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  • 1. Basheer, Al-Qassab Reliability 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
  • 2. SEKHAR, SANDHYA A DISTANCE BASED SLEEP SCHEDULE ALGORITHM FOR ENHANCED LIFETIME OF HETEROGENEOUS WIRELESS SENSOR NETWORKS

    MS, University of Cincinnati, 2005, Engineering : Computer Engineering

    This thesis describes the concept of sensor networks which has been made viable by the convergence of MEMS system technology and efficient routing protocols. Sensor nodes possess finite, non-renewable energy that they expend in sensing a multitude of modalities including temperature, moisture, pressure, light and infrared radiation. A radio-interconnected collection of such sensors forms a sensor network and the information collected from the network is transmitted for analysis at a distant location termed as the sink. The main purpose of a sensor network is to gather information about the various parameters of the area in which it is deployed and to transmit this information to the sink for appropriate utilization. A wireless sensor node is capable of only a limited amount of communication and processing. Therefore, unlike traditional networks, where the objective is to maximize channel throughput, the chief consideration in a sensor network is to extend the system lifetime as well as system robustness. Wireless ad hoc and sensor networks are comprised of energy–constrained nodes. This limitation has led to the dire need for energy-aware protocols to produce an efficient network. Heterogeneity is introduced in a wireless sensor network by having a large number of low power sensor nodes and a small number of more powerful nodes to serve as cluster heads. We propose a self-tuning scheme that improves the lifetime of a heterogeneous wireless sensor network by appropriately scheduling the transmission rate of individual sensor nodes in the network. We consider a distance based sleep scheduling problem for equal energy consumption rates in low power sensor nodes and evaluate the optimal settings required in a heterogeneous sensor network. We evaluate the efficiency of our proposed algorithm based on an analytical model and perform simulations to verify the adequacy of our scheme in terms of important network parameters and compare with existing heterogeneous sensor netw (open full item for complete abstract)

    Committee: Dr. Dharma Agrawal (Advisor) Subjects: Computer Science
  • 3. Fan, Kai-Wei On Structure-less and Everlasting Data Collection in Wireless Sensor Networks

    Doctor of Philosophy, The Ohio State University, 2008, Computer and Information Science

    Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Prior works on data aggregation protocols have focused on tree-based or cluster-based structured approaches. Although structured approaches are suited for data gathering applications, they incur high maintenance overhead in dynamic scenarios for event-based applications. The goal of this dissertation is to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure.We propose the first structure-free data aggregation technique that achieves high efficiency. Based on this technique, we propose two semi-structured approaches to support scalability. We conduct large scale simulations and real experiments on a testbed to validate our design. The results show that our protocols can perform similar to an optimum structured approach which has global knowledge of the event and the network. In addition to conserving energy through efficient data aggregation, renewable energy sources are required for sensor networks to support everlasting monitoring services. Due to low recharging rates and the dynamics of renewable energy such as solar and wind power, providing data services without interruptions caused by battery runouts is non-trivial. Moreover, most environment monitoring applications require data collection from all nodes at a steady rate. The objective is to design a solution for fair and high throughput data extraction from all nodes in the network in presence of renewable energy sources. Specifically, we seek to compute the lexicographically maximum data collection rate for each node in the network, such that no node will ever run out of energy. We propose a centralized algorithm and an asynchronous distributed algorithm that can compute the optimal lexicographic rate assignment for all nodes. The centralized algorithm jointly computes the o (open full item for complete abstract)

    Committee: Prasun Sinha (Advisor); Anish Arora (Committee Member); David Lee (Committee Member) Subjects: Computer Science
  • 4. Schoenwald, Joshua Scenarios as a tool for collaborative envisioning /

    Master of Science, The Ohio State University, 2005, Graduate School

    Committee: Not Provided (Other) Subjects:
  • 5. Das, Tanmoy Exploiting Hidden Resources to Design Collision-Embracing Protocols for Emerging Wireless Networks

    Doctor of Philosophy, The Ohio State University, 2019, Computer Science and Engineering

    The explosive growth of the Internet, the advent of novel distributed applications, and an abundance of inexpensive hardware, have led to significant increases in the use of wireless networks. At present, different types of wireless networks are being used to support the requirements of several applications. WiFi networks are most widely used for universal access of the Internet. Vehicular networks that enable car-to-car communication have gained much attention because they can be utilized to develop a multitude of distributed applications to improve road safety and driving experience. Similarly, a dense deployment of inexpensive and battery-free (passive) radio frequency identification (RFID) tags is ideal for object tracking and monitoring in shopping malls and warehouses. Any wireless networks have to provide better performance when the number of users and applications increases rapidly. To meet this ever-increasing demand, we have proposed several protocols that utilize previously unused resources to gain additional information. Such information is beneficial for the design of collision-embracing protocols that allow simultaneous transmissions from multiple nodes for better resource utilization, resulting in improved performance. In our first work, a medium access control (MAC) protocol for WiFi networks, named BASIC, is devised. BASIC utilizes the high bandwidth Ethernet backbone networks that connect WiFi access points (APs). Multiple APs received packets from the same WiFi client, and several APs share this received signal among each other to maximize throughput from iia client. By working together, APs in enterprise WiFi networks can decode packets from several clients simultaneously, resulting in a considerable increment in the total throughput. As a continuation, a collision-embracing protocol, called CoReCast, is designed for vehicular networks and is suitable for broadcasting. CoReCast exploits the abundant power and the availabili (open full item for complete abstract)

    Committee: Prasun Sinha (Advisor); Rajiv Ramnath (Committee Member); Can Koksal (Committee Member); Brent Sohngen (Committee Member) Subjects: Computer Engineering; Computer Science
  • 6. Chen, Wei-Chuan A Multi-Channel, Impedance-Matching, Wireless, Passive Recorder for Medical Applications

    Doctor of Philosophy, The Ohio State University, 2019, Electrical and Computer Engineering

    This dissertation presents a new technology for batteryless and wireless neurorecording system which can be applied clinically. Two clinical issues of this type of neural implant are the 1) multichannel operation and 2) high impedance and DC voltage offset from the brain electrode impedance. To resolve these two problems, one wireless multichannel system and one brain electrode interface impedance-matching system are proposed respectively. To achieve multichannel operation, one photo-activated multiplexer is employed in the implant circuit. The interrogator additionally sends an infrared control signal for channel selection. Experimental results show that the proposed neuropotential recorder exhibits 20 uVpp sensitivity at all eight channels. The system is also in compliance with the strictest Federal Communications Commission standards for patient safety. Notably, the proposed approach is scalable to a much higher number of channels. On the other hand, to mitigate the high impedance and DC voltage offset of the brain-electrode interface, one self-biasing PNP Bipolar Junction Transistor (BJT) is adopted in the brain circuits. This self-biasing PNP BJT increases the overall system's impedance and maintains the system sensitivity while the high impedance is present. Measurement results demonstrate that emulated neuropotentials as low as 200 uVpp can be detected at a 33 kOhms electrode impedance. Together, these proposed techniques would lead the wireless neuro recorders to be applicable in real, in-vivo clinical applications.

    Committee: John L. Volakis (Advisor); Asimina Kiourti (Advisor); Liang Guo (Committee Member); Daniel Rivers (Committee Member) Subjects: Biomedical Engineering; Electrical Engineering; Electromagnetics
  • 7. Nanduri, Krishna Teja Energy Efficient Key Management in Wireless Sensor Networks using Multivariate Polynomials

    MS, University of Cincinnati, 2017, Engineering and Applied Science: Computer Science

    A Wireless Sensor Network (WSN) are a collection of tiny devices (Sensor Nodes SNs) that are deployed in an environment and with the help of in-built sensors and transceivers, record and collect data about the surroundings. All the data gathered by these devices is transmitted via other SNs using radio waves to a central base station (BS) which collects and processes the information. Such WSNs can be deployed anywhere with variable number of SNs depending on the requirement of the user. Appropriate transducers with the SNs can be used to gather information like environmental data, Health data, Temperature, Humidity, etc. They are usually deployed in areas that are hard to access or is access restricted. This means that the SNs must last long enough that the batteries on the SN or the SN itself need not be replaced too often. These networks usually have many constraints that limit how they function like Energy of nodes, security of transmissions, etc. Any wireless network being deployed must take these constraints into account and try to address them in their life cycle. Energy efficiency and Security are two most important aspects considered when deploying a wireless network. Energy efficiency is critical as the SNs must last long period of time and Security is favored as most of the transmitted data in the network is private. In this thesis, we discuss various security methods employed in the management of keys like the asymmetric, symmetric and Hybrid Key management systems. We go into detail about the various polynomial based key management systems and how they help improve the security of a network. We describe various existing polynomial based schemes that exist today and explain their functionality. We also describe what hashing algorithms are and show how they function. We then introduce a multivariate polynomial based scheme that we propose as a part of this thesis. We describe how the network is initiated, how the polynomial keys and variables a (open full item for complete abstract)

    Committee: Dharma Agrawal D.Sc. (Committee Chair); Yizong Cheng Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member) Subjects: Computer Science
  • 8. BANDEKAR, ASHUTOSH A Secure and Low-Power Consumption Communication Mechanism for IoT (Internet of Things) and Wireless Sensor Networks

    Master of Science, University of Toledo, 2017, Electrical Engineering

    Internet of Things (IoT), the newer generation of traditional wireless sensor network devices, offer wide variety of applications in various areas including military, medicine, home automation, remote monitoring, etc. Due to their wide usage and recent large-scale DDoS (distributed denial of service) attacks using millions of these devices, security of these devices have become an important aspect to address. Additionally, security implementation needs to be power efficient considering the limited power resource available to these wireless devices. Since users, as well as attackers, can control or access IoT devices remotely using smartphone or a computer, any attack on these devices can result in disasters. This thesis is directed towards development and implementation of a secure and power-efficient communication mechanism on these low-power devices. First, we performed a detailed analysis of the power consumption of these devices for different environment variables including temperature, lighting and location (in/outdoor), to understand effects of these parameters on device power consumption. Second, we proposed and implemented a novel security algorithm to detect and mitigate RPL (routing protocol layer) attacks in IoT networks. We evaluated changes in the behavior of IoT devices before and after the implementation of our proposed algorithm in terms of the change in battery life and power consumption. The proposed security implementation has the novel approach of using the RSSI (received signal strength indicator) tunneling to detect and mitigate RPL (routing protocol layer) attacks. Finally, we conducted experiments in simulation as well as on first generation real-world sensor nodes (Zolertia Z1 motes) to evaluate the power efficiency of our proposed algorithm. We conclude the thesis with insights on (a) the effect of interference present in the atmosphere on battery life, (b) security provided by the proposed algorithm, and (c) power-efficiency of the propos (open full item for complete abstract)

    Committee: Ahmad Javaid (Committee Chair); Alam Mansoor (Committee Member); Hond Wang (Committee Member) Subjects: Computer Science
  • 9. Adamek, Jordan Concurrent Geometric Routing

    PHD, Kent State University, 2017, College of Arts and Sciences / Department of Computer Science

    Geometric routing is navigating the message on the basis of node coordinates. In ad hoc wireless networks, geometric routing allows stateless message navigation with minimum routing tables and constant message size. Concurrent geometric routing improves message delivery latency and reliability at the expense of greater message cost. In this dissertation, we study concurrent geometric routing solutions to geocasting, multicasting and multisource broadcast. For each solution, we present theoretical performance bounds as well as performance comparison through abstract and concrete simulation.

    Committee: Mikhail Nesterenko (Advisor); Gokarna Sharma (Committee Member); Hassan Peyravi (Committee Member); Volodymyr Andriyevskyy (Committee Member); Andrew Tonge (Committee Member) Subjects: Computer Science
  • 10. Howard, Shaun Deep Learning for Sensor Fusion

    Master of Sciences (Engineering), Case Western Reserve University, 2017, EECS - Computer and Information Sciences

    The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).

    Committee: Wyatt Newman Dr (Committee Chair); M. Cenk Cavusoglu Dr (Committee Member); Michael Lewicki Dr (Committee Member) Subjects: Artificial Intelligence; Computer Science
  • 11. Chowdhury, Tashnim Jabir Shovon A distributed cooperative algorithm for localization in wireless sensor networks using Gaussian mixture modeling

    Master of Science, University of Toledo, 2016, Electrical Engineering

    Wireless sensor networks are defined as spatially distributed autonomous sensors to monitor certain physical or environmental conditions like temperature, pressure, sound, etc. and incorporate the collected data to pass to a central location through a network. Multifarious applications including cyber-physical systems, military, eHealth, environmental monitoring, weather forecasting, etc. make localization a crucial part of wireless sensor networks. Since accuracy and low computational time of the localization, in case of some applications like emergency police or medical services, is very important, the main objective of any localization algorithm should be to attain more accurate and less time consuming scheme. This thesis presents a cooperative sensor network localization scheme that approximates measurement error statistics by Gaussian mixture. Expectation Maximization (EM) algorithm has been implemented to approximate maximum-likelihood estimator of the unknown sensor positions and Gaussian mixture model (GMM) parameters. To estimate the sensor positions we have adopted several algorithms including Broyden-Fletcher-Goldfarb-Shanno (BFGS) Quasi-Newton (QN), Davidon-Fletcher-Powell (DFP), and Cooperative Least Square (LS) algorithm. The distributive form of the algorithms meet the scalability requirements of sparse sensor networks. The algorithms have been analyzed for different number of network sizes. Cramer Rao Lower Bound (CRLB) has been presented and utilized to evaluate the performance of the algorithms. Through Monte Carlo simulation we show the superior performance of BFGS-QN over DFP and cooperative LS in terms of localization accuracy. Moreover the results demonstrate that Root Mean Square Error (RMSE) of BFGS-QN is closer to derived CRLB than both DFP and cooperative LS.

    Committee: Jared Oluoch (Committee Chair); Vijay Devabhaktuni (Committee Co-Chair); Junghwan Kim (Committee Member) Subjects: Electrical Engineering
  • 12. Chakraborty, Suryadip Data Aggregation in Healthcare Applications and BIGDATA set in a FOG based Cloud System

    PhD, University of Cincinnati, 2016, Engineering and Applied Science: Computer Science and Engineering

    The Wireless Body Area Sensor Network (WBASN) is a wireless network of wearable computing devices including few medical body sensors which capture and transmit different physiological data wirelessly to a monitoring base station. When a physiological sensor continuously senses and generates huge amount of data, the network might become congested due to heavy traffic and it might lead to starvation and ineffectiveness of the WBASN system. This had led to the beginning of our first problem in this research which is the use of aggregation of data so as to reduce the traffic, enhancing the network life time, and saving the network energy. This research also focuses on dealing with huge amount of healthcare data which is widely known today as `BIGDATA'. Our research investigates the use of BIGDATA and ways to analyze them using a cloud based architecture that we have proposed as FOG Networks which improves the use of cloud architecture. During the work of data aggregation, we propose to use of the statistical regression polynomial of the order 4, and 8. Due to computation, we performed the 6th order coefficient computation and analyzed our results with real-time patient data with compression ratio and correlation coefficients. We also focus on studying the energy saving scenarios using our method and investigate how the node failure scheme would be handled. While focusing on building a polynomial based data aggregation approach in the WBASN system which involves summing and aggregating of wireless body sensors data of the patient's, we noticed the problem of dealing with thousand and millions of patients data when we run a WBASN system for continuous monitoring purpose. We could not also deal with such big amount of data in the small storage of the physiological sensors with small computation abilities of them. So, there is an immediate necessity of an architecture and tools to deal with these thousands of data commonly known today as the BIGDATA. To analyze the (open full item for complete abstract)

    Committee: Dharma Agrawal D.Sc. (Committee Chair); Amit Bhattacharya Ph.D. (Committee Member); Rui Dai Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science
  • 13. Jamthe, Anagha Mitigating interference in Wireless Body Area Networks and harnessing big data for healthcare

    PhD, University of Cincinnati, 2015, Engineering and Applied Science: Computer Science and Engineering

    Wireless Body Area Network (WBAN) has become an important field of research that could provide cost effective solution for ubiquitous health care monitoring of human body. In recent past, it has attracted attention from several researchers due to its potential applications in various disciplines including health care, sports-medicine, entertainment, etc. It is rapidly replacing wired counterparts due to its several attractive features such as light-weight easy portability, support for real time remote monitoring, ease of use, etc. Users of WBANs are increasing exponentially as more people are embracing wearable monitoring devices for numerous health care causes. Interference is considered to be one of the major issues in WBANs, which arises primarily due to close proximity of other WBANs, random human mobility, and distributed nature of people carrying WBANs. Coexisting WBANs have great chance of interference, which might degrade the network performance. If left unchecked, interference can cause serious threat to reliable operation of the network. It could cause to loss of critical medical data of patients, which might even prove to be life threatening. The primary motivation behind this dissertation is to avoid such a situation by using various interference mitigation techniques. Graceful coexistence could be ensured by scheduling the transmissions between co-existing WBANs. MAC layer is responsible for scheduling data transmissions and coordinating nodes' channel access that avoids possible collisions during data transmissions. In this dissertation, we have attempted to address intra-WBAN and inter-WBAN interference issues. We model a fuzzy logic based inference engine to make decisions while scheduling transmissions in isolated WBANs. For coexisting WBANs, due to its distributed nature and lack of central coordinator, we propose a QoS based MAC scheduling approach that avoids inter-WBAN interference. Our proposed MAC scheduling scheme can be used for im (open full item for complete abstract)

    Committee: Dharma Agrawal D.Sc. (Committee Chair); Richard Beck Ph.D. (Committee Member); Prabir Bhattacharya Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member); Wen-Ben Jone Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science
  • 14. Mishra, Amitabh Modeling and Performance Evaluation of Wireless Body Area Networks for Healthcare Applications

    PhD, University of Cincinnati, 2015, Engineering and Applied Science: Computer Science and Engineering

    Wireless Body Area Network (WBAN) is a low-power Personal Area Network involving sensor nodes (SNs) that sense and relay physiological data to a central station. WBANs are new and still evolving. We try to address three open research areas involving WBANs. The limited energy budget in WBANs necessitates energy conservation to prolong the network lifetime. The first challenge we try to address is related to improvement of the lifetime of a WBAN, given the small sizes of body sensor nodes (SNs) and the limited battery power that they run on. We proposed a dual-prediction framework for improvement of network lifetime. The framework allows for minimizing data transmission involving four important body parameters by reconstructing their information by time series prediction at reception. A sample elimination algorithm further optimizes the framework performance. We enhanced the framework by reducing the sampling frequency and implementing the algorithm on top, increasing the network lifetime further. The missing samples were reconstructed by interpolation at the receiver. We probed the effects of adaptive sampling and evaluated the increase in battery lifetime in WBANs. We then tried to test the behavior of a WBAN in the presence of other WBANs around it and check the issues faced by WBANs. Wireless systems can face severe interference problems if they use the same communication channels at a time. There are issues related to data routing because the critical nature of WBAN data requires assured communication of body data. For optimum network utilization, efficient scheduling of transmissions in multiple co-existing WBANs is important in order to avoid intra and inter-WBAN interference and for a graceful coexistence. We propose that inter-WBAN interference can be avoided by a QoS based MAC scheduling approach and that intra-WBANs interference can be circumvented by fuzzy scheduling of intra-WBAN transmissions. We also propose to use interference to the benefit of WBA (open full item for complete abstract)

    Committee: Dharma Agrawal D.Sc. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Prabir Bhattacharya Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member) Subjects: Computer Science
  • 15. Elkin, Colin Development of Novel Computational Algorithms for Localization in Wireless Sensor Networks through Incorporation of Dempster-Shafer Evidence Theory

    Master of Science, University of Toledo, 2015, Engineering (Computer Science)

    Wireless sensor networks are a collection of small, disposable, low-power devices that monitor vital sensory data for a variety of civil, military, and navigational applications. For instance, some cities have a network of emergency phones scattered across walkways so that citizens in distress can immediately reach emergency services. Using effective localization techniques that are both highly accurate and of low computational cost, 911 services can dispatch police, fire, or medical services to a caller's location as quickly as humanly possible. Hence, from the standpoint of locating a node in a network, every percent of accuracy achieved and every second of time saved can be the difference between life and death. This thesis presents two novel algorithms for wireless sensor network localization through the incorporation of Dempster-Shafer Evidence Theory. The first technique follows a verbose methodology for node positioning that fuses multiple types of signal measurements, such as received signal strength and angle of arrival, and utilizes the expected value property of DS Theory to geo-locate a node with a moderate accuracy of 78-87%, thereby providing an introductory approach to the previously untapped fusion of WSN localization and DS Theory. The second approach consists of a low cost, highly accurate data fusion technique that incorporates the plausibility property of DS Theory to establish a high level of accuracy. Due to this unique approach to data fusion and predictive data modelling, this second algorithm achieves an optimal accuracy range of 83-98% in a flexible multitude of simulation scenarios at a fraction of the runtime required under prior established localization techniques. Overall, these two algorithms provide a groundbreaking new application of Dempster-Shafer Theory as well as fast, accurate, and informative new approaches to wireless sensor network localization that can improve a wide range of vital applications.

    Committee: Vijay Devabhaktuni PhD (Committee Chair); Mansoor Alam PhD (Committee Member); Richard Molyet PhD (Committee Member); Hong Wang PhD (Committee Member) Subjects: Computer Science; Electrical Engineering
  • 16. Rettig, Andrew Design and Implementation of Affordable, Self-Documenting, Near-Real-Time Geospatial Sensor Webs for Environmental Monitoring using International Standards

    PhD, University of Cincinnati, 2014, Arts and Sciences: Geography

    This dissertation documents the design and implementation of a near-real-time geospatial in situ sensor network for monitoring stormwater runoff at the Green Learning Station. The project solves the need by the Environmental Protection Agency and Cincinnati Metropolitan Sewer District for an affordable and standardized network. The project also makes contributions to geospatial standards and sensor web research. This dissertation uses open innovation, including open standards, to help reduce cost and complexities of environmental sensor networking architectures. Article 1 focuses on the technical implementation of the in situ sensor network helping to fill the research gap of applied end-to-end in situ sensing. This gap is further highlighted by the inadequacies within international geospatial standards. Article 2 discusses the greatest hardware challenge within sensor webs, embedded devices. The Green Learning Station project solved this challenge, bridged the gap between sensor protocols and standard communication protocols, with Common-off-the-Shelf (COTS) routers. The modified routers enable the development of the client/server architecture for environmental sensor networking outlined in Article 3. The client software is designed for embedded devices while the web services were designed with the Representational State Transfer (REST) approach. The Green Learning Station design and implementation is unique because of the open innovation approach to geospatial in situ sensor webs by a team of engineers with expertise at every layer of the architecture. This expertise enabled the inclusion of spatial standards and spatial data throughout the architecture. This approach creates a standardized and affordable geospatial sensor network as an example for others to study and expand upon for a variety of monitoring solutions.

    Committee: Richard Beck Ph.D. (Committee Chair); Dharma Agrawal D.Sc. (Committee Member); Ishi Buffam Ph.D. (Committee Member); Hongxing Liu Ph.D. (Committee Member); Michael Widener Ph.D. (Committee Member) Subjects: Geographic Information Science
  • 17. Kumarasiri, Nuwan Development of Novel Algorithms for Localization in Wireless Sensor Networks

    Master of Science, University of Toledo, 2014, Engineering (Computer Science)

    Highly accurate localization in wireless sensor networks (WSNs) has been considered as one of the most significant challenges in wireless sensor networks. Significant efforts have been made in order to uplift the solutions to this challenging problem as, localization of a signal source in a wireless sensor network is now appealing for a range of real life applications, including emergency services, navigational systems, and civil/military surveillance. For instance, a couple of seconds of delay in identifying a location of an injured victim could create life threatening situations. During the last few years, several techniques have been proposed to provide an accurate estimation of the location of an unknown sensor node. Received-signal-strength (RSS), angle-of-arrival (AOA), time-difference-of-arrival (TDOA) and time-of-arrival (TOA) to name a few. While these techniques are quick to produce fairly accurate location estimation, they suffer effects from non-line-of-site (NLOS) conditions, unavailability of one or more sensors, or the requirement of expensive receivers, all of which would lead to poor or no location estimation at all. Motivated by the above observations, this thesis aims to develop two novel localization algorithms for localization in WSNs. Furthermore, it suggests to use Dempster-Shafter theory as an efficient tool for localization purposes in WSNs. In this thesis two new localization schemes are proposed. One proposed algorithm for localization in WSNs simultaneously exploits received signal strength (RSS) and time difference of arrival (TDOA) measurements. The accuracy and convergence reliability of the proposed hybrid scheme is also enhanced by incorporating RSS measurements from Wi-Fi networks via cooperative communications between Wi-Fi and sensor networks. Simulation results show that the proposed hybrid positioning approach significantly outperforms each individual method. The advantages of the proposed scheme, which include providing (open full item for complete abstract)

    Committee: Vijay Devabhaktuni Dr. (Committee Chair); Nghi Tran Dr. (Committee Co-Chair); Mansoor Alam Dr. (Committee Member); Robert Green Dr. (Committee Member); Weiqing Sun Dr. (Committee Member) Subjects: Communication; Electrical Engineering
  • 18. Raju, Madhanmohan Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processing

    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
  • 19. Henson, Cory A Semantics-based Approach to Machine Perception

    Doctor of Philosophy (PhD), Wright State University, 2013, Computer Science and Engineering PhD

    Machine perception can be formalized using semantic web technologies in order to derive abstractions from sensor data using background knowledge on the Web, and efficiently executed on resource-constrained devices. Advances in sensing technology hold the promise to revolutionize our ability to observe and understand the world around us. Yet the gap between observation and understanding is vast. As sensors are becoming more advanced and cost-effective, the result is an avalanche of data of high volume, velocity, and of varied type, leading to the problem of too much data and not enough knowledge (i.e., insights leading to actions). Current estimates predict over 50 billion sensors connected to the Web by 2020. While the challenge of data deluge is formidable, a resolution has profound implications. The ability to translate low-level data into high-level abstractions closer to human understanding and decision-making has the potential to disrupt data-driven interdisciplinary sciences, such as environmental science, healthcare, and bioinformatics, as well as enable other emerging technologies, such as the Internet of Things. The ability to make sense of sensory input is called perception; and while people are able to perceive their environment almost instantaneously, and seemingly without effort, machines continue to struggle with the task. Machine perception is a hard problem in computer science, with many fundamental issues that are yet to be adequately addressed, including: (a) annotation of sensor data, (b) interpretation of sensor data, and (c) efficient implementation and execution. This dissertation presents a semantics-based machine perception framework to address these issues. The tangible primary contributions created to support the thesis of this dissertation include the development of a Semantic Sensor Observation Service (SemSOS) for accessing and querying sensor data on the Web, an ontology of perception (Intellego) that provides a formal semanti (open full item for complete abstract)

    Committee: Amit Sheth Ph.D. (Advisor); Krishnaprasad Thirunarayan, Ph.D. (Committee Member); Payam Barnaghi Ph.D. (Committee Member); Satya Sahoo Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Artificial Intelligence; Computer Science; Information Science
  • 20. Xie, Qing Yan K-Centers Dynamic Clustering Algorithms and Applications

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

    Every day large and increasing amounts of unstructured information are created, putting ever more demands on retrieval methods, classification, automatic data analysis and management. Clustering is an important and efficient way for organizing and analyzing information and data. One of the most widely used dynamic clustering algorithms is K-Means clustering. This dissertation presents our K-Centers Min-Max dynamic clustering algorithm (KCMM) and K-Centers Mean-shift Reverse Mean-shift dynamic clustering algorithm (KCMRM). These algorithms are designed to modify K-Means in order to achieve improved performance and help with specific goals in certain domains. These two algorithms can be applied to many fields such as wireless sensor networks, server or facility location optimization, and molecular networks. Their application in wireless sensor networks are described in this dissertation. The K-Centers Min-Max clustering algorithm uses a smallest enclosing disk/sphere algorithm to attain a minimum of the maximum distance between a cluster node and data nodes. Our approach results in fewer iterations, and shorter maximum intra-cluster distances than the standard K-Means clustering algorithm with either uniform distribution or normal distribution. Most notably, it can achieve much better performance when the size of clusters is large, or when the clusters includes large numbers of member nodes in normal distribution. The K-Centers Mean-shift Reverse Mean-shift clustering algorithm is proposed to solve the "empty cluster" problem which is caused by random deployment. It employs a Gaussian function as a kernel function, discovers the relationship between mean shift and gradient ascent on the estimated density surface, and iteratively moves cluster nodes away from their weighted means. This results in cluster nodes which better accommodate the distribution of data nodes. The K-Centers Mean-shift Reverse Mean-shift algorithm can not only reduce the number of empty clu (open full item for complete abstract)

    Committee: Yizong Cheng Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Wen Ben Jone Ph.D. (Committee Member); Anca Ralescu Ph.D. (Committee Member); Xuefu Zhou Ph.D. (Committee Member) Subjects: Computer Science