Master of Science, University of Toledo, 2014, 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.

Committee: Mansoor Alam (Committee Chair); Robert Green II (Committee Co-Chair); Weiqing Sun (Committee Member); Vijay Devabhaktuni (Committee Member) Subjects: Artificial Intelligence; Computer Science; Engineering

MS, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science

We propose a group based real-time fault-tolerant physical intrusion detection system in an indoor scenario using Received Signal Strength Indicator (RSSI), to enhance security in wireless sensor networks considering its importance. Since there are a lot of techniques available to solve this problem in an outdoor scenario, we focus our research for the indoor environment. We provide a unique and novel approach, by applying a set of Fuzzy Logic (FL) rules on our distributed protocol before merging the beliefs of the fuzzy membership classes using Transferable Belief Model (TBM). Even though other techniques that have been designed earlier provide a solution to this problem, almost all of the techniques depend on incorporating additional sensor hardware. In some cases, sensor technology is even combined with other technologies such as cameras, motion sensors, video camera, etc. This makes the solutions complex, expensive, and difficult to deploy. However, there are published works that address this problem by measuring the drop in the RSSI. At the same time, many of the published works show that RSSI is an unreliable and unstable metric. Hence, we carry out an exhaustive experimentation to identify the behavior of RSSI both indoors and outdoors. The unstable characteristic of RSSI is clearly evident from these results. But, we embrace the unreliability of RSSI by using an additional metric, Link Quality Indicator (LQI) as a filter to localize the node in a network. Our approach helps in obtaining a tighter bound on the number of possible distances that any given two nodes are away from or to one another. Again, through experimental results, we observe a drastic reduction in the number of possible distances and show how RSSI and LQI can be used in combination for node localization. While, this reduced the number of possible distances, there were still numerous distances. Therefore, we propose a distributed protocol which employed Fuzzy Logic (FL) and Transferable (open full item for complete abstract)

Committee: Dharma Agrawal D.Sc. (Committee Chair); Prabir Bhattacharya Ph.D. (Committee Member); Anca Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science (MS), Ohio University, 2009, Industrial and Systems Engineering (Engineering and Technology)

Radio frequency identification (RFID) technology is used for inventory and asset tracking because of its accuracy and speed. Currently, RFID tracking systems are being used to identify and locate tagged objects in indoor environments. In this research, received signal strength indication (RSSI) values are collected from off-the-shelf passive RFID readers and antennas to be used in conjunction with an artificial neural network (ANN) to create a localization algorithm for two-dimensional location estimation with a single tag. The aim of this research is to create a highly accurate real-time location tracking system to be used in a room with objects that create RF interference. Multiple linear regression is used as a benchmark method for comparison with artificial neural networks.

Committee: Gary Weckman PhD (Advisor); Kevin Berisso PhD (Committee Member); Diana Schwerha PhD (Committee Member); Andrew Snow PhD (Committee Member) Subjects: Artificial Intelligence; Engineering; Industrial Engineering; Systems Design