Energy optimization in wireless sensor network through natural science computing: A survey <s> The Human organ components for WSN application <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> The Human organ components for WSN application <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> The Human organ components for WSN application <s> The topology control is a very important issue in wireless sensor networks (WSNs). Many approaches have been proposed to carry out in this aspect, including modern heuristic approach. In this paper, the Topology Control based on Artificial Immune Algorithm (ToCAIA) is proposed to solute the energy-aware topology control for WSNs. ToCAIA is a heuristic algorithm, which is heuristic from the immune system of human. In ToCAIA, the antibody is the solution of the problem, and the antigen is the problem. ToCAIA could be used to solve the multi-objective minimum energy network connectivity (MENC) problem, and get the approximate solution. The experiment result shows that the topology control by using ToCAIA can be utilized for WSNs network optimization purposes. <s> BIB003 </s> Energy optimization in wireless sensor network through natural science computing: A survey <s> The Human organ components for WSN application <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> BIB004 </s> Energy optimization in wireless sensor network through natural science computing: A survey <s> The Human organ components for WSN application <s> Sensor nodes in a Wireless Sensor Network (WSN) are responsible for sensing the environment and propagating the collected data in the network. The communication between sensor nodes may fail due to different factors, such as hardware failures, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem has been inspired by Gene Regulatory Networks (GRNs). Owing to millions of years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this paper, we exploit real biological gene structures to deploy wireless sensor networks, called bio-inspired WSNs. Exhaustive structural analysis of the network and experimental results demonstrate that the topology of bio-inspired WSNs is robust, energy-efficient, and resilient to node and link failures. <s> BIB005
The components of natural science i.e. human body organ are selected for modelling solutions to wireless sensor networks concern to energy optimization by inheriting their working strategy as shown in Table 1 . The distinguished features are passed from parents to offspring through gene transmission. Genes are located on chromosomes and consist of DNA. They are passed from parent to offspring through reproduction. BIB005 Azad nazi and et.l. designed the biological gene structure to design wireless sensor networks. The concept Gene Regulatory Network (GRN) of the real living organisms which gives information about intrinsic properties of adaptation and robustness which have used for creating dynamic network environments. The researcher use the strength of the robust GRN topology as a template to design communication structure of the WSN. The nervous system of human consists of the brain, spinal cord, and a complex network of neurons. This system is responsible for sending, receiving, and interpreting information from all parts of the body. The nervous system monitors and coordinates internal organ function and responds to changes in the external environment. BIB004 H. He, Z. Zhu et. l. proposed a neural network model to find the minimum Weakly Connected Dominating Set in a wireless sensor network. The proposal presents a directed convergence and proves Immune system The protection mechanism Intelligent Routing is based on optimized data Optimum time for topology transitions the scalability of the neural network model by testing it on a range of sized graphs and on a range of transmission radius. Another BIB001 BIB002 intelligent agent based scheme on a neural network topology which do significant reduction in the energy resource consumption required for training and operation of nodes without compromising its change detection performance. TheAzzam I. M. et.l. use neural network approach for recurrent neural network to model sensor nodes and their dynamics. The topology is updated based on neighbouring sensors information. A cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off. The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighbourhood is defined relative to the specified cell. An initial state is selected by assigning a state for each cell. A new generation is created, according to some fixed rule that determines the new state of each cell in terms of the current state of the cell and the states of the cells in its neighbourhood. Typically, the rule for updating the state of cells is the same for each cell and does not change over time, and is applied to the whole grid simultaneously, though exceptions are known, such as the probabilistic cellular automata and asynchronous cellular automaton. The synthetic cellular automation provided by Stavros A. & et. l.  is decentralized computing model provide complex computation in wireless sensor network using local information. The proposal assumes Moors neighbourhoodof eight surrounding nodes. The method  combines local evolutionary design rules with finite element analysis. The design rule incorporated for minimizing error between local mechanical signal and its optimum price by different control strategies. Within an organism the system that protects against disease are known to the immune system, it's a system of biological structures and processes within. An immune system is used to detects a wide variety of agents from viruses and parasitic worms and distinguish them from the organism's own healthy tissue for functioning organ properly. The heuristics method BIB003 inspired from immune system where antibodies are decided as solutions to the problem and antigen is the problem. It tries to justify the multi-objective minimum energy network connectivity problems which optimize the energy. In another model  the range for selected nodes is decided and affinity rate is evaluated. Based on this evaluation, selected node declared as memory cell and the links of other nodes are mutated. The process continues until the number of iterations exceeds to threshold rate or the affinity rate of all nodes become good enough than threshold rate.