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SANTHANAM, LAKSHMIIntegrated Security Architecture for Wireless Mesh Networks
PhD, University of Cincinnati, 2008, Engineering : Computer Science and Engineering
Wireless Mesh Networks (WMNs) have revolutionized provisioning of economical and broadband wireless internet service to the whole community of users. The self-configurable and self-healing ability of WMNs has encouraged their rapid proliferation, as adding a mesh router (MR) is as simple as plugging and turning on. The plug-and-play architecture of WMN, however paves way to malicious intruders. An attacker can raise several security concerns, like rogue routers, selfishness, and denial-of-service attacks. Unfortunately, current thrust of research in WMNs, is primarily focused on developing multi-path routing protocols; and security is very much in its infancy. Owing to the hierarchical architecture of WMNs, security issues are multi-dimensional. As mesh routers form the backbone of the network, it is critical to secure them from various attacks. In this dissertation we develop integrated security architecture to protect the mesh backbone. It is important to provide an end-to-end security for mesh clients and hence we design a novel authentication protocol for mutually authenticating mesh clients and mesh routers. The aim of this dissertation is to explore various issues that affect the performance and security of WMNs. We first examine the threat of an active attack like Denial of service attack on MRs and design a cache based throttle mechanism to control it. Next, we develop a MAC identifier based trace table to determine the precise source of a DoS attacker. We then evaluate the vulnerability of WMNs to passive attacks, like selfishness and propose an adaptive mechanism to penalize selfish MRs that discretely drop other’s packets. In order to handle route disruption attacks like malicious route discovery, we design an intelligent Intrusion Detection System. Through extensive simulations, we evaluate effectiveness of our proposed solutions in mitigating these attacks. Finally, we design a light weight authentication protocol for mesh clients using inexpensive hash operations that enables authentication of important control messages and also performs auto-refresh of authentication tokens.

Committee:

Dr. Dharma Agrawal (Advisor)

Keywords:

Wireless mesh networks,; Networks attack in mesh networks,; Selfishness in mesh networks,; Security in mesh networks,; mutual authentication protocol for mesh clients,

Kadiyala, AkhilDevelopment and Evaluation of an Integrated Approach to Study In-Bus Exposure Using Data Mining and Artificial Intelligence Methods
Doctor of Philosophy in Engineering, University of Toledo, 2012, Civil Engineering

The objective of this research was to develop and evaluate an integrated approach to model the occupant exposure to in-bus contaminants using the advanced methods of data mining and artificial intelligence. The research objective was accomplished by executing the following steps. Firstly, an experimental field program was implemented to develop a comprehensive one-year database of the hourly averaged in-bus air contaminants (carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), 0.3-0.4 micrometer (¿¿¿¿m) sized particle numbers, 0.4-0.5 ¿¿¿¿m sized particle numbers, particulate matter (PM) concentrations less than 1.0 ¿¿¿¿m (PM1.0), PM concentrations less than 2.5 ¿¿¿¿m (PM2.5), and PM concentrations less than 10.0 ¿¿¿¿m (PM10.0)) and the independent variables (meteorological variables, time-related variables, indoor sources, on-road variables, ventilation settings, and ambient concentrations) that can affect indoor air quality (IAQ). Secondly, a novel approach to characterize in-bus air quality was developed with data mining techniques that incorporated the use of regression trees and the analysis of variance. Thirdly, a new approach to modeling in-bus air quality was established with the development of hybrid genetic algorithm based neural networks (or evolutionary neural networks) with input variables optimized from using the data mining techniques, referred to as the GART approach. Next, the prediction results from the GART approach were evaluated using a comprehensive set of newly developed IAQ operational performance measures. Finally, the occupant exposure to in-bus contaminants was determined by computing the time weighted average (TWA) and comparing them with the recommended IAQ guidelines.

In-bus PM concentrations and sub-micron particle numbers were predominantly influenced by the month/season of the year. In-bus SO2 concentrations were mainly affected by indoor relative humidity (RH) and the month of the year. NO concentrations inside the bus cabin were largely influenced by the indoor RH, while NO2 concentrations primarily varied with the month of the year. Passenger ridership and the month of the year mainly affected the in-bus CO2 concentrations; while the month and sky conditions had a significant impact on CO concentrations within the bus compartment.

The hybrid GART models captured majority of the variance in in-bus contaminant concentrations and performed much better than the traditional artificial neural networks methods of back propagation and radial basis function networks.

Exposure results indicated the average 8-hr. exposure of biodiesel bus occupants to CO2, CO, NO, SO2, and PM2.5 to be 559.67 ppm (¿¿¿¿ 45.01), 18.33 ppm (¿¿¿¿ 9.23), 5.23 ppm (¿¿¿¿ 4.49), 0.13 ppm (¿¿¿¿ 0.01), and 13.75 ¿¿¿¿g/m3 (¿¿¿¿ 4.24), respectively. The statistical significance of the difference in exposure levels to in-bus contaminants were compared during morning, afternoon, and evening/night time periods. There was statistically significant difference only between the morning (driver 1) and the evening/night (driver 3) exposure levels for CO2 and PM2.5. CO levels exceeded the TWA in some months.

Committee:

Dr. Ashok Kumar, PhD (Committee Chair); Dr. Devinder Kaur, PhD (Committee Member); Dr. Cyndee Gruden, PhD (Committee Member); Dr. Defne Apul, PhD (Committee Member); Dr. Farhang Akbar, PhD (Committee Member)

Subjects:

Civil Engineering; Environmental Engineering; Environmental Health

Keywords:

Indoor Air Quality; Public Transportation Buses; Biodiesel; Data Mining; Sensitivity of the Regression Trees; Artificial Neural Networks; Genetic Algorithm Neural Networks; Evolutionary Neural Networks; In-Bus Exposure; Air Quality Model Validation

Prakash, AbhinavRendering Secured Connectivity in a Wireless IoT Mesh Network with WPAN's and VANET's
PhD, University of Cincinnati, 2017, Engineering and Applied Science: Computer Science and Engineering
A ubiquitous pervasive network incorporates today’s Internet of Things/Internet of Everything Paradigm: Everything becomes smart with at least one microprocessor and a network interface. All these are under an umbrella of IoT/IoE paradigm where everything is network capable and connected. In most of the cases, these devices have multiple microprocessors and network interfaces at their disposal. In such a scenario, bringing every application to specific network on the same platform is critical, specifically for Sensor Networks, Cloud, WPANs and VANETs. While, enforcing and satisfying the requirements of CIA triad with non-repudiation universally is critical as this can solve multiple existing problems of ISM band exhaustion, leading to excessive collisions and contentions. Cooperative Interoperability also enables universal availability of data across all platforms which can be reliable and fully synchronized. Plug and play universal usability can be delivered. Such a network necessitates robust security and privacy protocols, spanning uniformly across all platforms. Once, reliable data access is made available, it leads to an accurate situation aware decision modeling. Simultaneous multiple channel usage can be exploited to maximize bandwidth otherwise unused. Optimizing Content delivery in hybrid mode which will be the major chunk of network traffic as predicted for near future of IoE. Now, such a proposed hybrid network does sound very complicated and hard to establish and maintain. However, this is the future of networks with huge leaps of technological advancement and ever dropping prices of hardware coupled with immensely improved capabilities, such a hybrid ubiquitous network can be designed and deployed in a realistic scenario. In this work, we go through not only looking into the issues of the large scale hybrid WMN, but also minutely discovering every possible scenario of direct mesh clients or sub-nets (VANET, Cloud or BAN) associated to it. Further, we propose to design and implement a robust all around security and privacy for each and every possible unit of such a large network. Special focus is provided to the application of a BAN in medical usage with intricate details is provided in form of our recent endeavor, along with an ongoing work for a wearable device patent, Smart Shoe (Patent Pending). The concepts explained with this example are equally applicable to any such Wireless Personal Area Networks (WPAN’s).

Committee:

Dharma Agrawal, D.Sc. (Committee Chair); Richard Beck, Ph.D. (Committee Member); Yizong Cheng, Ph.D. (Committee Member); Rashmi Jha, Ph.D. (Committee Member); Wen-Ben Jone, Ph.D. (Committee Member); Marepalli Rao, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

IoT;Mesh Networks;Security;Ubiquitous Networks;Vehicular Networks;Cryptography

Gummadi, JayaramA Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio
Master of Science in Engineering, University of Toledo, 2013, Engineering (Computer Science)
Radon-222 and its parent Radium-226 are naturally occurring radioactive decay products of Uranium-238. The US Environmental Protection Agency (USEPA) attributes about 10 percent of lung cancer cases that is `around 21,000 deaths per year’ in the United States, caused due to indoor radon. The USEPA has categorized Ohio as a Zone 1 state (i.e. the average indoor radon screening level greater than 4 picocuries per liter). In order to implement preventive measures, it is necessary to know radon concentration levels in all the zip codes of a geographic area. However, it is not possible to survey all the zip codes, owing to reasons such as inapproachability. In such places where radon data are unavailable, several interpolation techniques are used to estimate the radon concentrations. This thesis presents a comparison between recently developed interpolation techniques to new techniques such as Support Vector Regression (SVR), and Random Forest Regression (RFR). Recently developed interpolation techniques include Artificial Neural Network (ANN), Knowledge Based Neural Networks (KBNN), Correction-Based Artificial Neural Networks (CBNN) and the conventional interpolation techniques such as Kriging, Local Polynomial Interpolation (LPI), Global Polynomial Interpolation (GPI) and Radial Basis Function (RBF) using the K-fold cross validation method.

Committee:

William Acosta (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Ashok Kumar (Committee Member); Rob Green (Committee Member)

Subjects:

Computer Science

Keywords:

artificial neural networks; cross-validation; correction based artificial neural networks; prior knowledge input; source difference; space-mapped neural networks; support vector regression; radon; random forest regression

CAVALCANTI, DAVE ALBERTO TAVARESINTEGRATED ARCHITECTURE AND ROUTING PROTOCOLS FOR HETEROGENEOUS WIRELESS NETWORKS
PhD, University of Cincinnati, 2006, Engineering : Computer Science and Engineering
One of the main challenges in next generation wireless networks is to integrate heterogeneous wireless technologies to provide seamless connectivity, with guaranteed Quality of Service (QoS), to mobile users “anytime, anywhere and with any device”. In this dissertation, we investigate the problem of integrating cellular networks and Wireless Local Area Networks (WLANs) with the multi-hop communication paradigm used in Mobile Ad hoc Networks (MANETs) to exploit all the connectivity alternatives available to different types of Mobile Stations (MSs). We propose an integrated architecture based on three basic functionalities, namely, topology discovery, gateway discovery, and link quality estimation. We combine these three functionalities into an integrated routing mechanism that exploits all connectivity alternatives available in a generic heterogeneous scenario. Then, we provide a simulation-based analysis of our architecture and integrated routing mechanism in different heterogeneous networking scenarios. Our results show improvements in network’s capacity and coverage achieved by our architecture as compared to isolated networks. The results also highlight the importance of the link quality estimation in providing QoS to users, as well as indicate that multi-hop links can be exploited in a controlled network configuration, but the QoS in multi-hop routes cannot be always guaranteed. Furthermore, we address the problem of selecting the best connectivity opportunity for a given service type based on the applications’ QoS requirements, as well as on the network condition and user mobility profile. We propose the Connectivity opportunity Selection Algorithm (CSA) that allows MSs to select the connectivity opportunity most appropriate for a given type of service and mobility profile. Furthermore, we describe how our proposed selection algorithm can be introduced into the IEEE 802.21 standard for Media Independent Handover services.

Committee:

Dr. Dharma Agrawal (Advisor)

Subjects:

Computer Science

Keywords:

Heterogeneous Wireless Networks.; Routing Protocols for Heterogeneous Wireless Networks; Multi-hop communications in integrated wireless networks; network selection; always best connectivity

SUBRAMANIAN, VINODSOCRATES: Self-Organized Corridor Routing and Adaptive Transmission in Extended Sensor Networks
MS, University of Cincinnati, 2003, Engineering : Electrical Engineering
Large-scale sensor networks (LSSN's) are formed when very large numbers of miniaturized sensor nodes with wireless communication capability are deployed randomly over an extended region, e.g., scattered from the air or embedded in material. Systems such as smart matter, smart paint and smart dust imply the existence of LSSN's, but they can also be used in applications involving large geographical regions such as environmental monitoring or disaster relief. Our contention is that, given their scale and random structure, LSSN's should be treated as complex systems rather than as standard wireless networks. Approaches from wireless networks typically have difficulty scaling up to large numbers of nodes, especially when the nodes have limited capabilities and are deployed over a region much larger than their communication range. We explore how a system comprising of very large number of randomly distributed sensor nodes can organize itself to communicate information. To keep the system realistic, we assume that nodes in our system are unreliable, have limited energy resources and have minimal on-board computational capabilities. Our focus is on the efficient routing of messages in such a system, specifically on the network algorithms aspect, rather than on issues such as hardware, signal processing and communication. The goal is to develop a system that scales effectively and is robust to node failures. The approach we propose is to limit the usage of bandwidth and energy while tapping the inherent parallelism of simple flooding to achieve robustness. Simulation results show significant improvement in performance compared to simple flooding algorithms.

Committee:

Dr. Ali A. Minai (Advisor)

Keywords:

sensor networks; wireless networks; complex systems; adaptation; large scale networks

GUPTA, ANANYADECENTRALIZED KEY GENERATION SCHEME FOR CELLULAR-BASED HETEROGENEOUS WIRELESS Ad Hoc NETWORKS
MS, University of Cincinnati, 2006, Engineering : Computer Engineering
A majority of group communication applications in cellular-based heterogeneous wireless setups entail secure data exchange. The problem can be effectively tackled if the underlying cellular infrastructure is used to provide an authentication backbone to the security associations. We propose a novel distributed ID based key exchange mechanism using shared polynomials in which the shares are generated by the communicating groups. Our idea employs a mechanism where the Base Stations (BSs) carry out an initial key generation by a polynomial in a distributed manner and then pass on the key material to the Mobile Stations (MSs). The multi-interface MSs can now securely communicate over interfaces other than cellular. The scheme incorporates symmetric polynomials, which are chosen by the BS acting as polynomial distributors. Simulations done to measure performance have shown encouraging results.

Committee:

Dr. Agrawal Dharma (Advisor)

Subjects:

Computer Science

Keywords:

Ad hoc networks; Base Station; Cellular networks; Distributed algorithm; Heterogeneous networks; Multi-interface Mobile Station; Pairwise key; Polynomial; Symmetric key

Hope, PriscillaUsing Artificial Neural Networks to Identify Image Spam
Master of Science, University of Akron, 2008, Computer Science

Internet technology has made international communication easy and convenient. This convenience has compelled a number of people to rely on electronic mail for almost all spheres of life – personal, business etc. Scrupulous organizations/individuals have taken undue advantage of this convenience and populate users’ inboxes with unwanted messages making email spam a menace. Even as anti-spam software producers think they have almost solved the problem, spammers come out with new techniques. One such tactic in the spammers’ toolbox comes in the form of image spam – messages that contain little more than a link to an image rendered in an HTML mail reader. The image typically contains the spam message one hopes to avoid, yet it is able to bypass most filters due to the composition and format of these pictures.

This research focuses on identifying these images as spam by using an artificial neural network (ANN), software programs used for recognizing patterns, based on the biological neural networks in our brains. As information propagates through a neural network, it “learns” about the data. A large collection of both spam and non-spam images have being used to train an ANN, and then test the effectiveness of the trained network against an unidentified or already identified set of pictures. This process involves formatting images and adding the desired training values expected by the ANN. Several different ANNS have being trained using different configurations of hidden layers and nodes per layer. A detailed process for preprocessing spam image files is given, followed by a description on how to train an artificial neural network to distinguish between ham and spam. Finally, the trained network is tested against both known and unknown images.

Committee:

Kathy Liszka, PhD (Advisor); Timothy O’Neil (Other); Tim Marguish (Other)

Subjects:

Computer Science

Keywords:

image spam; FANN; artificial neural networks; using artificial neural networks to identify image spam

Basheer, Al-QassabReliability of Data Collection and Transmission in Wireless Sensor Networks
Master of Science in Engineering, Youngstown State University, 2013, Department of Electrical and Computer Engineering
A network of wireless sensor nodes that are connected to a centralized base station is presented to conduct a study on reliability of data collection and transmission in wireless sensor networks (WSNs) with focus on data loss and data duplication. Software applications for specific sensor nodes called Sun SPOTs are presented, and programming techniques, for example packet transmitting time delay and data checking for loss and duplication, are implemented in these software applications to improve the functionality of the network. Acceleration data on a vibration plate are collected at sampling frequency of 100 Hz to validate the operation of the network. Additionally, the wireless sensor network is optimized to enhance the synchronization of data collection from different nodes. The result of this research shows that the reliability of the network is related to data sampling frequency, synchronization of the wireless data traffic, wireless sensor node signal strength, and wireless data routing protocols. The indoor tests on signal strength show the limitation of -70 dBm and higher for optimum data collection without data or packet loss.

Committee:

Li Frank, Ph.D. (Advisor); Munro Philip, Ph.D. (Committee Member); Mossayebi Faramarz, Ph.D. (Committee Member)

Subjects:

Computer Engineering; Electrical Engineering; Engineering; Information Technology

Keywords:

Wireless sensor networks; WSN; data collection; data transmission; reliability of wireless sensor networks

Bhargava, SonaliGeneric and Scalable Security Schemes for Ad Hoc Networks
MS, University of Cincinnati, 2002, Engineering : Computer Science

An Ad Hoc network is a collection of wireless, mobile nodes that dynamically form a network without the use of centralized, fixed network infrastructure. Inherent characteristics of an Ad Hoc network such as dynamic topology and limited physical security poses severe security challenges to the network. Hence, these networks demand much stronger security mechanisms than the traditional, wired and static networks. Well established contemporary routing protocols seem to adapt to the dynamic conditions as well. However, they provide either no security mechanisms at all, or have only partial solutions for protecting the dynamic routing framework.

It is hard to achieve security and robustness in the routing protocols at the same time in such networks. Several issues have to be understood and addressed before devising a security mechanism. Moreover, challenges involved in addressing attacks differ from one protocol to the other. This thesis targets at securing reactive routing protocol AODV. The routing protocol is vulnerable to two kinds of attack: External and Internal attack. We have discussed some existing external attacks and possible malicious behavior from compromised nodes. To mitigate the attacks, we propose a dual level security model. On the first level, we have External Attack Detection Model(EADM), that secures the network with authentication and confidentiality that rely on mutual trust between nodes. And on the second level, Intrusion Detection Model (IDM) identifies the misbehaving nodes using the knowledge base and Response Model (RM) isolates these nodes from the network.

Committee:

Dr. Dharma P. Agrawal (Advisor)

Subjects:

Computer Science

Keywords:

Ad Hoc Networks; security; AODV; IP sec; wireless networks

Yoon, Suk-UnDynamic Radio Resource Allocation in Wireless Sensor and Cognitive Radio Networks
Master of Science, The Ohio State University, 2009, Electrical and Computer Engineering

In wireless networks, it is required to change an operating frequency as part of the radio resource management due to strong interference or system requirements of accessing radio resources. In this thesis, we propose two radio resource management schemes in wireless sensor networks and cognitive radio networks. In the proposed schemes, sensor networks switch to a new channel when they detect strong interference and a secondary user in cognitive radio networks moves to a new spectrum when it detects or predicts the presence of a primary user.

In the first part of the thesis, we propose a channel hopping scheme which can be used for interfered wireless networks. With the additive functionality of a channel hopping mechanism on the sensor network stack, we aim to avoid the interference from other sensor nodes and wireless technologies on ISM band as well as avoid narrow-band jamming. For simple and reliable channel hopping, we introduce an Adaptive Channel Hopping scheme, a spectrum environment aware channel hopping scheme, for interference robust wireless sensor networks. When the channel status becomes suboptimal to communicate, the adaptive channel hopping lets the sensors switch to a new clean channel. To generate channel selection/scanning orders which minimize channel hopping latency, we use two parameters which are link quality indicator (LQI) and channel weighting. The proposed adaptive channel hopping scheme is evaluated through simulations. Simulation results indicate that the proposed scheme significantly reduces the channel hopping latency and selects the best quality channel.

In the second part of the thesis, we propose a novel approach to spectrum management in cognitive radio networks. To support flexible use of spectrum, cognitive radio networks employ spectrum mobility management schemes, including spectrum handoff, which refers to the switching of the operating spectrum due to changes in licensed (primary) user activity. Spectrum handoff inevitably results in temporary disruption of communication for the unlicensed (secondary) user operating in a licensed band opportunistically. Minimization of secondary user service disruption is an important objective of spectrum handoff schemes. In this thesis, we introduce a new type of spectrum handoff called Voluntary Spectrum Handoff assisted by a primary user spectrum usage estimation scheme. The two mechanisms proposed under voluntary spectrum handoff method estimate opportune times to initiate unforced spectrum handoff events to facilitate setup and signaling of alternative channels without having communication disruption, which occurs when a secondary user is forced out of an operating spectrum due to primary user activity. To estimate primary user spectrum usage, channel usage information is continuously updated with a fixed spectrum sensing window and a variable history window. Proposed voluntary spectrum handoff and primary usage estimation schemes are evaluated through extensive simulations. Simulation results indicate that the proposed schemes significantly reduce the communication disruption duration due to handoffs.

Committee:

Eylem Ekici (Advisor); Bradley Clymer, D. (Committee Member)

Subjects:

Engineering

Keywords:

Channel Hopping; Wireless Sensor Networks; Spectrum Handoff; Cognitive Radio Networks

Anderson, Jerone S.A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks
Master of Science (MS), Ohio University, 2009, Industrial and Systems Engineering (Engineering and Technology)
The Old Woman Creek National Estuary is studied in this project to evaluate effective modelling techniques for predicting Net Ecosystem Metabolism (NEM). NEM is modelled using artificial neural networks, Bayesian belief networks, and a hybrid model. A variety of data preprocessing techniques are considered prior to model development. The effects of discretization on model development are considered and discrete data is ultimately used to produce models which classify NEM into three ranges based on inputs with information significance. Artificial neural networks are found to be the most accurate for classification while Bayesian belief networks are found to provide a better framework for dynamically predicting NEM as inputs are changed.

Committee:

Gary R. Weckman, PhD (Advisor); David Millie, PhD (Committee Member); Kevin Berisso, PhD (Committee Member); Diana Schwerha, PhD (Committee Member)

Subjects:

Ecology; Engineering; Environmental Engineering; Industrial Engineering

Keywords:

BBN; ANN; ecology; NEM; Bayesian Belief Networks; Artificial Neural Networks; computer modelling

Perumal, SubramoniamStability and Switchability in Recurrent Neural Networks
MS, University of Cincinnati, 2008, Engineering : Computer Science

Artificial Neural Networks (ANNs) are being extensively researched for their wide range of applications. Among the most important is the ability of a type of ANNs—recurrent attractor networks—to work as associative memories. The most common type of ANN used for associative memory is the Hopfield network, which is a fully connected network with symmetric connections. There have been numerous attempts to improve the capacity and recall quality of recurrent networks, with the focus primarily on the stability of the stored attractors, and the network's convergence properties. However, the ability of a recurrent attractor network to switch between attractors is also an interesting property, if it can be harnessed for use. Such switching can be useful as a model of switching between context-dependent functional networks thought to underlie cognitive processing.

In this thesis, we design and develop a stable-yet-switchable (SyS) network model which provides an interesting combination of stability and switchability. The network is stable under random perturbations, but highly sensitive to specific targeted perturbations which cause it to switch attractors. Such functionality has previously been reported in networks with scale-free (SF) connectivity. We introduce networks with two regions: A densely connected core region, and a sparsely connected and larger periphery. We show that these core-periphery (CP) networks are better for providing a combination of stability and targeted switching than scale-free networks. We develop and validate a specific approach to switching between attractors in a targeted way. The CP and SF models are also compared with each other and with randomly connected homogeneous networks.

Committee:

Dr. Ali Minai (Advisor); Dr. Raj Bhatnagar (Committee Member); Dr. Anca Ralescu (Committee Member)

Subjects:

Computer Science; Engineering

Keywords:

recurrent neural networks; core-periphery networks; switchability; switching between attractors; stability and switchability

Stevenson, Lauren DeMarcoThe Influence of Treatment Motivation, Treatment Status and Social Networks on Perceived Social Support of Women with Substance Use or Co-Occurring Disorders
Doctor of Philosophy, Case Western Reserve University, 2009, Social Welfare

This study examined predictors of perceived social support and support forrecovery of women with substance use disorders or co-occurring substance use and mental disorders. The sample consisted of 136 adult women; 86 women were engaged in inpatient and outpatient substance abuse treatment programs, and 50 women were recruited from a study of mothers with cocaine exposed infants.

The women in the study were predominantly African American (82.4%) and of low income status with 80% of the women reporting an annual family income below $15,000. All of the women had a current substance use disorder and 77 (56.6%) of the women also had a co-occurring mental disorder including: Major Depression, Post Traumatic Stress Disorder, Mania, Generalized Anxiety Disorder, Hypomania, and Dysthymia. On average, women reported having a social network comprised of 10.73 members.

A significant relationship was found between critical members (those who provide negative support) within women’s social networks and perceived social support, with a higher percent of critical network members predicting lower perceived social support. Perceived social support scores were also significantly lower for women with a co-occurring mental disorder. Indirect relationships were found for women’s perceived social support. The percent of professionals within women’s social networks moderated the relationships between women’s treatment motivation and treatment status with perceived social support. The percent of substance users in women’s networks moderated the relationship between treatment motivation and perceived social support.

A sub sample analysis of 86 women in substance abuse treatment explored predictors of support for recovery. A significant relationship was found between the percent of members who support sobriety and support for recovery. This finding provides construct validity for the support for recovery measure.

Practice implications as well as directions for future research are included in this study. Findings suggest that clinicians should work with social network members and clients on improving communication and eliminating critical support to improve social support. Future research should focus on the impact of social relationships on treatment outcomes.

Committee:

Elizabeth Tracy, PhD (Committee Chair); David Biegel, PhD (Committee Member); Kathryn Adams, PhD (Committee Member); Sonia Minnes, PhD (Committee Member)

Subjects:

Social Research; Social Work

Keywords:

Social Support Networks; Social Support; Substance Use Disorders; Dual Disorders; Co-Occurring Disorders; Treatment Motivation; Social Networks; Substance Abuse; Women

Prakash, AbhinavAnonymous and Secure Communication in a Wireless Mesh Network
MS, University of Cincinnati, 2012, Engineering and Applied Science: Computer Science

With the rapid advancement of different types of wireless technologies the problem arose of combining them together to provide improved bandwidth and enhanced throughput. The answer came out in the form of a Wireless Mesh Network (WMN). A typical WMN is made up of mesh routers and mesh clients where mesh routers have somewhat limited mobility and they form the backbone of the network whereas mesh clients are allowed to be highly mobile or completely stationary or somewhere in between. This forms a very versatile network which allows clients with different levels of mobility, interface and bandwidth requirements to be a part of the same network. The communication can be achieved by directly communicating with the router by being in its range or in an ad hoc fashion through several hops. A WMN is mainly designed to be self-configured and self-adjusting dynamically. This ensures large network coverage with minimum infrastructure requirements, hence low cost. Although a WMN gives multifold advantages it is also vulnerable to several security and privacy threats being a dynamic open medium. Different types of clients such as laptops, cell phones, smart devices can join or leave the network anytime they wish. This opens up issues like fake registrations and packet sniffing.

This work deals with the issues of security and privacy separately in two parts in great detail by simulating countermeasures for different kinds of attacks in a WMN. The first part mainly deals with creating a perfectly secure network for safe communication by using a bi-variate polynomial scheme for low overheads instead of a public-private key mechanism. The second part deals with making any communication in the network anonymous by hiding the node initiating the session by using redundancy at the cost of some associated overheads.

Committee:

Dharma Agrawal, DSc (Committee Chair); Yizong Cheng, PhD (Committee Member); Chia Han, PhD (Committee Member)

Subjects:

Computer Science

Keywords:

Mesh Networks; Security; Onion Routing; Bivariate Polynomial Function; Backbone; Hybrid Networks

Mackersie, JohnATHLETES’ PERSPECTIVES ON PSYCHOLOGICAL REHABILITATION FROM SPORT INJURY IN RELATION TO THEIR RESTORATION NETWORKS
Master of Science in Sport Studies, Miami University, 2010, Physical Education, Health, and Sport Studies
This paper examines the role of social support and its affect on athletic injury rehabilitation. The study utilized a semi-structured interview structure on six previously injured Division I athletes. Results were analyzed using qualitative methodology looking for emergent themes and sub-themes. It was originally thought a social network of supporting roles was crucial for injured athletes’ recovery. However, with the current results, it is now evident that social networks are but a small fraction of the process. This study concludes with future research directions.

Committee:

Robin Vealey (Advisor); Valeria Freysinger (Committee Chair); Brett Massie (Committee Chair)

Subjects:

Psychology; Social Psychology; Sociology; Sports Medicine

Keywords:

social support; restoration networks; social; networks; support; rehabilitation; athletic injury

Doboli, SimonaLatent Attractors: A Mechanism for Context-Dependent Information Processing in Biological and Artificial Neural Systems
PhD, University of Cincinnati, 2001, Engineering : Electrical Engineering
The hippocampus is an important area in the brain involved mainly in memory processes. In humans, the hippocampus is essential for the formation and consolidation of memory, and is a primary target of Alzheimer's disease. In other animals, the hippocampus is especially involved in spatial tasks (e.g. navigation). It has been the subject of extensive experimental and theoretical investigation due to its major role in memory and cognition. This thesis focuses mainly on the mechanisms of context-dependent, non-linear spatial information processing in the rodent hippocampus. There is strong experimental evidence that the hippocampus creates and stores cognitive maps of an animal's environment. These maps facilitate path planning and goal-directed behavior – all tasks of great interest in robots as well as animals. However, the mechanisms of spatial information processing in the hippocampus are not completely understood. One aspect of cognitive maps that is not yet clarified is their dependence on the past experience, or context. The first part of the thesis focuses on developing computational models for context-dependent cognitive maps. These models are based on the idea of latent attractors – patterns embedded in recurrent neural networks that influence network dynamics and the response to external inputs without becoming fully manifested themselves. Context-dependent information processing is important not only in animal cognition, but also for problems such as robot navigation, sequence disambiguation, sequential recognition, etc. In the second part of the thesis the biologically inspired concept of latent attractor networks is studied as a general computational paradigm for solving context-dependent problems with neural networks. To gain better understanding of the capabilities of latent attractor networks, a theoretical analysis of their capacity and dynamics is performed. In addition to the model for context-dependence, more comprehensive computational models of the hippocampus are developed to explain specific experimental results such as the effect of changes in the environment on cognitive maps.

Committee:

Ali Minai (Advisor)

Keywords:

context-dependent information processing; attractor neural networks; spatial processing in the hippocampus; analysis of attractor neural networks

Uppalapati, PraneethNetwork Mining Approach to Cancer Biomarker Discovery
Master of Science, The Ohio State University, 2010, Computer Science and Engineering

With the rapid development of high throughput gene expression profiling technology, molecule profiling has become a powerful tool to characterize disease subtypes and discover gene signatures. Most existing gene signature discovery methods apply statistical methods to select genes whose expression values can differentiate different subject groups. However, a drawback of these approaches is that the selected genes are not functionally related and hence cannot reveal biological mechanism behind the difference in the patient groups.

Gene co-expression network analysis can be used to mine functionally related sets of genes that can be marked as potential biomarkers through survival analysis. We present an efficient heuristic algorithm EigenCut that exploits the properties of gene co-expression networks to mine functionally related and dense modules of genes. We apply this method to brain tumor (Glioblastoma Multiforme) study to obtain functionally related clusters. If functional groups of genes with predictive power on patient prognosis can be identified, insights on the mechanisms related to metastasis in GBM can be obtained and better therapeutical plan can be developed. We predicted potential biomarkers by dividing the patients into two groups based on their expression profiles over the genes in the clusters and comparing their survival outcome through survival analysis. We obtained 12 potential biomarkers with log-rank test p-values less than 0.01.

Committee:

Kun Huang, PhD (Committee Chair); Raghu Machiraju, PhD (Committee Member)

Subjects:

Bioinformatics; Computer Science

Keywords:

Biomarker; Gene co-expression networks; Glioblastoma Multiforme; Network mining; Biological networks

Pech, Thomas JoelA Deep-Learning Approach to Evaluating the Navigability of Off-Road Terrain from 3-D Imaging
Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Computer and Information Sciences
This work investigates a strategy for evaluating the navigability of terrain from 3-D imaging. Labeled training data was automatically generated by running a simulation of a mobile robot nai¨vely exploring a virtual world. During this exploration, sections of terrain were perceived through simulated depth imaging and saved with labels of safe or unsafe, depending on the outcome of the robot's experience driving through the perceived regions. This labeled data was used to train a deep convolutional neural network. Once trained, the network was able to evaluate the safety of perceived regions. The trained network was shown to be effective in achieving safe, autonomous driving through novel, challenging, unmapped terrain.

Committee:

Wyatt Newman (Advisor); Cenk Cavusoglu (Committee Member); Michael Lewicki (Committee Member)

Subjects:

Computer Science; Robotics; Robots

Keywords:

Mobile robots, Autonomous Navigation, Machine Learning, Artificial Neural Networks, Terrain, Simulation, Training Data, Data Generation, Labeling, Classifiers, Convolutional Neural Networks, Point Clouds, Perception, Prediction, Artificial Intelligence

Yoon, HyejinThe Animation Industry: Technological Changes, Production Challenge, and Glogal Shifts
Doctor of Philosophy, The Ohio State University, 2008, Geography

Animated films have grown in popularity as expanding markets (such as TV and video) and new technologies (notably computer graphics imagery) have broadened both the production and consumption of cartoons. As a consequence, more animated films are produced and watched in more places, as new - worlds of production - have emerged. The animation production system, specialized and distinct from film production, relies on different technologies and labor skills. Therefore, its globalization has taken place differently from live-action film production, although both are structured to a large degree by the global production networks (GPNs) of the media conglomerates.

This research examines the structure and evolution of the animation industry at the global scale. In order to investigate these, 4,242 animation studios from the Animation Industry Database are used. The spatial patterns of animation production can be summarized as, 1) dispersion of the animation industry, 2) concentration in world cities, such as Los Angeles and New York, 3) emergence of specialized animation cities, such as Annecy and Angoulême in France, and 4) significant concentrations of animation studios in some Asian countries, such as India, South Korea and the Philippines.

In order to understand global production networks (GPNs), networks of studios in 20 cities are analyzed. Animation studios in these cities have formed different types of networks - some global, some local, and some both global and local. In addition to seeking lower production cost, other factors, such as institutions, business culture and cultural contents have affected the geography and strategies of animation studios throughout the world.

Committee:

Edward Malecki (Committee Chair); Nancy Ettlinger (Committee Member); Darla Munroe (Committee Member)

Subjects:

Geography

Keywords:

cultural industries; animation industry; world city networks; global production networks (GPNs); technology; computer graphic imagery (CGI); globalization

Ghosh Dastidar, SamanwoyModels of EEG data mining and classification in temporal lobe epilepsy: wavelet-chaos-neural network methodology and spiking neural networks
Doctor of Philosophy, The Ohio State University, 2007, Biomedical Engineering
A multi-paradigm approach integrating three novel computational paradigms: wavelet transforms, chaos theory, and artificial neural networks is developed for EEG-based epilepsy diagnosis and seizure detection. This research challenges the assumption that the EEG represents the dynamics of the entire brain as a unified system. It is postulated that the sub-bands yield more accurate information about constituent neuronal activities underlying the EEG. Consequently, certain changes in EEGs not evident in the original full-spectrum EEG may be amplified when each sub-band is analyzed separately. A novel wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma sub-bands of EEGs for detection of seizure and epilepsy. The methodology is applied to three different groups of EEGs: healthy subjects, epileptic subjects during a seizure-free interval (interictal), and epileptic subjects during a seizure (ictal). Two potential markers of abnormality quantifying the non-linear chaotic EEG dynamics are discovered: the correlation dimension and largest Lyapunov exponent. A novel wavelet-chaos-neural network methodology is developed for EEG classification. Along with the aforementioned two parameters, the standard deviation (quantifying the signal variance) is employed for EEG representation. It was discovered that a particular mixed-band feature space consisting of nine parameters and LMBPNN result in the highest classification accuracy (96.7%). To increase the robustness of classification, a novel principal component analysis-enhanced cosine radial basis function neural network classifier is developed. The rearrangement of the input space along the principal components of the data improves the classification accuracy of the cosine radial basis function neural network employed in the second stage significantly. The new classifier is as accurate as LMBPNN and is twice as robust. Next, biologically realistic artificial neural networks are developed to reach the next milestone in artificial intelligence. First, an efficient spiking neural network (SNN) model is presented using three training algorithms: SpikeProp, QuickProp, and RProp. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Next, a new Multi-Spiking Neural Network (MuSpiNN) and supervised learning algorithm (Multi-SpikeProp) are developed. Finally, the models are applied to the epilepsy and seizure detection problems to achieve high classification accuracies.

Committee:

Hojjat Adeli (Advisor)

Keywords:

Temporal Lobe Epilepsy; Electroencephalogram (EEG); EEG Classification; Epilepsy Diagnosis; Seizure Detection; Wavelet Transform; Chaos Theory; Artificial Neural Networks; Spiking Neural Networks; Principal Component Analysis; Cosine Radial Basis Function

Gaur, AmitSecured Communication in Wireless Sensor Network (WSN) and Authentic Associations in Wireless Mesh Networks
MS, University of Cincinnati, 2010, Engineering and Applied Science: Computer Science
Wireless sensors are low power devices with small transmission range, restricted computation power, limited amount of memory and with portable power supply. Wireless Sensor Network (WSN) is a collection of such sensors where the number of sensors can vary from few hundreds to thousands. Performing secure pair-wise communication between sensors is a really difficult task due to inherent characteristics such as lack of any fixed infrastructure. As memory and power consumption are most stringent requirements for these devices, use of conventional techniques for secured communication are totally out of question. This thesis introduces scheme that enables a complete pair-wise secure connectivity between any two adjacent sensor nodes in spite of using small key ring (KR) for sensors. The Proposed Scheme (ELKPD) doesn't require any additional hardware while providing keys to the sensors irrespective of their location. Also, proposed scheme is easily scalable which allows enables addition of sensor nodes without any computational or hardware overheads. Due to the varying degree of mobility of Mesh Clients has provided much more flexibility in Wireless Mesh Networks. And establishing an Authentic Association among entities is a non -trivial problem. In this thesis, we introduce a Polynomial Based scheme which not only provides high pair-wise connectivity, low communication and storage overhead and high scalability but also makes on the fly Authentic Association feasible. The proposed scheme is also observed to be resilient against both the traffic analysis and the node capture attacks.

Committee:

Dharma Agrawal, DSc (Committee Chair); Raj Bhatnagar, PhD (Committee Member); Carla Purdy, C, PhD (Committee Member)

Subjects:

Computer Science

Keywords:

security;wireless sensor networks;key-predistribution;wireless mesh networks;bi-variate polynomials

Zheng, ZizhanSparse Deployment of Large Scale Wireless Networks for Mobile Targets
Doctor of Philosophy, The Ohio State University, 2010, Computer Science and Engineering

Deploying wireless networks at large scale is challenging. Despite various effort made in the design of coverage schemes and deployment algorithms with static targets in mind, how to deploy a wireless network to achieve a desired quality of service for mobile targets moving in a large region without incurring prohibitive cost largely remains open. To address this issue, this dissertation proposes Sparse Coverage, a deployment scheme that provides guaranteed service to mobile targets while trading off service quality with cost in a deterministic way.

The first part of this dissertation discusses two sparse coverage models for deploying WiFi access points (APs) along a city-wide road network to provide data service to mobile vehicles. The first model, called Alpha Coverage, ensures that a vehicle moving through a path of length α is guaranteed to have a contact with some AP. This is the first partial coverage model (in contrast to the more expensive full coverage model) that provides a performance guarantee to disconnection-tolerant mobile users. We show that under this general definition, even to verify whether a given deployment provides Alpha Coverage is co-NPC. Thus, we propose two practical metrics as approximations, and design efficient approximation algorithms for each of them. The concept of Alpha Coverage is then extended by taking connectivity into account. To characterize the performance of a roadside WiFi network more accurately, we propose the second sparse coverage model, called Contact Opportunity, which measures the fraction of distance or time that a mobile user is in contact with some AP. We present an efficient deployment method that maximizes the worst-case contact opportunity under a budget constraint by exploiting submodular optimization techniques. We further extend this notion to the more intuitive metric -- average throughput -- by taking various uncertainties involved in the system into account.

The second part of this dissertation studies sparse deployment techniques for placing sensor nodes in a large 2-d region for tracking movements. We propose a sparse coverage model called Trap Coverage, which provides a bound on the largest gap that a mobile target, e.g., an intruder or a dynamic event, is missed by any sensor node. In contrast to the current probabilistic partial coverage models, this is the first 2-d coverage model that can trade off the quality of tracking with network lifetime in a deterministic way. For an arbitrarily deployed sensor network, we propose efficient algorithms for determining the level of Trap Coverage even if the sensing regions have non-convex or uncertain boundaries. We then discuss a roadmap assisted geographic routing protocol to support efficient pairwise routing in large sensor networks with holes, which embodies a novel hole approximation technique and makes desired tradeoff between route-stretch and control overhead.

Committee:

Prasun Sinha (Advisor); Ness Shroff (Committee Member); Yusu Wang (Committee Member)

Subjects:

Computer Science

Keywords:

Wireless networks; sensor networks; coverage; sparse coverage; approximation algorithms

JAIN, NITINMULTICHANNEL CSMA PROTOCOLS FOR AD HOC NETWORKS
MS, University of Cincinnati, 2001, Engineering : Computer Science and Engineering
An ad hoc network is a collection of wireless mobile nodes dynamically forming a network without the use of any existing stationary network infrastructure. The network can be multi-hop and mobile; there is no central controller and packet transmissions are typically unsynchronized. The efficiency of the medium access control (MAC) protocol to coordinate the access to the shared radio medium is critical. Carrier sense multiple access (CSMA) protocols are typically used. However, their efficiency is limited when the load and the level of contention is high. This thesis proposes use of multichannel CSMA protocols to reduce contention on the wireless medium. Though the aggregate capacity with a multichannel scheme is the same as a single channel, contention per channel is now lower and thus channel access is more efficient. We show that only a handful of channels provide optimum performance as with too many channels per-channel bandwidth is too low that affects performance adversely. Since the number of channels are much lower than the number of nodes, effective channel selection schemes are needed. We propose a receiver-based channel selection (RBCS) scheme that selects channel based on interference levels on different channels at the receiver. We implement this technique as an extension of IEEE standard 802.11 MAC protocol (which is a single channel protocol) on a network simulator. We show that it provides superior delay performance at high loads compared to single channel, as well as other, previously studied, channel selection schemes, such as selcting a random free channel or selecting channel based on sender-side signal power. As a final contribution, we study the effect of multichannel CSMA protocols for multipath routing on ad hoc networks. With use of single channel CSMA, ''route coupling'' can exist for multipath routes. This means that routes can form in radio neighborhood, and their transmissions can interfere with each other, preventing multiple routes to be used concurrently. Thus, the load balancing advantages of multiple paths are lost. We show that the use of multichannel CSMA protocols as above can remarkably improve the effectiveness of the multipath routing by providing more diversity.

Committee:

Dr. Samir R. Das (Advisor)

Subjects:

Computer Science

Keywords:

Ad Hoc Networks; medium access control; multichannel MAC; wireless networks; channel selection techniques

Afifi, Mohammed Ahmed Melegy MohammedTCP FTAT (Fast Transmit Adaptive Transmission): A New End-To- End Congestion Control Algorithm
Master of Science in Electrical Engineering, Cleveland State University, 2014, Washkewicz College of Engineering
Congestion Control in TCP is the algorithm that controls allocation of network resources for a number of competing users sharing a network. The nature of computer networks, which can be described from the TCP protocol perspective as unknown resources for unknown traffic of users, means that the functionality of the congestion control algorithm in TCP requires explicit feedback from the network on which it operates. Unfortunately this is not the way it works with TCP, as one of the fundamental principles of the TCP protocol is to be end-to-end, in order to be able to operate on any network, which can consist of hundreds of routers and hundreds of links with varying bandwidth and capacities. This fact requires the Congestion Control algorithm to be adaptive by nature, to adapt to the network environment under any given circumstances and to obtain the required feedback implicitly through observation and measurements. In this thesis we propose a new TCP end-to-end congestion control algorithm that provides performance improvements over existing TCP congestion control algorithms in computer networks in general, and an even greater improvement in wireless and/or high bandwidth- delay product networks.

Committee:

Nigamanth Sridhar, PhD (Committee Chair); Chansu Yu, PhD (Committee Member); Pong Chu, PhD (Committee Member)

Subjects:

Computer Engineering; Computer Science; Electrical Engineering

Keywords:

TCP; Congestion Control; Computer Networks; TCP NewReno; TCP Westwood; TCP Cubic; Linux TCP; ns-3; DCE Cradle; Direct Code Execution Cradle - ns-3; high bandwidth delay product networks; random loss radio signal; Adaptive Transmission

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