Optimizing cloudlet scheduling and wireless sensor localization using computational intelligence techniques
Hussein S. Al-Olimat · OhioLink ETD Center (Ohio Library and Information Network) · 2014
Optimization algorithms are truly complex procedures that consider many elements when optimizing a specific problem.Cloud computing (CCom) and Wireless sensor networks (WSNs) are full of optimization problems that need to be solved.One of the main problems of using the clouds is the underutilization of the reserved resources, which causes longer makespans and higher usage costs.Also, the optimization of sensor nodes' power consumption, in WSNs, is very critical due to the fact that sensor nodes are small in size and have constrained resources in terms of power/energy, connectivity, and computational power.This thesis formulates the concern on how CCom systems and WSNs can take advantage of the computational intelligent techniques using single-or multi-objective particle swarm optimization (SOPSO or MOPSO), with an overall aim of concurrently minimizing makespans, localization time, energy consumption during localization, and maximizing the number of nodes fully localized.The cloudlet scheduling method is implemented inside CloudSim advancing the work of the broker, which was able to maximize the resource utilization and minimize the makespan demonstrating improvements of 58% in some cases.Additionally, the localization method optimized the power consumption during a Trilateration-based localization (TBL) procedure, through the adjustment of sensor nodes' output power levels.Finally, a parameteriii I would like to first thank my brother Dr. Khalid Al-Olimat and his wife Dr. Feng Jao.