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

Master of Science in Engineering (MSEgr), Wright State University, 2009, Electrical Engineering

The ever growing challenges in hostile environments, health care and warzone require accurate indoor localization and surveillance. The de facto localization technique using GPS has poor indoor performance due to the complexity of the indoor environment. Other Radio frequency based indoor localization techniques are unable of accurate localization due to multipath fading. In this thesis, a system consisting of Cricket wireless sensor motes, a camera and a Pan/Tilt gimbal is proposed to solve the indoor localization and surveillance problems. The system is easy to deploy, is cost effective and gives accurate results. The Crickets motes use the Time Difference of Arrival (TDoA) between the RF and the ultrasound signals to estimate the distance of the object. Multilateration is used to calculate the position of the object in the reference beacon coordinate system. This position is then transformed to the object coordinate system to calculate the pan and tilt angles of the gimbal which are then used to direct the camera to the object. The programming language JAVA was used to develop a GUI program to interface the gimbal, the camera and the Cricket motes. The localization and tracking of the object was successfully carried out in the laboratory. The accuracy of the system was tested using a laser pointer mounted on top of the camera and was shown that the system tracked the object with negligible error.

Committee: Kuldip Rattan Ph. D. (Advisor); Xiaodong Zhang Ph. D. (Committee Member); Devert Wicker Ph. D. (Committee Member) Subjects: Electrical Engineering

MS, University of Cincinnati, 2011, Engineering and Applied Science: Computer Science

There are many ways to geographically determine the boundary of an event based on its location and its nature through Satellite imaging and other learning mechanisms. In these methods, the availability of resources to perform the detection, their capabilities, actual time available to determine the event and its accessibility are constraints. At times, the satellite images may not be sufficient to get complete information about an event. Here we consider a particular case where the aim is to detect the actual boundary of an event based on its estimated boundary with the above constraints. A typical situation would be to determine the actual boundary of fire given the smoke area, or to estimate the concentration of chemical content, ideally any situation where sensors need to be used in an unmanned situation. We use a deploying agent to drop the sensors and there is a Base Station (BS) to which the event details are communicated by connectivity through localization with neighboring sensors. The research targets dynamic deployment of sensors with coverage and connectivity handled simultaneously as the information can reach the base station only if the sensors are able to connect to it. This is critical for real time applications. So we use an intelligent distribution scheme to test the behavior of different kinds of deployments using random, Gaussian, controlled random and combinational methods to deploy sensors. The set of parameters which are constraints are the communication radius of the base station, sensors & the event, the proximity of the event from the base station and location determination of the event based on the current state of the system. We use a weighted approach with more sensors around the event border and lesser inside to be able to detect the event and yet preserve the sensors as they might be lost due to fire or damage depending on the event. Additionally partial event boundary detection is used as experiment results show that we can reduce the (open full item for complete abstract)

Committee: Dharma Agrawal DSc (Committee Chair); Raj Bhatnagar PhD (Committee Member); George Purdy PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering

Distributed sensor networks are growing in popularity for a large number of sensing applications ranging from environmental monitoring to military target classification and tracking. However, knowledge of the individual sensor positions is a prerequisite to obtaining meaningful information from measurements made by the sensors. With the scale of sensor networks rapidly increasing due to advances in communications and MEMS technology, an automatic localization service based on inter-sensor measurements is becoming an essential element in modern networks. This dissertation studies fundamental aspects of localization performance while deriving general results for singular estimation problems. Because inter-sensor measurements, such as distances or angles-of-arrival (AOA), are invariant to absolute positioning of the sensor scene, localizing sensors with an absolute reference, e.g., latitude and longitude, is inherently a singular estimation problem suffering from non-identifiability of the absolute location parameters. This results in a corresponding singular Fisher information matrix. We consider means of regularizing the absolute localization problem and devise novel performance characterizations by showing that the location parameters have a natural decomposition into relative configuration and centroid transformation components based on the singularity of the problem. A linear representation of the transformation manifold, which includes representations of rotation, translation, and scaling, is used for decomposition of general localization error covariance matrices. The unified statistical framework presented – which naturally generalizes to non-localization problems – allows us to quantify and bound performance in the relative and transformation domains. These tools facilitate analysis of relative-only algorithms while enabling new algorithm development to finely tune performance in each subdomain. The analysis is applied to a novel closed-form AOA-based localiza (open full item for complete abstract)

Committee: Randolph Moses (Advisor) Subjects: