Double-Blind Data Discovery Using Double Cross for Large-Scale Wireless Sensor Networks With Mobile Sinks

Large-scale wireless sensor networks (WSNs) with mobile sinks present new challenges on data discovery because the locations of the mobile sinks cannot be predetermined. In this paper, we study the double-blind data discovery problem in a WSN, where the mobile sink(s) and the sensed data do not know the locations of each other a priori. To address this problem, we first propose a Random Line Walk (RLW) mechanism for message forwarding, and based on this forwarding mechanism, we further propose an efficient data discovery mechanism called Double Cross. Double Cross exploits a simple geometric property of a planar, i.e., for a couple of pairs of orthogonal lines in a planar, the probability that they intersect within the planar is larger than 99%. However, it does not depend on the geographic location or directional information on a node, which is difficult to obtain in such networks. Instead, each sensor node only needs to know the distances between the neighbor nodes within its transmission range. Analytical and simulation results show that Double Cross can achieve a higher successful discovery rate with lower energy consumption, compared existing work.