Master of Science in Engineering, University of Akron, 2020, Electrical Engineering

This thesis proposes a reinforcement learning (RL) driven software-defined networking (SDN) routing scheme for the situation-awareness and intelligent networking management. Firstly, the existing SDN network monitoring technique is applied to track the quality of service (QoS) parameters (link delay and packet loss). Afterward, the QoS data are fed to the RL algorithm in order to achieve situation awareness in SDN routing. The performance of the proposed RL-enabled routing scheme is evaluated in the simulation section by considering various network scenarios, including network congestion. Finally, the end-to-end delay, the episode reward, and the probability of path selection are recorded for each case. According to the outcomes, the proposed scheme intelligently select the efficient data path according to the current state of the network. Moreover, the end-to-end delay is compared with the Dijkstra algorithm, demonstrating the superiority of RL-enabled dynamic routing strategy over static. Additionally, the scalability of the algorithm is tested with multiple controller SDN.

Committee: Jin Wei-Kocsis (Advisor); Kye-Shin Lee (Committee Member); Hamid Bahrami (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering

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

PHD, Kent State University, 2018, College of Arts and Sciences / Department of Computer Science

Social Routing in Online Social Networks (OSNs) is very challenging, as it must handle privacy and performance. This study proposes a Social Online Routing (SOR) protocol for OSNs that satisfies Stratified Privacy Model (SPM) core requirements and minimizes end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM. SOR uses five messages (I-need Message, I-have Message, I-thank Message, I-like/dislike message, and the I-Ack Message) for carrying routing information. Forwarding models (I-need Module, I-have Module, I-thank Module, and I-ack Module) and routing algorithms (Topology aware Shortest-Path-Based routing algorithm, Social-Priority-Based routing algorithm, and Queue-aware Social-Priority-Based routing algorithm) are introduced. Four anonymization techniques are also utilized for stratified privacy. To evaluate the study's proposed protocol, an Online Social Networks Simulator is designed and implemented. Using real datasets from Google Plus, the simulator is used to evaluate end-to-end routing delays corresponding to the social routing information elements exchanged under the SPM.

Committee: Javed Khan Prof. (Advisor) Subjects: Computer Science

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

This research focuses on solving the min-max Multi Depot Vehicle Routing Problem (MDVRP) based on a swarm intelligence based algorithm called ant colony optimization. A traditional MDVRP tries to minimize the total distance travelled by all the vehicles to all customer locations. The min-max MDVRP, on the other hand, tries to minimize the maximum distance travelled by any vehicle. The algorithm developed is an extension of Single Depot Vehicle Routing Problem (SDVRP) algorithm developed by Bullnheimer et al. in 1997 based upon ant colony optimization. In SDVRP, all the vehicles start from a single depot and return to the same depot, and solution aims at finding tours of vehicles so that every customer location is visited exactly once and that minimizes the total distance travelled. Building upon the SDVRP algorithm, this study first involves developing an algorithm for the min-max variant of SDVRP problem where the maximum distance travelled by any vehicle is minimized. Later, the algorithm has been extended to address the Multi Depot variant of this problem. In this case, vehicles can start from multiple depots unlike SDVRP case and have to come back to their respective depot of origin once they visit a set of customer locations. The min-max multi-depot vehicle routing problem involves minimizing the maximum distance travelled by any vehicle in case of vehicles starting from multiple depots and travelling to each customer location at least once. This problem is of specific significance in case of time critical applications such as emergency response in large-scale disasters, and server-client network latency. The proposed algorithm uses an equitable region partitioning approach aimed at assigning customer locations to depots so that MDVRP is reduced to SDVRP. A background study on swarm intelligence based optimization techniques, region partitioning methods, approximation algorithms and also various techniques of optimization has been included in this research. T (open full item for complete abstract)

Committee: Manish Kumar PhD (Committee Chair); Sundararaman Anand PhD (Committee Member); Kelly Cohen PhD (Committee Member) Subjects: Mechanics

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

This dissertation addresses energy-efficient operations for a fleet of diverse electrified vehicles at two system levels, the single-vehicle powertrain system, and the multi-vehicle transportation system, contributing to both with optimal control- and heuristic-based integrative approaches. At the single vehicle powertrain level, an electrified powertrain exhibits a continuum of complexities: mechanical, thermal, and electrical systems with nonlinear, switched, multi-timescale dynamics; algebraic and combinatorial path constraints relating a mix of integer- and real-valued variables. For optimal energy management of such powertrains, “PS3” is proposed, which is a three-step numerical optimization algorithm based on pseudo-spectral collocation theory. Its feasibility, convergence, and optimality properties are presented. Simulation experiments using PS3 on increasingly complex problems are benchmarked with Dynamic Programming (DP). As problem size increases, PS3's computation time does not scale up exponentially like that of DP. Thereafter, PS3 is applied to a comprehensive 13-state 4-control energy management problem. It saves up to 6% energy demand, 2% fuel consumption, and 18% NOx emissions compared to coarsely-modeled DP baseline. For generalizability, parallel and series electrified powertrain architectures running various urban delivery truck drive cycles are considered with multi-objective cost functions, Pareto-optimal study, energy flow analyses, and warm versus cold aftertreatment-start transients. At the multi-vehicle fleet level, energy-efficient vehicle routing approaches lack in integrating optimal powertrain energy management solutions. Extending single vehicle PS3 algorithm for a multi-vehicle fleet of plug-in hybrid (PHEV), battery electric (BEV), and conventional engine (ICEV) vehicles, an integrative optimization framework to solve green vehicle routing with pickups and deliveries (PDP) is proposed. It minimizes the fleet energy consumption a (open full item for complete abstract)

Committee: Qadeer Ahmed Dr. (Advisor); Kiryung Lee Dr. (Committee Member); Joel Paulson Dr. (Committee Member); Giorgio Rizzoni Dr. (Committee Member) Subjects: Aerospace Engineering; Alternative Energy; Applied Mathematics; Artificial Intelligence; Automotive Engineering; Civil Engineering; Computer Science; Electrical Engineering; Engineering; Environmental Engineering; Geographic Information Science; Industrial Engineering; Information Systems; Information Technology; Mechanical Engineering; Naval Engineering; Ocean Engineering; Operations Research; Robotics; Sustainability; Systems Design; Transportation; Transportation Planning; Urban Planning

Master of Science (M.S.), University of Dayton, 2015, Electrical Engineering

The interest in specialized neuromorphic computing architectures has been increasing recently, and several applications have been shown to be capable of being accelerated on such platforms. This thesis describes the implementation of multicore digital neuromorphic processing systems on FPGAs. Static and Dynamic routing were used to allow communication between the cores on the FPGA. Several applications were mapped to the system including image edge detection, MNIST image classification, and biometric ECG classification. Given that all the applications were implemented on the same processor (hence same base Verilog code), with only a change in the synaptic weights and number of neurons utilized, the system has the capability to accelerate a broad range of applications.

Committee: Tarek Taha (Committee Chair); Vijayan Asari (Committee Member); Eric Balster (Committee Member) Subjects: Computer Engineering; Electrical Engineering

PhD, University of Cincinnati, 2008, Engineering : Computer Science and Engineering

Currently, Peer-to-Peer overlays can be classified into two main categories: unstructured and structured ones. Unstructured overlays are simple, robust, and powerful in keyword search. Structured ones can scale to very large systems in terms of node number and geography, and guarantee to locate an object within O(Log N) hops. However, both of them face difficulties in efficiency and security of overlays. For unstructured ones, the efficiency problem presented is poor scalability. For structured ones, it is long routing latency and enormous overhead on handling system churn. Moreover, both of them are vulnerable to malicious attacks. Peer-to-Peer overlays belong to application-level network. To a great extension, overlay network designs ignore physical characteristics. As the result, their structures are far from underlying physical network or the distribution pattern of overlay peers. These inconsistencies induce system common operations costly, such as routing and lookup. On the other hand, most peers are assumed to have uniform resources and similar behaviors. Thus, Peer-to-Peer protocols were designed to be symmetric. However, in the realistic environment, peers' resources and behaviors are highly skewed. Symmetric protocols actually compromise system performance. Frequently joining and leaving of peers generates enormous traffic. The significant fraction of peers with high latency/low bandwidth links increase lookup latency. Moreover, under the environment without mutual trust, Peer-to-Peer systems are very vulnerable for varied attacks because they lack a practical authentication mechanism. From a different perspective, this dissertation proposes to construct a highly efficient and secure Peer-to-Peer overlay based on the physical network structure of the Internet and network locality of overlay peers. By naturally integrating different network-aware techniques into the Peer-to-Peer overlay, a novel SNSA (Scalable Network Structure Aware) technique has been dev (open full item for complete abstract)

Committee: Dr. Yiming Hu (Advisor) Subjects: Computer Science

MS, University of Cincinnati, 2002, Engineering : Computer Science

A mobile ad hoc network is a collection of wireless mobile nodes dynamically forming a network without the use of any existing network infrastructure or centralized administration. As the nodes are mobile, the network topology is dynamic leading to frequent unpredictable connectivity changes. It is critical to route packets to destinations effectively without generating excessive overhead. This presents a challenging issue for protocol design since the protocol must adapt to frequently changing network topologies in a way that is transparent to the end user. A class of routing protocols called on-demand protocols has received a lot of interest because of their low routing overhead. In this thesis, we study techniques that can reduce this routing overhead even further. The on-demand protocols depend on query floods to discover routes whenever a new route is needed. Network-wide floods incur substantial overhead. Techniques have been proposed to contain the flood in a limited region where a route to the destination is highly likely to be found. Techniques have also been proposed to reduce redundant broadcasts. We propose various mechanisms to improve on these existing techniques. We propose adaptive mechanisms that utilize prior routing histories, mobility pattern and network load to choose the area in which the query flood should be contained. In addition, we propose a technique that utilizes the neighborhood information to reduce or eliminate redundant broadcasts. We evaluate their performances in isolation and in tandem. In the next part of the thesis, we turn our attention to use of location information for routing. In has been shown in prior work that availability of location information can substantially reduce routing overheads. However, equipping all mobile nodes with GPS or other positioning system is not a cost effective proposition. We develop and evaluate a localization technique that can localize mobile nodes even when only a fraction of nodes in the netw (open full item for complete abstract)

Committee: Dr. Samir Das (Advisor) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2006, Computer and Information Science

The lack of a basic understanding of its essential components has been an obstacle for reliable, efficient, and reusable messaging services in sensornets. To address this problem, this dissertation identifies the basic components of sensornet messaging and studies the related algorithmic design issues. More specifically, we propose the messaging architecture SMA that consists of three components: traffic-adaptive link estimation and routing (TLR), application-adaptive structuring (AST), and application-adaptive scheduling (ASC). TLR deals with dynamic wireless links as well as the impact of application traffic patterns on link dynamics; AST and ASC control the spatial and temporal flow of data packets to support application-specific in-network processing and QoS requirements. To provide an instance of TLR, we propose the routing protocol Learn-on-the-Fly that solves the problem of precisely estimating wireless link properties in the presence of varying network conditions. Then, we study ASC from the perspective of in-network processing and QoS provisioning. Taking packet packing as an example of in-network processing, we study the problem of scheduling packet transmissions to improve messaging efficiency. For the basic problem of reliable and real-time data transport in event-detection sensornets, we propose the protocol Reliable-Bursty-Convergecast that innovates the window-less block acknowledgment scheme and the retransmission-aware differentiated contention control. The architecture SMA provides a framework for sensornet messaging, and the study of TLR and ASC provides algorithmic references for instantiating SMA. This part of the dissertation work has also provided dependable messaging services for several real-world sensornet systems. The second part of this dissertation addresses the challenges that complex faults and large system scale bring to the design of fault-tolerant protocols. To this end, we propose the concept of “local stabilization“. In a locally- (open full item for complete abstract)

Committee: Anish Arora (Advisor) Subjects: Computer Science

Doctor of Philosophy, Case Western Reserve University, 2008, Computer Engineering

In this paper, I develop Directional Antenna Multi-path Location Aware Routing (DA-MLAR) that is a location aware routing with directional antenna capability. DA-MLAR is a reactive routing protocol that minimizes the protocol overhead of other reactive routing protocols. DA-MLAR also improves the packet delivery ratio and end-to-end delay. The long radio transmission range obtained using directional antenna can decrease the number of network partitions there by reducing the number of rebroadcasts. It also reduces the number of routing hops. The directionality further reduces the network interferences by directing the beam only towards the receiving node and involving few intermediate nodes that are in the direction of receiving node. Two extensions of DA-MLAR - DA-MLAR with on demand adjustment of transmission power (DA-MLAR-ODTP) and beam width (DA-MLAR-ODBW) are proposed which further improves the performance metrics of ad hoc networks. In the first phase, the adjustment is made based on the calculated distance between the current sending node and the receiving node in the network. In the second phase, the adjustment also incorporates the effect of random Received Signal Strength (RSS) environment. Multi-objective approach is adopted to assess the network performance of MANET with complex, competing and conflicting objectives – maximizing packet delivery ratio, minimizing protocol overhead, and minimizing energy consumption. The preference of objectives depends on the type of application. In space, energy consumption is given more preference than other objectives. I have used the Normalized Weighted Additive Utility Function (NWAUF) approach to obtain the best alternatives. Through simulations using ns-2, I have demonstrated that DA-MLAR exhibits better network performance. Some performance metrics like packet delivery ratio and end-to-end delay have been significantly improved using DA-MLAR-ODTP and DA-MLAR-ODBW with check in protocol overhead and energy consumpt (open full item for complete abstract)

Committee: Behnam Malakooti (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2024, Computer Science and Engineering

Wireless meshes offer a resilient and cost-effective framework where multi-hop communication occurs among mesh clients, routers, and gateways. In this framework, computing and communication are essential to guarantee capacity and achieve high performance in terms of network planning, frequency scheduling, and packet routing. In order to optimize the achievable capacity to provide high throughput-latency communication with scalability, adaptivity, and reliability against various network configurations and dynamics, my thesis studies a cross-layer approach, from network infrastructure development to communication schedule and routing path selection, to show that the problem complexity can be managed by decomposition into subproblems via approximation, even learning based solutions. My study in this thesis consists of several approaches: first, we focus on the middle-mile network optimization problem to provide broadband connectivity in rural regions with a theoretical upper bound of infrastructure cost. Second, we investigate channel hopping to achieve high spectrum utilization, interference avoidance, and jamming tolerance. Third, we look into capacity-aware routing using bounded exploration regions to high throughput and reliability with low overhead. Finally, by developing machine learning algorithms using domain knowledge of bounded exploration on the network routing problem, we study the generalizability of the learned routing policies to all uniform random graphs. The middle-mile network optimization is to connect the last-mile networks to the core network service providers with minimal infrastructure cost and throughput constraints. It includes topology construction, tower height assignment, antenna and orientation selection, as well as transmit power assignment, which is known to be a computationally hard problem. We propose the first polynomial time approximation solution for a generalized version of the middle-mile network optimization problem, wherein (open full item for complete abstract)

Committee: Anish Arora (Advisor); Shaileshh Bojja Venkatakrishnan (Committee Member); Kannan Athreya (Committee Member); Ness Shroff (Committee Member) Subjects: Computer Science

MS, University of Cincinnati, 2023, Engineering and Applied Science: Environmental Engineering

Most global rivers exhibit distinct seasonality, with periods each year of low flow and periods of high flow. Low flows are critical for urban water security, sufficient agricultural irrigation, satisfaction of water quality regulatory thresholds, successful navigation passage, and ecological well-being. In order to protect and control these low flow functions of rivers, people have for thousands of years built dams and artificial reservoirs. However, there is insufficient understanding of the impact of those dams and artificial reservoirs on the rivers they control, in part because we have inadequate records of the natural flow prior to the introduction of the water control infrastructure. This paper surveys the methods currently used in the United States and elsewhere to estimate the impact of water control infrastructure on low flow seasonality in major river systems. It then presents a case study application of one of those techniques for estimation of human impact on low flow, and describes the analytical innovations necessary to apply the technique in this case. The case study used is the Ohio River in the northeast United States, and the primary innovation necessary was the development of a parsimonious stream flow routing algorithm to accumulate approximately 30 years of algebraic estimates of "naturalized" flow from reservoirs operated by the USACE to locations of concern along the Ohio River main stem. This study shows that, in dry years, releases from USACE reservoirs during autumn months account for up to approximately half of the river flow at critical locations along the Ohio River main stem (meaning that USACE adds 100 percent again the daily volume of the natural stream flow). This has implications for dry season river functionality in all the categories listed above; if the infrastructure were to fail, these river functions would be threatened.

Committee: Patrick Ray Ph.D. (Committee Chair); Reza Soltanian Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member) Subjects: Environmental Engineering

Master of Science in Engineering, University of Akron, 2022, Electrical Engineering

A decentralized framework for network optimization is presented for wireless sensing nodes. The wireless sensing nodes use a dynamic programming algorithm to choose optimal routes for data transmission from any network node to a specialized ‘gateway' node that provides access to the wider internet. The dynamic programming algorithm is a variation of the Bellman-Ford algorithm and allows for the wireless sensing nodes to make decisions based on locally available network information, resulting in a decentralized routing algorithm. Routing decisions depend on the cost it takes to communicate from a node to a gateway, either directly or indirectly, using neighboring nodes as relay points. Nodes constantly share information with neighbors and when something effects the cost of a path, such as a node failure or the discovery of a less costly route, all nodes upstream along the existing path are made aware and re-route accordingly. A sample network is used to illustrate and verify the functionality of the proposed algorithm. The network and node decisions are simulated to show the evolution of the network routing decisions, and the simulation consistently shows the network converging to an optimal configuration. The speed of convergence depends on the order in which the nodes are assumed to attempt to establish and optimize their connections.

Committee: Robert Veillette (Advisor); Jose Alexis De Abreu Garcia (Committee Member); Nghi Tran (Committee Member) Subjects: Applied Mathematics; Computer Engineering; Computer Science; Electrical Engineering; Engineering

PHD, Kent State University, 2022, College of Arts and Sciences / Department of Computer Science

This dissertation has two contributions. The primary contribution is to present a trie based routing table lookup algorithm named CP-Trie. The secondary contribution is to present a high-level synthesis tool named C2RTL that can generate routing table lookup implementation in ASIC from C code. Routing table lookup is a key function of a router. It involves performing the longest prefix match (LPM). A router needs to perform a routing table lookup for each incoming packet. High-speed routers generally implement routing table lookup in Software and ASIC (Application Specific Integrated Circuit). This dissertation describes a new routing table algorithm named CP-Trie that outperforms the state-of-the-art trie based routing table lookup algorithm in lookup speed while consuming slightly more memory. We evaluated our algorithms with real routing tables from RouteView project. Our experiments with real routing tables from core routers show that CP-Trie achieves upto 1.43X lookup throughput on a general purpose CPU, but consumes 1.36-1.47X memory compared to the state-of-the-art solution. CP-Trie also outperforms the state-of-the-art solutions in ASIC. Implementing routing table lookup in ASIC is another challenge. The ASICs in high-speed routers are currently designed in a register transfer level (RTL) hardware description language (HDL) such as Verilog or VHDL. However, manually writing hardware logic is notoriously complicated and painful. This dissertation describes a high-level synthesis (HLS) tool named C2RTL that can generate Verilog RTL from C code. It takes a routing table lookup algorithm in C as an input and generates corresponding Verilog RTL code. We used C2RTL to generate the Verilog RTL implementation of CP-Trie. We then synthesized the generated RTL code with OpenROAD in a 1 GHz pipelined ASIC with a 45nm standard cell library. Our OpenROAD report shows that CP-Trie consumes 14% less power and 20.5% less area compared to the state-of-the-art solutio (open full item for complete abstract)

Committee: Javed Khan (Advisor); Hassan Peyravi (Committee Member); Mark Lewis (Other); Murali Shanker (Committee Member); Jong-Hoon Kim (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Wright State University, 2022, Computer Science and Engineering PhD

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting with TE and evaluating TE systems efficiently as this tool employs parallel processing to achieve high efficiency. The purpose of the simulator is two aspects: (1) We use it to understand traffic engineering, (2) we use it to formulate a new traffic engineering algorithm that is near-optimal and applicable in practice. We study the design of some important aspects of any TE system. In particular, the consequences of achieving optimal TE by solving the multi-commodity flow problem (MCF) and the consequences of choosing single-path routing over multi-path routing. With the help of the TE simulator, we compare many TE systems constructed by combining different paths selection techniques with two objective functions for rate adaptations: load balancing (LB) and average delay (AD). The results confirm that paths selected based on the theoretical approach known as Oblivious Routing combined with AD objective function can significantly increase the performance in terms of throughput, congestion, and delay.\par However, the new proposed system comes with a cost. The AD function has a higher complexity than the LB function. We show that this problem can be tackled by training deep learning models. We trained two models with two different neural network architectures: Mult (open full item for complete abstract)

Committee: Bin Wang Ph.D. (Advisor); Phu Phung Ph.D. (Committee Member); Krishnaprasad Thirunarayan Ph.D. (Committee Member); Yong Pei Ph.D. (Committee Member) Subjects: Computer Science

Master of Science in Cyber Security (M.S.C.S.), Wright State University, 2022, Computer Science

The current Routing Protocol for Low Power and Lossy Networks (RPL) standard provides three security modes Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and Authenticated Secure Mode (ASM). The PSM and ASM are designed to prevent external routing attacks and specific replay attacks through an optional replay protection mechanism. RPL's PSM mode does not support key replacement when a malicious party obtains the key via differential cryptanalysis since it considers the key to be provided to nodes during the configuration of the network. This thesis presents an approach to implementing a secure authenticated key exchange mechanism for RPL, which ensures the integrity and authentication of the received key while providing tamper-proof data communication for IoTs in insecure circumstances. Moreover, the proposed approach allows the key to be updated regularly, preventing an attacker from obtaining the key through differential cryptanalysis. However, it is observed that the proposed solution imposes an increase in the cost of communication, computation, power consumption, and memory usage for the network nodes.

Committee: Bin Wang Ph.D. (Advisor); Zhiqiang Wu Ph.D. (Committee Member); Meilin Liu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science, University of Toledo, 2021, Engineering (Computer Science)

With the rise of road congestion in modern cities, there are a lot of road traffic accidents. According to various studies, human drivers are at fault for over 90% of the accidents that occur on the road. Over the years, there have been huge advancements in the field of self-driving cars, also known as Autonomous Vehicles (AV), where there is little to no human involvement during driving. The main challenge AVs face is in their autonomy with the non-autonomous entities on the roads like pedestrians, other vehicles, and infrastructure. For a smooth road network, the vehicles communication is of major importance and the development of Vehicular Adhoc Network (VANET) is rapidly increasing. It is of utmost importance to create an efficient and secure routing protocol to route the safety messages within the vehicles in the network. In addition to this, it is also necessary to identify and prevent malicious messages in the network from interfering with effective and efficient communication. This thesis presents a brief survey on potential failures and attacks on AVs and VANETs. It performs a simulation study of VANET routing protocols: Optimized Link State Routing Protocol (OLSR), Ad-hoc On-demand Distance Vector (AODV), and Destination-Sequenced Distance-Vector Routing (DSDV) on a real-world map under different scenarios: low, medium, and high-density network. The results show OLSR performing better in delivering the basic safety messages within the network, and AODV performing better in terms of average routing goodput. The MAC/PHY overhead in the network depended upon the network density and DSDV and AODV came quite close in lowering the overhead. In addition, the thesis proposes a machine learning approach for identifying and preventing blackhole attacks in VANET. Different supervised machine learning classifiers are compared based on accuracy, F1-score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score. The experiments show that the bes (open full item for complete abstract)

Committee: Jared Oluoch (Advisor); Ezzatollah Salari (Committee Member); Junghwan Kim (Committee Member) Subjects: Computer Science

Doctor of Philosophy (PhD), Ohio University, 2021, Industrial and Systems Engineering (Engineering and Technology)

Transportation and logistics of valuable items (e.g., banknotes and coins, credit cards, securities, gold, jewelry, safes, and special pharmaceutical items like vaccines) are generally exposed to the risk of robbery and armored car heist. An inherent problem in designing supply chain networks for the transportation of valuables is to mitigate the risk while decrease the total cost by appropriate configuration of facility location and routing. In this research, the vehicle routing problem for the transportation of valuables is developed in several directions. From the modeling side, the risk mitigation approach that encompasses both the amount/number of valuables carried by a vehicle and the traveltime of a route is considered to minimize the risk of robbery and generate safe routes. The utilized risk function relaxes a pre-defined parameter of the risk threshold. Furthermore, the risk-minimizing problem is developed by incorporating various real-world characteristics and constraints into the model. At the expense of adding solution complexity, such formulation offers a more realistic model applicable to real situations.The model is extended from different angles, such as developing risk modeling by considering the vulnerability component and diversified arrival time using the multigraph network, and demand forecasting using an extensive evaluation of statistical and machine learning model. From the methodology side, multiple exact and metaheuristic methodologies are utilized and evaluated on several small to medium-sized instances and a case study. The augmented 휖-constraint 2 is used to solve the small instances of the problem. While solvable on small-sized instances, it poses computational challenges when applied to a large-scale rich problem. Therefore, five metaheuristics, namely, non-dominated sorting genetic algorithms (NSGAII and NSGAIII), strength of Pareto evolutionary algorithm 2 (SPEA2), indicator-based evolutionary algorithm (IBEA), and archived multi (open full item for complete abstract)

Committee: Gary Weckman (Advisor); Tao Yuan (Committee Member); Saeed Ghanbartehrani (Committee Member); Ashly Metcalf (Committee Member); Ehsan Ardjmand (Committee Member) Subjects: Banking; Industrial Engineering; Management; Statistics; Transportation; Transportation Planning

Doctor of Philosophy, The Ohio State University, 2021, Industrial and Systems Engineering

This dissertation develops methods to study the asymptotic expected optimal solutions to the combined Order Picking and Routing problem in a rectangular warehouse. We consider the asymptotic expected solutions, and thus consider that each order has a number of items drawn from a finite distribution, and that each item is located within an aisle in the warehouse according to a finite distribution. First, we develop a polyhedral approach for efficiently checking whether, for a given distribution of order sizes, p, a set of orders with sizes distributed according to p can be perfectly packed asymptotically. That is, packed in such a way that each bin is full. This also provides an efficient method for computing the asymptotic optimal expected bin packing solution for arbitrary distributions over the order sizes. We then study the relationship between the asymptotic expected bin packing solution and the asymptotic optimal expected routing solution. For the general Vehicle Routing Problem, there is a close connection between the asymptotic optimal bin packing solution and the asymptotic optimal route. For the present problem, however, we show that such a relationship does not exist. We then make use of graph theoretic techniques to develop bounds on the optimal routing solution for any given packing. Using these bounds, we show that a straightforward bin packing heuristic yields routing solutions exceeding the true optimal by at most a fraction that is a function of the bin size. Finally, we extend our polyhedral methods to study the probability that an Inhomogeneous Random Graph has a perfect matching as the number of nodes goes to infinity. Polytopes are constructed, whose non-emptiness, or emptiness, provides a sufficient condition for the asymptotic probability of a perfect matching to approach 1 or 0, respectively. When an asymptotic perfect matching may not exist, these sets are utilized within optimization procedures to develop bounds on the (open full item for complete abstract)

Committee: Marc Posner (Advisor); Ramteen Sioshansi (Committee Member); Chen Chen (Committee Member) Subjects: Industrial Engineering; Operations Research

MS, University of Cincinnati, 2021, Engineering and Applied Science: Mechanical Engineering

The aim of this thesis is to explore decomposition methods for solving problems of routing and planning of aerospace systems, specifically to those of Unmanned Aerial Vehicles (UAVs). Given some parameter of the system to optimize, the routing can be formulated as some optimization problem, which incorporates all the information of the system and any constraints on the routing of the UAVs. As these problems grow in size, with more UAVs, constraints, and targets, the problem becomes increasingly more difficult to solve, and often in an exponential manner. Optimal solutions in these cases are computationally expensive to solve. While sub-optimal and locally-optimal solutions can often be found with much less computation, there still remains a gap from the global optimum. As the system grows larger, the cost of this gap grows too, whether it be in monetary cost, increased equipment degradation, or longer time to complete tasks. Thus, extra computational time spent to find the optimal solution may be justified. However, as the systems increase in size, there will reach a point where finding the global optimum becomes intractable for whatever machine (computer) is being utilized to solve it. One method to do solve faster is to decompose the problem into smaller subproblems. Solving multiple subproblems iteratively can be faster than solving the original, primal problem outright. The decomposition methods here are developed to the end of solving the Persistent Intelligence Surveillance Reconnaissance (PISR) problem. This involves routing a set of UAVs to continuously monitor a set of targets of varying importance. Targets deemed more important must be visited more frequently. This problem and the frequency constraints can be modeled as a Multiple Depot Traveling Salesman Problem (MDTSP) incorporating the revisit periods of each target. Two different decomposition schemes are studied in this thesis. The first is a market based method, where the UAVs ar (open full item for complete abstract)

Committee: Manish Kumar Ph.D. (Committee Chair); Michael Alexander-Ramos Ph.D. (Committee Member); David Casbeer Ph.D. (Committee Member); Satyanarayana Gupta Manyam Ph.D. (Committee Member) Subjects: Mechanical Engineering