ABSTRACT
Wireless sensor networks (WSNs) are systems of spatially distributed independent nodes used to perform various monitoring tasks. Several applications of WSN demand the improvement of the current protocols and specific parameters like lifetime of network, coverage, and energy consumption for routing. However, long communication distances between the sensor and sink nodes can greatly drain the energy of sensors and reduce the network lifespan. Therefore, we designed biomimicry techniques with the help of metaheuristic bio-inspired algorithms which apply the features of natural systems in communication systems to improve routing and efficient selection of cluster head and extend network lifespan in WSNs. In this chapter, three efficient, population-based, bio-inspired algorithms—particle swarm intelligence, genetic algorithm, and ant colony optimization—are used for selecting cluster heads and solving optimized routing problems like the Traveling Salesman Problem to achieve the shortest path for transmitting data in the sensing network. These algorithms simultaneously search within a set of candidate solutions and calculate their fitness value to select the best solutions in every iteration. In this comparative study, these bio-inspired algorithms were analyzed for obtaining optimal results in less processing time. The experiment has been carried out on these algorithms by tuning different parameters of these algorithms. Thus, we can greatly reduce the communication distance, thus maximizing network energy efficiency and prolonging network lifetime.
