Computational Intelligence in Wireless Sensor Networks: by Ajith Abraham, Rafael Falcon, Mario Koeppen

By Ajith Abraham, Rafael Falcon, Mario Koeppen

This booklet emphasizes the more and more very important position that Computational Intelligence (CI) equipment are enjoying in fixing a myriad of entangled instant Sensor Networks (WSN) comparable difficulties. The publication serves as a consultant for surveying a number of state of the art WSN eventualities within which CI ways were hired. The reader unearths during this booklet how CI has contributed to unravel quite a lot of demanding difficulties, starting from balancing the associated fee and accuracy of heterogeneous sensor deployments to improving from real-time sensor disasters to detecting assaults introduced by means of malicious sensor nodes and enacting CI-based protection schemes. community managers, specialists, academicians and practitioners alike (mostly in machine engineering, laptop technology or utilized arithmetic) make the most of th e spectrum of winning functions stated during this publication. Senior undergraduate or graduate scholars may well observe during this ebook a few difficulties compatible for his or her personal learn endeavors.

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Extra info for Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges

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Figure 4 shows the rate of noise as the available budget level drops for three sensors in the synthesized dataset introduced in Sect. 1. As A Genetic Programming Approach to Cost-Sensitive Control in … 19 Fig. 4 The noise rate for different sensors with different cost from synthesized dataset shown there, the cheapest sensor S1 becomes less noisy with decreasing budget, whereas the most expensive sensor S3 suffers a greater noise increase as the budget decreases. This noise model should encourage the selection of models that make use of less expensive sensors for predictions for two reasons.

Com L. jp F. edu © Springer International Publishing AG 2017 A. Abraham et al. 1007/978-3-319-47715-2_2 33 34 E. Spaho et al. 1 Introduction Wireless Mesh Networks (WMNs) [1, 2] are gaining a lot of attention because of their low cost nature that makes them attractive for providing wireless Internet connectivity. A WMN is dynamically self-organized and self-configured, with the nodes in the network automatically establishing and maintaining mesh connectivity among themselves (creating, in effect, an ad hoc network).

In the case of WMNs, the hierarchical approach 38 E. Spaho et al. is used due achieving network connectivity is considered more important than user coverage. It should be noted that due to this optimization priority, some client nodes may not be covered due the user coverage is less optimized. 4 Resolution Methods for Solving Nodes Placement Problem Given the complexity of node placement problems, most authors have proposed the use of simple heuristic methods or more advanced search methods such as Genetic Algorithms [6, 14, 16–21].

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