Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Maximizing network lifetime of broadcasting over wireless stationary ad hoc networks
Mobile Networks and Applications
INFOCOM'10 Proceedings of the 29th conference on Information communications
Minimum-hot-spot query trees for wireless sensor networks
Proceedings of the Ninth ACM International Workshop on Data Engineering for Wireless and Mobile Access
Lifetime Optimization by Load-Balanced and Energy Efficient Tree in Wireless Sensor Networks
Mobile Networks and Applications
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Wireless sensor networks are envisioned to be promising in gathering useful information from areas of interest. Due to the limited battery power of sensors, one critical issue in designing a wireless sensor network is to maximize its lifetime. Many efforts have been made to deal with this problem. However, most existing algorithms are not well optimized. In this paper, we investigate the maximum lifetime data gathering problem formally. We adopt tree structure as the basic routing scheme for our analysis, and propose a near optimal maximum lifetime data gathering and aggregation algorithm MLDGA. MLDGA tries to minimize the total energy consumption in each round as well as maximize the lifetime of a routing tree used in the round. Comparing with existing algorithms which are only efficient in some specified conditions, the simulation results show that our algorithm performs well regardless of the base station location and the initial battery energy levels of sensors.