which in turn send it to a mobile data collector (MDC) when it is in communication range. In this paper, we first show the factors affecting the data collection process of MDC in such an environment. We then present an adaptive algorithm and control parameters that the MDC uses for autonomously controlling its motion. These parameters allow the speed of the MDC to be adjusted at run time in order to adaptively improve the data collection process. Built-in intelligence helps our system adapting to the changing requirements of data collection. Our scheme shows significant advantages for sparsely deployed, large scale sensor networks and heterogeneous networks (where sensors have variable sampling rates). The simulation results show a significant increase in data collection rate and reduction in the overall time and number of laps that the MDC spends for data gathering.