Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


UThesis_Al-Olimat.pdf (1.1 MB)

ETD Abstract Container

Abstract Header

Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques

Al-Olimat, Hussein S
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600

Abstract Details

2014, Master of Science, University of Toledo, Engineering (Computer Science).
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 parameter-study of the applied PSO variants for WSN localization is performed, leading to results that show algorithmic improvements of up to 32\% better than the baseline results in the evaluated objectives.
Mansoor Alam (Committee Chair)
Robert Green, II (Committee Co-Chair)
Weiqing Sun (Committee Member)
Vijay Devabhaktuni (Committee Member)
79 p.
Artificial Intelligence; Computer Science; Engineering
Cloud Computing; Particle Swarm Optimization; Random Inertia Weight; Cloudlet Scheduling; Makespan; Utilization; CloudSim; Wireless Sensor Network; Trilateration; Localization; Multi-objective; ZigBee; RSSI; Genetic Algorithm; Simulated Annealing

Recommended Citations

Citations

  • Al-Olimat, H. S. (2014). Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600

    APA Style (7th edition)

  • Al-Olimat, Hussein. Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques. 2014. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600.

    MLA Style (8th edition)

  • Al-Olimat, Hussein. "Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques." Master's thesis, University of Toledo, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600

    Chicago Manual of Style (17th edition)

toledo1403922600
2,549
© 2014, some rights reserved.Creative Commons License Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques by Hussein S Al-Olimat is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.