Applying Swarm Intelligence Algorithms In Wireless Sensor Networks Energy Optimization

Wireless Sensor Networks (WSNs) is one of the emerging technologies at the present time. This is due to its potential applications in surveillance, environment and habitat monitoring, structural monitoring, healthcare, and disaster management. One of the prominent challenges that face the researchers recently, is the extending of the wireless network’s lifetime.Clustering in WSNs is one of the techniques used to manage energy consumption efficiently through data aggregation at the cluster head. Clustering scheme reduces the communication overheads, thereby decreasing the energy consumptions and interfaces among sensor nodes. It is proposed to WSNs because of its network scalability, energy-saving attributes and network topology stability.WSNs energy-aware clustering is often formulated as an optimization problem. For this reason, an improved Swarm Intelligence Optimization Algorithm is proposed. In this thesis, an Improved Many Optimizing Liaisons (IMOL) algorithm is proposed to optimize the wireless network clustering in order to extend the network lifetime. Simulation results demonstrate that the used cluster-based protocol using IMOL algorithm has higher efficiency and can achieve better network lifetime over its comparatives.