CATS

Abstract

Data sharing among multiple sampling tasks significantly reduces energy consumption and communication cost in low-power wireless sensor networks (WSNs). Conventional proposals have already scheduled the discrete point sampling tasks to decrease the amount of sampled data. However, less effort has been expended for applications that generate continuous interval sampling tasks. Moreover, most pioneering work limits its view to schedule sampling intervals of tasks on a single sensor node and neglects the process of task allocation in WSNs. Therefore, the gained efforts in prior work cannot benefit a large-scale WSN because the performance of a scheduling method is sensitive to the strategy of task allocation. Broadening the scope to an entire network, this article is the first work to maximize data sharing among continuous interval sampling tasks by jointly optimizing task allocation and scheduling of sampling intervals in WSNs. First, we formalize the joint optimization problem and prove it NP-hard. Second, we present the COMBINE operation, which is the crucial ingredient of our solution. COMBINE is a 2-factor approximate algorithm for maximizing data sharing among overlapping tasks. Furthermore, our heuristic named CATS is proposed. CATS is 2-factor approximate algorithm for jointly allocating tasks and scheduling sampling intervals so as to maximize data sharing in the entire network. Extensive empirical study is conducted on a testbed of 50 sensor nodes to evaluate the effectiveness of our methods. In addition, the scalability of our methods is verified by utilizing TOSSIM, a widely used simulation tool. The experimental results indicate that our methods successfully reduce the volume of sampled data and decrease energy consumption significantly.