Optimizing the Path-Points Identification for Data Mules in Mobile WSNs

Abstract

To alleviate the data collection latency problem in mobile WSNs, we shorten the data collection path by visiting a minimal set of points in the network, which we call the {\it Stop Point Set} (SPS). A point selection method named path-points identification method has been proposed recently, which plays the same role as our SPS calculating method. However, as a clustering based method, it may not scale well when the WSN is large. Furthermore, there exists some uncertainty in its clustering implementation. In this paper, we compare the path-points identification method with our stop point selection method, and verifies our prediction about its scalability and uncertainty. We also proposed one feasible modification to improve its performance. Our work is evaluated by comprehensive simulations. The results show that our stop point selection method outperforms the path-points identification method both in terms of the size of resulting SPS and the length of data collection path.