Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Optimal Energy-Efficient Routing for Wireless Sensor Networks
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
An Online Heuristic for Maximum Lifetime Routing in Wireless Sensor Networks
IEEE Transactions on Computers
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks
IEEE Transactions on Mobile Computing
General Network Lifetime and Cost Models for Evaluating Sensor Network Deployment Strategies
IEEE Transactions on Mobile Computing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Optimization of Distance-Based Location Management for PCS Networks
IEEE Transactions on Wireless Communications
A centralized energy-efficient routing protocol for wireless sensor networks
IEEE Communications Magazine
Energy efficiency analysis of a chain-based scheme via intra-grid for wireless sensor networks
Computer Communications
Self healing wireless sensor network
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
On the performance evaluation of query-based wireless sensor networks
Performance Evaluation
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Nodes in most wireless sensor networks (WSNs) are powered by batteries with limited energy. Prolonging network lifetime and saving energy are two critical issues for WSNs. Some energy-saving routing algorithms like minimum spanning tree based ones can reduce total energy consumption of a WSN, but they place too heavy burden of forwarding data packets on several key nodes so that these nodes quickly drain out available battery energy, making network lifetime shortened. In this paper, a routing algorithm termed Energy-efficient Routing Algorithm to Prolong Lifetime (ERAPL) is proposed, which is able to dramatically prolong network lifetime while efficiently expends energy. In the ERAPL, a data gathering sequence (DGS), used to avoid mutual transmission and loop transmission among nodes, is constructed, and each node proportionally transmits traffic to the links confined in the DGS. In addition, a mathematical programming model, in which minimal remaining energy of nodes and total energy consumption are included, is presented to optimize network lifetime. Moreover, genetic algorithms are used to find the optimal solution of the proposed programming problem. Further, simulation experiments are conducted to compare the ERAPL with some well-known routing algorithms and simulation results show the ERAPL outperforms them in terms of network lifetime.