An Efficient Optimization Technique for Scheduling in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) use a large number of tiny sensor devices for monitoring, gathering and processing data with low hardware complexity, low energy consumption, high network lifetime, scalability, and real-time support. Sensor node deployment, coverage, task allocation, and energy efficiency are the main constraints in WSNs that impact the node lifetime. Scheduling allows the platform to improve the performance of WSN. Scheduling the sensor nodes and category of sensor data minimizes the energy consumption and increases the lifetime of sensor nodes. This chapter describes the concept of optimization techniques in WSNs to extend performance. We surveyed four metaheuristic optimization approaches to enhance the scheduling performance because these approaches help to find optimal solutions quickly. Optimizing the sensor node placement through scheduling of sensor and sensor data allows a better quality of service (QoS) in WSN. In this chapter, we survey such optimization techniques as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and artificial bee colony (ABC) for scheduling methods.
KeywordsWSNs Optimization techniques Energy consumption Network lifetime
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