Energy optimization in wireless sensor network through natural science computing: A survey <s> Physical Layer <s> This paper presents an intelligent, dynamic power conservation scheme for sensor networks in which the sensor network operation is adaptive to both changes in the objects under measurement and the network itself. The conservation scheme switches sensor nodes between a sleep and an active mode in a manner such that the nodes can maximize the time they spend in a power-efficient sleep state, which corresponds to a nonmeasuring and/or nontransmitting mode, while not missing important events. A switching decision is made based on changes (or their absence) in the signals sensed from the environment by an intelligent agent that has been trained to determine whether or not a special event has occurred. This intelligent agent is based on a novel neural network topology that allows for a significant reduction in the resource consumption required for its training and operation without compromising its change detection performance. The scheme was implemented to control a sensor network built from a number of Telos rev. B motes currently available on the market. A few new utilities including an original neural network-based intelligent agent, a ldquovisualizer,rdquo a communication manager, and a scheduler have been designed, implemented, and tested. Power consumption measurements taken in a laboratory environment confirm that use of the designed system results in a significant extension of sensor network lifetime (versus ldquoalways onrdquo systems) from a few days to a few years. <s> BIB001 </s> Energy optimization in wireless sensor network through natural science computing: A survey <s> Physical Layer <s> This paper presents a dynamic model of wireless sensor networks (WSNs) and its application to a sensor node fault detection. Recurrent neural networks (RNNs) are used to model a sensor node, its dynamics, and interconnections with other sensor network nodes. The modeling approach is used for sensor node identification and fault detection. The input to the neural network is chosen to include delayed output samples of the modeling sensor node and the current and previous output samples of neighboring sensors. The model is based on a new structure of backpropagation-type neural network. The input to the neural network and topology of the network are based on a general nonlinear dynamic sensor model. A simulation example has demonstrated effectiveness of the proposed scheme. <s> BIB002 </s> Energy optimization in wireless sensor network through natural science computing: A survey <s> Physical Layer <s> A wireless ad hoc sensor network consists of a number of sensors spreading across a geographical area. The performance of the network suffers as the number of nodes grows, and a large sensor network quickly becomes difficult to manage. Thus, it is essential that the network be able to self-organize. Clustering is an efficient approach to simplify the network structure and to alleviate the scalability problem. One method to create clusters is to use weakly connected dominating sets (WCDSs). Finding the minimum WCDS in an arbitrary graph is an NP-complete problem. We propose a neural network model to find the minimum WCDS in a wireless sensor network. We present a directed convergence algorithm. The new algorithm outperforms the normal convergence algorithm both in efficiency and in the quality of solutions. Moreover, it is shown that the neural network is robust. We investigate the scalability of the neural network model by testing it on a range of sized graphs and on a range of transmission radii. Compared with Guha and Khuller's centralized algorithm, the proposed neural network with directed convergency achieves better results when the transmission radius is short, and equal performance when the transmission radius becomes larger. The parallel version of the neural network model takes time O(d), where d is the maximal degree in the graph corresponding to the sensor network, while the centralized algorithm takes O(n2). We also investigate the effect of the transmission radius on the size of WCDS. The results show that it is important to select a suitable transmission radius to make the network stable and to extend the lifespan of the network. The proposed model can be used on sink nodes in sensor networks, so that a sink node can inform the nodes to be a coordinator (clusterhead) in the WCDS obtained by the algorithm. Thus, the message overhead is O(M), where M is the size of the WCDS. <s> BIB003
Specifications tuning Processor voltage and sensing frequency  . The neural system BIB003 BIB001 BIB002 The Swarm -an ants  .