Titlebook: Computational Intelligence in Sensor Networks; Bijan Bihari Mishra,Satchidanand Dehuri,Himansu Da Book 2019 Springer-Verlag GmbH Germany,

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Wirtschaftswissenschaftliche Beitr?geption gives the right approach for key distribution on WSN without interacting the nodes in the network. Besides, we inspect several highly optimized, energy and memory efficient, and scalable variant of Elliptic curve cryptography that is quickly and flexible to integrate on WSN. Further, we examin

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https://doi.org/10.1007/978-3-662-13057-5ectrum sharing challenges are: Common Control Channel and Dynamic Radio Range. There are many other challenging issues still need additional investigations, thus, making Cognitive Radio an open research area. In this chapter, we present a detail survey on different spectrum sharing techniques in CRN

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Book 2019works, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the

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Computational Intelligence Techniques for Localization in Static and Dynamic Wireless Sensor Networodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optim

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Routing Protocols,s may differ depending on application or the sensor network architecture, but the main design criterion of any WSN will be to keep the nodes functioning as long as possible in order to enhance the network lifetime with a limited expenditure of energy. As clustering is by far the best approach for ef

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A Meta-heuristic Based Hybrid Predictive Model for Sensor Network Data, sensor network data. Here, FNN is trained using a hybrid BBATGSA algorithm for predicting temperature data in sensor network. The data is collected using 54 sensors in a controlled environment of Intel Berkeley Research lab. The developed predictive model is evaluated by comparing it with existing

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Routing Protocols in Wireless Sensor Networks,e flat networks routing protocols, the hierarchical networks routing protocols and the QoS aware routing protocols. The article explores the flat networks routing protocols as Re-active, Pro-active and Hybrid Protocols and hierarchical networks routing protocols as chain-based, grid-based, tree-base