A HYBRIDFITNESS-BASED ROUTING AND ENERGY CONSERVATION TECHNIQUE FOR WSN

Biological systems present remarkable adaptation, reliability, and robustness in various environments, even under hostility. Most of them are controlled by the individuals in a distributed and self-organized way. These biological mechanisms provide useful resources for designing the dynamical and adaptive routing schemes of wireless mobile sensor networks, in which the individual nodes should ideally operate without central control. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimization (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behavior and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This proposal proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilize AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this proposal presents an improved pheromone update method by considering energy levels and path length.