Studies conducted on resource management in wireless sensor networks have identified energy efficient routing protocols as one of the energy saving mechanisms that can be used to manage the consumption of networks’ available energy and extend network lifetime. Routing protocols assist in finding paths for transmission of sensed events, and they must be able to extend the lifetime of a network despite some of the limitations of sensor nodes in a network and the harsh environments in which the sensor nodes are to operate. In this paper, we survey and compare existing routing protocols in wireless sensor networks. We start by introducing the different solutions that can be used to improve the network lifetime and focus on energy efficient routing protocols as the area of the survey, in addition to network topology modeling. We also model the network regarding energy consumption, sensing and event extraction analysis in the network. Categorization of the routing protocols into homogeneous and heterogeneous was performed, for which, sub-classification into static and mobile and other behavioral patterns of the routing protocols was done. The second phase of the paper presents models and simulations of selected routing protocols and comparisons of their performances. We conclude this paper by discussing future work directions with highlights on some futuristic applications.
Social insect communities are formed from simple, autonomous, and cooperative organisms that are interdependent for their survival. These communities are able to effectively coordinate themselves to achieve global objectives despite a lack of centralized planning, and the behaviour is referred to as swarm intelligence. This chapter presents a study of communication protocols for wireless sensor networks utilizing nature-inspired systems: social insect-based communities and natural creatures. Three types of insects are used for discussion: ants, termites, and bees. In addition, a study of the social foraging behavior of spider monkeys is presented. The performances of these swarm-intelligence-based algorithms were tested on common routing scenarios. The results were compared with other routing algorithms with varying network density and showed that swarm-intelligence-based routing techniques improved on network energy consumption with a control over best-effort service. The results were strengthened with a model of termite-hill routing algorithm for WSN.
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