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An optimum selective transmission scheme for energy-limited sensor networks, where sensors send or forward messages of different importance (priority), is developed. Considering the energy costs, the available battery, the message importances and their statistical distribution, sensors decide whether to transmit or discard a message so that the importance sum of the effectively transmitted messages...
The transmission and reception of sensor measures between nodes in distributed target tracking applications of wireless sensor networks is energy expensive. This paper shows that a selective transmission policy can be used to increase the network lifetime without reducing the accuracy of the target parameter (position, velocity) estimates in a significant manner. To do so, nodes compute an importance...
Unpredictable topology changes, energy constraints and link unreliability make the information transmission a challenging problem in wireless sensor networks (WSN). Taking some ideas from machine learning methods, we propose a novel geographic routing algorithm for WSN, named Q-probabilistic routing (Q-PR), that makes intelligent routing decisions from the delayed reward of previous actions and the...
Energy is a valuable resource in wireless sensor networks since it constitutes a limiting factor for the network lifetime. In order to make an efficient use of its own energy resources, each node in the network should be aware of the energy resources at other nodes, which can be relevant to the success of their routing decisions. The proposal of this paper is twofold: (i) to design a routing algorithm...
In this paper we propose an efficient energy-aware routing algorithm based on learning patterns. Energy and message importance are considered in a Bayesian model in order to establish intelligent decision rules that make the network economize in crucial resources.
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