Issue No. 02 - February (2010 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2009.45
Yanwei Wu , Dept. of Comput. Sci., Minnesota State Univ., Mankato, MN, USA
Xiang-Yang Li , Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA
YunHao Liu , Dept. of Comput. Sci., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
Wei Lou , Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
A sensor in wireless sensor networks (WSNs) periodically produces data as it monitors its vicinity. The basic operation in such a network is the systematic gathering (with or without in-network aggregation) and transmitting of sensed data to a base station for further processing. A key challenging question in WSNs is to schedule nodes' activities to reduce energy consumption. In this paper, we focus on designing energy-efficient protocols for low-data-rate WSNs, where sensors consume different energy in different radio states (transmitting, receiving, listening, sleeping, and being idle) and also consume energy for state transition. We use TDMA as the MAC layer protocol and schedule the sensor nodes with consecutive time slots at different radio states while reducing the number of state transitions. We prove that the energy consumption by our scheduling for homogeneous network is at most twice of the optimum and the timespan of our scheduling is at most a constant times of the optimum. The energy consumption by our scheduling for heterogeneous network is at most ¿ (log Rmax/Rmin) times of the optimum. We also propose effective algorithms to construct data gathering tree such that the energy consumption and the network throughput is within a constant factor of the optimum. Extensive simulation studies show that our algorithms do considerably reduce energy consumption.
wireless sensor networks, access protocols, scheduling, time division multiple access
Yanwei Wu, Xiang-Yang Li, YunHao Liu and Wei Lou, "Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation," in IEEE Transactions on Parallel & Distributed Systems, vol. 21, no. 2, pp. 275-287, 2010.