Time-Sensitive Ant Colony Optimization To Schedule a Mobile Sink for Data Collection in Wireless Sensor Networks

Abstract

In Wireless Sensor Networks, sensor nodes are deployed to monitor and record the changes in their surroundings. The collected data in the sensor memories is transferred to a remote central via static or mobile sinks. Because sensors have scarce memory capacity various challenges occur in gathering the data from the environment and transferring them to the remote control. For instance, a sensor's memory might get completely full with the sensed data if the sensor can not transfer them on time. Then, a memory overflow happens which causes all the collected data to be erased to free the memory for future readings. Therefore, when a mobile sink (MS) is employed to collect data from the sensors, the MS has to visit each sensor before any memory overflow takes place. In this paper, we study the design of a mobile sink scheduling algorithm based on the Ant Colony Optimization (ACO) meta-heuristic to address this specific issue. The proposed scheduling algorithm, called Mobile Element Scheduling with Time Sensitive ACO (MES/TSACO), aims to prepare a schedule for a mobile sink to visit sensors such that the number of memory overflow incidents is reduced and the amount of collected data is increased. To test and compare the effectiveness of the MES/TSACO approach, the Minimum Weighted Sum First (MWSF) heuristic is implemented as an alternative solution. The results obtained from the extensive simulation tests show that the MES/TSACO generates schedules with considerably reduced number of overflow incidents and increased amount of collected data compared to the MWSF heuristic.