Scheduling data collection with dynamic traffic patterns in wireless sensor networks

Abstract

The network traffic pattern of continuous sensor data collection often changes constantly over time due to the exploitation of temporal and spatial data correlations as well as the nature of condition-based monitoring applications. This paper develops a novel TDMA schedule that is capable of efficiently collecting sensor data for any network traffic pattern and is thus well suited to continuous data collection with dynamic traffic patterns. Following this schedule, the energy consumed by sensor nodes for any traffic pattern is very close to the minimum required by their workloads given in the traffic pattern. The schedule also allows the base station to conclude data collection as early as possible according to the traffic load, thereby reducing the latency of data collection. Experimental results using real-world data traces show that, compared with existing schedules that are targeted on a fixed traffic pattern, our proposed schedule significantly improves the energy efficiency and time efficiency of sensor data collection with dynamic traffic patterns.