Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
The impact of data aggregation on the performance of wireless sensor networks
Wireless Communications & Mobile Computing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
A centralized energy-efficient routing protocol for wireless sensor networks
IEEE Communications Magazine
Optimal cluster number selection in ad-hoc wireless sensor networks
WSEAS TRANSACTIONS on COMMUNICATIONS
WSEAS TRANSACTIONS on COMMUNICATIONS
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Information discovery in mission-critical wireless sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Energy-efficient query management scheme for a wireless sensor database system
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Anomaly detection in monitoring sensor data for preventive maintenance
Expert Systems with Applications: An International Journal
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
Lifetime Maximization in Wireless Sensor Networks
International Journal of Wireless Networks and Broadband Technologies
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Clustering has been well received as one of the effective solutions to enhance energy efficiency and scalability of large-scale wireless sensor networks. The goal of clustering is to identify a subset of nodes in a wireless sensor network, then all the other nodes communicate with the network sink via these selected nodes. However, many current clustering algorithms are tightly coupled with exact sensor locations derived through either triangulation methods or extra hardware such as GPS equipment. However, in practice, it is very difficult to know sensor location coordinates accurately due to various factors such as random deployment and low-power, low-cost sensing devices. Therefore, how to develop an adaptive clustering algorithm without relying on exact sensor location information is a very important yet challenging problem. In this paper, we try to address this problem by proposing a new adaptive clustering algorithm for energy efficiency of wireless sensor networks. Compared with other work having been done in this area, our proposed adaptive clustering algorithm is original because of its capability to infer the location information by mining wireless sensor energy data. Furthermore, based on the inferred location information and the remaining (residual) energy level of each node, the proposed clustering algorithm will dynamically change cluster heads for energy efficacy. Simulation results show that the proposed adaptive clustering algorithm is efficient and effective for energy saving in wireless sensor networks.