摘要：Sensor networks are a collection of sensor nodes spread in a geographical location. Nodes collect information and send this information to a sink node (access point). They usually form many-to-one sensor network where all traffic generated at sensors is destined for AP, thus a routing tree is formed. Scheduling is the process of deciding which node to send at a particular time. TDMA scheduling have been studied in terms of minimizing packet delay, improving fairness, maximizing parallel operation, minimizing the energy consumption, and shortening the total slots to finish a set of transmission tasks. Allowing the sensors to turn their radio off when not active is a common energy-saving strategy. Intelligent search algorithms were used to obtain an efficient scheduler In this work we look at the usage of 2 particular algorithms: evolutionary (EA) and particle swarm optimization (PSO). Then, we propose a hybrid algorithm that utilizes both EA and PSO algorithms using different optimization functions. The performance of the propped algorithm is evaluated using simulation. The obtained simulation results demonstrated that the PSO different optimization functions will give different fitness values and results.
