Issue No. 04 - April (2011 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2010.193
Shuai Gao , Beijing Jiaotong University, Beijing
Hongke Zhang , Beijing Jiaotong University, Beijing
Sajal K. Das , University of Texas at Arlington, Arlington
Recent work has shown that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. However, due to the path constraint, a mobile sink with constant speed has limited communication time to collect data from the sensor nodes deployed randomly. This poses significant challenges in jointly improving the amount of data collected and reducing the energy consumption. To address this issue, we propose a novel data collection scheme, called the Maximum Amount Shortest Path (MASP), that increases network throughput as well as conserves energy by optimizing the assignment of sensor nodes. MASP is formulated as an integer linear programming problem and then solved with the help of a genetic algorithm. A two-phase communication protocol based on zone partition is designed to implement the MASP scheme. We also develop a practical distributed approximate algorithm to solve the MASP problem. In addition, the impact of different overlapping time partition methods is studied. The proposed algorithms and protocols are validated through simulation experiments using OMNET++.
Sensor networks, mobile sinks, path constraint, data collection, energy utilization efficiency.
H. Zhang, S. Gao and S. K. Das, "Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks," in IEEE Transactions on Mobile Computing, vol. 10, no. , pp. 592-608, 2010.