An Online Algorithm for Data Collection by Multiple Sinks in Wireless-Sensor Networks

Abstract

Data collection by multiple sinks is a fundamental problem in wireless-sensor networks. Existing works focused on designing the optimal offline methods provided that the number and positions of sensors and sinks (or trajectories of mobile sinks) are predetermined. This may not be practical, because although sensors are cheap, sinks are quite expensive in reality. A more practical scenario is that sinks are deployed step by step during the network operation due budget constraints, and we do not know the number, positions, and capacities of sinks a priori. In this paper, we investigate the problem of data collection with multiple sinks, and design a suboptimal online algorithm via a primal-dual approach, requiring very little priori knowledge. We theoretically derive the competitive ratio of the online algorithm, and further improve it by finding the optimal sink location with an approximation ratio. We also analyze the computational complexity of the improved approach. Extensive simulations are conducted to demonstrate the performance of the proposed online algorithm and performance-complexity tradeoff.