Hybrid Optimization Model for Energy Efficient Cloud Assisted Wireless Sensor Network

The role of wireless sensor networks is ubiquitous in the present era. The dependency of wireless sensor networks is inevitable for small scale to large scale applications due to its compact, reliable, and efficient processing capabilities. However, wireless sensor network has its few limitations. Since the network is created by deploying sensor nodes and it requires efficient energy management procedures. Localization of nodes is an important process that should be considered in wireless sensor networks which directly relates the energy management. To reduce the node localization issues in wireless sensor networks, this research work proposed a hybrid optimization model using Particle Swarm Optimization and Grey Wolf Optimization as a combined approach. The proposed model effectively handles the node localization issues. To reduce the data processing and storage issues in wireless sensor networks, Cloud module is incorporated in the proposed model which improves the energy management features. Similarly, to transfer the data from node to cloud, hybrid optimization model shortest path discovery process is utilized. This combined approach reduces the packet loss, avoids route failures, improves network reliability, and lifetime compared to conventional models such as ant colony optimization.