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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A sensor network serves as a vital source for collecting raw sensory data. Sensor data are later processed, analyzed, visualized, and reasoned over with the help of several decision making tools. A decision making process can be disastrously misled by a small portion of anomalous sensor readings. Therefore, there has been a vast demand for mechanisms that identify and then eliminate such anomalies in order to ensure the quality, integrity, and/or trustworthiness of the raw sensory data before they can even be interpreted. Prior to identifying anomalies, it is essential to understand the various anomalous behaviors prevalent in a sensor network deployment. Therefore, we begin this work by providing a comprehensive study of anomalies that exist in a sensor network deployment, or are likely to exist in future deployments. After this thorough systematic analysis, we identify those anomalies that, in fact, hinder the quality and/or trustworthiness of the collected sensor data. One approach towards the reduction of the negative impact of misleading sensor readings is to perform off-line analysis after storing a large amount of sensor data into a centralized database. To this end, in this work, we propose an off-line abnormal node detection mechanism rooted in machine learning and data mining. Our proposed mechanism achieves high detection accuracy with low false positives. The major disadvantage of a centralized architecture is the tremendous amount of energy wasted while communicating the sensor readings. Therefore, we further propose an on-line distributed anomaly detection framework that is capable of accurately and rapidly identifying data-centric anomalies in-network, while at the same time maintaining a low energy profile. Unlike previous approaches, our proposed framework utilizes a very small amount of data memory through on-line extraction of few statistical features over the sensor data stream. In addition, previous detection mechanisms leverage sensor (open full item for complete abstract)

Committee: Bin Wang Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); Keke Chen Ph.D. (Committee Member); Shu Schiller Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science in Mathematics, Youngstown State University, 2009, Department of Mathematics and Statistics

Wireless sensor nodes generally face serious limitations in terms of computational power, energy supply, and network bandwidth. One of the biggest challenges faced by researches today is to provide effective and secure techniques for establishing cryptographic keys between wireless sensor networks. Public-key algorithms (such as the Diffie-Hellman key-exchange protocol) generally have high energy requirements because they require computational expensive operations. So far, due to the limited computation power of the wireless sensor devices, the Diffie-Hellman protocol is considered to be beyond the capabilities of today's sensor networks. We analyzed existing methods of implementing Diffie-Hellman and proposed a new improved method of implementing the Diffie-Hellman key-exchange protocol for establishing secure keys between wireless sensor nodes. We also provide an easy-to-use implementation of the Elliptic Curve Diffie-Hellman key-exchange protocol for use in wireless sensor networks.

Committee: Graciela Perera PhD (Advisor); John Sullins PhD (Committee Member); Jamal Tartir PhD (Committee Member) Subjects: Communication; Computer Science; Information Systems; Mathematics

MS, University of Cincinnati, 2001, Engineering : Computer Science

Wireless sensor networks are expected to find wide applicability and increasing deployment in coming years, as they enable reliable monitoring and analysis of the environment. Users of such a system will expect to get a real time warning when time-critical situations occur in the network and also to be able to retrieve any required information by issuing queries to the network. In this thesis, we propose a formal classification of sensor networks, based on their mode of functioning, as proactive and reactive networks. In proactive networks data is collected at pre-specified, fixed intervals while reactive strategy requires the nodes to respond immediately ton changes in the relevant parameters of interest. We are able to combine the best features of proactive and reactive networks while minimizing their limitations to create a new type of network called a Hybrid network which not only reacts to time-critical situations, but also gives an overall picture of the network at periodic intervals in a very energy efficient manner. We introduce two new energy efficient protocols, TEEN (Threshold-sensitive Energy Efficient sensor Network protocol) for reactive networks and APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network protocol) for hybrid networks. We have also proposed a third protocol for routing queries in hybrid networks. This protocol provides the user, flexibility to request either past, present or future data from the network in the form of historical, one-time and persistent queries respectively. We have also analytically determined the delay incurred in handling the various types of queries. To our knowledge, such an analytical modeling has been done for the first time for sensor network queries. These three protocols offer versatility to the users while consuming energy very efficiently by minimizing non-critical data transmissions. We evaluated the performance of these protocols for a simple temperature sensing application with a Po (open full item for complete abstract)

Committee: Dr. Dharma Agrawal (Advisor) Subjects: Computer Science

MS, University of Cincinnati, 2012, Engineering and Applied Science: Computer Science

Reliability and fault tolerance are an important part of data gathering at a base station (sink) in a wireless sensor network. Due to the uncertain environments of the network it becomes necessary to have robustness and information security integrated into the routing schemes. Multipath routing using independent trees is an eective way to achieve it. In real world applications, every node of the network does not have homogeneous connectivity to the base station. A more practical approach and a recent research area is having heterogeneous node connectivity with independent trees which are not necessarily spanning. A recent algorithm, referred to as DAG Independent Trees that gives vertex independent trees in heterogeneously connected directed acyclic graphs (DAGs) is discussed. The problem of realizing the vertex connectivity in independent trees is NP-complete. Therefore, an experimental analysis of the connectivity realized with independent trees given by the algorithm, and vertex connectivity from the nodes to sink is given. The results show that the algorithm nearly satises the condition that all nodes realize at least half the connectivity. A close approximation to the connectivity at more than half the nodes is achieved. In the case, when the algorithm does less than half the connectivity for most of the nodes, an optimization of the algorithm is given. The optimization over all permutations of the sink nodes is presented in the case when it does not fair well.

Committee: Kenneth Berman PhD (Committee Chair); Fred Annexstein PhD (Committee Member); Anca Ralescu PhD (Committee Member) Subjects: Computer Science

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

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

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