Approximate Data Collection for Wireless Sensor Networks

Abstract

Data collection is a fundamental issue in wireless sensor networks. In many application scenarios for sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with given error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform a global approximate data collection on the sink according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of several subsets of sensor nodes, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and use a greedy heuristic algorithm to find an approximate solution. We also propose a monitoring algorithm to adjust these subsets according to the changes of sensor readings. Our trace-driving simulation results show that our data collection approach ADC can notably reduce the communication cost with given error bounds.