Snapshot/Continuous Data Collection capacity for large-scale probabilistic Wireless Sensor Networks

Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most of the current works on the network capacity issue are based on the deterministic network model, which is not practical for real applications due to the “transitional region phenomenon” [22]. The probabilistic network model is actually a more practical one. In this paper, we investigate the achievable Snapshot/Continuous Data Collection (SDC/CDC) capacity for WSNs under the probabilistic network model. For SDC, we propose a novel Cell-based Multi-Path Scheduling (CMPS) algorithm, whose achievable network capacity is Ω(p_o/3ω · W) in the worst case and Ω(p_o/ω · W) in the average case, where p_o is the promising transmission threshold probability, ω is a constant, and W is the data transmitting rate over a wireless channel, i.e. the channel bandwidth, which are both order-optimal. For CDC, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the data collection process and achieves surprising network capacities for both the worst case and the average case. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works.