VTechWorks Repository :: Browsing by Author "Zeng, Haibo"

Browsing by Author "Zeng, Haibo"

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Advancing the Development and Utilization of Data Infrastructure for Smart Homes
Anik, Sheik Murad Hassan (Virginia Tech, 2024-09-12)
The smart home era is inevitably arising towards our everyday life. However, the scarcity of publicly available data remains a major hurdle in the domain, limiting people's capability of performing data analysis and their effectiveness in creating smart home automations. To mitigate this hurdle and its influence, our research explored three research directions to (1) create a better infrastructure that effectively collects and visualizes indoor-environment sensing data, (2) create a machine learning-based approach to demonstrate a novel way of analyzing indoor-environment data to facilitate human-centered building design, and (3) conduct an empirical study to explore the challenges and opportunities in existing smart home development. Specifically, we conducted three research projects. First, we created an open-source IoT-based cost-effective, distributed, scalable, and portable indoor environmental data collection system, Building Data Lite (BDL). We deployed this research prototype in 12 households, which deployment so far has collected more than 2 million records that are available to public in general. Second, building occupant persona is a very important component in human-centered smart home design, so we investigated an approach of applying state-of-the-art machine-learning models to data collected by an existing infrastructure, to enable the automatic creation of building occupant persona while minimizing human effort. Third, Home Assistant (HA) is an open-source off-the-shelf smart home platform that users frequently use to transform their residences into smart homes. However, many users seem to be stuck with the configuration scripts of home automations. We conducted an empirical study by (1) crawling posts on HA forum, (2) manually analyzing those posts to understand users' common technical concerns as well as frequently recommended resolutions, and (3) applying existing tools to assess the tool usefulness in alleviating users' pain. All our research projects will shed light on future directions in smart home design and development.
Age of Information: Fundamentals, Distributions, and Applications
Abd-Elmagid, Mohamed Abd-Elaziz (Virginia Tech, 2023-07-11)
A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.
Analysis and Enforcement of Properties in Software Systems
Wu, Meng (Virginia Tech, 2019-07-02)
Due to the lack of effective techniques for detecting and mitigating property violations, existing approaches to ensure the safety and security of software systems are often labor intensive and error prone. Furthermore, they focus primarily on functional correctness of the software code while ignoring micro-architectural details of the underlying processor, such as cache and speculative execution, which may undermine their soundness guarantees. To fill the gap, I propose a set of new methods and tools for ensuring the safety and security of software systems. Broadly speaking, these methods and tools fall into three categories. The first category is concerned with static program analysis. Specifically, I develop a novel abstract interpretation framework that considers both speculative execution and a cache model, and guarantees to be sound for estimating the execution time of a program and detecting side-channel information leaks. The second category is concerned with static program transformation. The goal is to eliminate side channels by equalizing the number of CPU cycles and the number of cache misses along all program paths for all sensitive variables. The third category is concerned with runtime safety enforcement. Given a property that may be violated by a reactive system, the goal is to synthesize an enforcer, called the shield, to correct the erroneous behaviors of the system instantaneously, so that the property is always satisfied by the combined system. I develop techniques to make the shield practical by handling both burst error and real-valued signals. The proposed techniques have been implemented and evaluated on realistic applications to demonstrate their effectiveness and efficiency.
An Automatic Solution to Checking Compatibility between Routing Metrics and Protocols
Liu, Chang (Virginia Tech, 2016-01-19)
Routing metrics are important mechanisms to adjust routing protocols' path selection according to the needs of a network system. However, if a routing metric design does not correctly match a particular routing protocol, the protocol may not be able to find an optimal path; routing loops can be produced as well. Thus, the compatibility between routing metrics and routing protocols is increasingly significant with the widespread deployment of wired and wireless networks. However, it is usually difficult to tell whether a routing metric can be perfectly applied to a particular routing protocol. Manually enumerating all possible test cases is very challenging and often infeasible. Therefore, it is highly desirable to have an automatic solution so that one can avoid putting an incompatible combination of routing metric and protocol into use. In this thesis, the above issue has been addressed by developing two automated checking systems for examining the compatibility between real world routing metric and protocol implementations. The automatic routing protocol checking system assumes that some properties of routing metrics are given and the system's job is to check if a new routing protocol is able to achieve optimal, consistent and loop- free routing when it is combined with metrics that hold the given metric properties. In contrast to the protocol checking system, the automatic routing metric checking system assumes that a routing protocol is given and the checking system needs to verify if a new metric implementation will be able to work with this protocol. Experiments have been conducted to verify the correctness of both protocol and metric checking systems.
CAN Bus Intrusion Detection based on Auxiliary Classifier GAN and Out-of-Distribution Detection
Zhao, Qingling; Chen, Mingqiang; Gu, Zonghua; Luan, Siyu; Zeng, Haibo; Chakraborty, Samarjit (ACM, 2022-09-05)
The Controller Area Network (CAN) is a ubiquitous bus protocol present in the Electrical/Electronic (E/E) systems of almost all vehicles. It is vulnerable to a range of attacks once the attacker gains access to the bus through the vehicle's attack surface. We address the problem of Intrusion Detection on the CAN bus, and present a series of methods based on two classifiers trained with Auxiliary Classifier Generative Adversarial Network (ACGAN) to detect and assign fine-grained labels to Known Attacks, and also detect the Unknown Attack class in a dataset containing a mixture of (Normal + Known Attacks + Unknown Attack) messages. The most effective method is a cascaded two-stage classification architecture, with the multi-class Auxiliary Classifier in the first stage for classification of Normal and Known Attacks, passing Out-of-Distribution (OOD) samples to the binary Real-Fake Classifier in the second stage for detection of the Unknown Attack class. Performance evaluation demonstrate that our method achieves both high classification accuracy and low runtime overhead, making it suitable for deployment in the resource-constrained in-vehicle environment.
A Comparison of Image Classification with Different Activation Functions in Balanced and Unbalanced Datasets
Zhang, Moqi (Virginia Tech, 2021-06-04)
When the novel coronavirus (COVID-19) outbreak began to ring alarm bells worldwide, rapid, efficient diagnosis was critical to the emergency response. The limited ability of medical systems and the increasing number of daily cases pushed researchers to investigate automated models. The use of deep neural networks to help doctors make the correct diagnosis has dramatically reduced the pressure on the healthcare system. Promoting the improvement of diagnosis networks depends not only on the network structure design but also on the activation function performance. To identify an optimal activation function, this study investigates the correlation between the activation function selection and image classification performance in balanced or imbalanced datasets. Our analysis evaluates various network architectures for both commonly used and novel datasets and presents a comprehensive analysis of ten widely used activation functions. The experimental results show that the swish and softplus functions enhance the classification ability of state-of-the-art networks. Finally, this thesis distinguishes the neural networks using ten activation functions, analyzes their pros and cons, and puts forward detailed suggestions on choosing appropriate activation functions in future work.
Compiler-Directed Error Resilience for Reliable Computing
Liu, Qingrui (Virginia Tech, 2018-08-08)
Error resilience has become as important as power and performance in modern computing architecture. There are various sources of errors that can paralyze real-world computing systems. Of particular interest to this dissertation are single-event errors. They can be the results of energetic particle strike or abrupt power outage that corrupts the program states leading to system failures. Specifically, energetic particle strike is the major cause of soft error while abrupt power outage can result in memory inconsistency in the nonvolatile memory systems. Unfortunately, existing techniques to handle those single-event errors are either resource consuming (e.g., hardware approaches) or heavy-weight (e.g., software approaches). To address this problem, this dissertation identifies idempotent processing as an alternative recovery technique to handle the system failures in an efficient and low-cost manner. Then, this dissertation first proposes to design and develop a compiler-directed lightweight methodology which leverages idempotent processing and the state-of-the-art sensor-based detection to achieve soft error resilience at low-cost. This dissertation also introduces a lightweight soft error tolerant hardware design that redefines idempotent processing where the idempotent regions can be created, verified and recovered from the processor's point of view. Furthermore, this dissertation proposes a series of compiler optimizations that significantly reduce the hardware and runtime overhead of the idempotent processing. Lastly, this dissertation proposes a failure-atomic system integrated with idempotent processing to resolve another type of single-event error, i.e., failure-induced memory inconsistency in the nonvolatile memory systems.
Constraint-Based Thread-Modular Abstract Interpretation
Kusano, Markus Jan Urban (Virginia Tech, 2018-07-25)
In this dissertation, I present a set of novel constraint-based thread-modular abstract-interpretation techniques for static analysis of concurrent programs. Specifically, I integrate a lightweight constraint solver into a thread-modular abstract interpreter to reason about inter-thread interference more accurately. Then, I show how to extend the new analyzer from programs running on sequentially consistent memory to programs running on weak memory. Finally, I show how to perform incremental abstract interpretation, with and without the previously mentioned constraint solver, by analyzing only regions of the program impacted by a program modification. I also demonstrate, through experiments, that these new constraint-based static analyzers are significantly more accurate than prior abstract interpretation-based static analyzers, with lower runtime overhead, and that the incremental technique can drastically reduce runtime overhead in the presence of small program modifications.
Cooperative Automated Vehicle Movement Optimization at Uncontrolled Intersections using Distributed Multi-Agent System Modeling
Mahmoud, Abdallah Abdelrahman Hassan (Virginia Tech, 2017-02-28)
Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network.
A Cost-Efficient Digital ESN Architecture on FPGA
Gan, Victor Ming (Virginia Tech, 2020-09-01)
Echo State Network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks (RNNs). Its performance metrics outperform traditional RNNs in nonlinear system identification and temporal information processing. In this thesis, we design and implement ESNs through Field-programmable gate array (FPGA) and explore their full capacity of digital signal processors (DSPs) to target low-cost and low-power applications. We propose a cost-optimized and scalable ESN architecture on FPGA, which exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks (CLBs). The proposed work includes a linear combination processor with negligible deployment of CLBs, as well as a high-accuracy non-linear function approximator, both with the help of only 9 DSP units in each neuron. The architecture is verified with the classical NARMA dataset, and a symbol detection task for an orthogonal frequency division multiplexing (OFDM) system on a wireless communication testbed. In the worst-case scenario, our proposed architecture delivers a matching bit error rate (BER) compares to its corresponding software ESN implementation. The performance difference between the hardware and software approach is less than 6.5%. The testbed system is built on a software-defined radio (SDR) platform, showing that our work is capable of processing the real-world data.
Cross-ISA Execution Migration of Unikernels: Build Toolchain, Memory Alignment, and VM State Transfer Techniques
Mehrab, A K M Fazla (Virginia Tech, 2018-12-12)
The data centers are composed of resource-rich expensive server machines. A server, overloadeded with workloads, offloads some jobs to other servers; otherwise, its throughput becomes low. On the other hand, low-end embedded computers are low-power, and cheap OS-capable devices. We propose a system to use these embedded devices besides the servers and migrate some jobs from the server to the boards to increase the throughput when overloaded. The datacenters usually run workloads inside virtual machines (VM), but these embedded boards are not capable of running full-fledged VMs. In this thesis, we propose to use lightweight VMs, called unikernel, which can run on these low-end embedded devices. Another problem is that the most efficient versions of these boards have different instruction set architectures than the servers have. The ISA-difference between the servers and the embedded boards and the migration of the entire unikernel between them makes the migration a non-trivial problem. This thesis proposes a way to provide the unikernels with migration capabilities so that it becomes possible to offload workloads from the server to the embedded boards. This thesis describes a toolchain development process for building migratable unikernel for the native applications. This thesis also describes the alignment of the memory components between unikernels for different ISAs, so that the memory referencing remains valid and consistent after migration. Moreover, this thesis represents an efficient VM state transfer method so that the workloads experience higher execution time and minimum downtime due to the migration.
Delay-Aware Multi-Path Routing in a Multi-Hop Network: Algorithms and Applications
Liu, Qingyu (Virginia Tech, 2019-06-21)
Delay is known to be a critical performance metric for various real-world routing applications including multimedia communication and freight delivery. Provisioning delay-minimal (or at least delay-bounded) routing services for all traffic of an application is highly important. As a basic paradigm of networking, multi-path routing has been proven to be able to obtain lower delay performance than the single-path routing, since traffic congestions can be avoided. However, to our best knowledge, (i) many of existing delay-aware multi-path routing studies only consider the aggregate traffic delay. Considering that even the solution achieving the optimal aggregate traffic delay has a possibly unbounded delay performance for certain individual traffic unit, those studies may be insufficient in practice; besides, (ii) most existing studies which optimize or bound delays of all traffic are best-effort, where the achieved solutions have no theoretical performance guarantee. In this dissertation, we study four delay-aware multi-path routing problems, with the delay performances of all traffic taken into account. Three of them are in communication and one of them is in transportation. Note that our study differ from all related ones as we are the first to study the four fundamental problems to our best knowledge. Although we prove that our studied problems are all NP-hard, we design approximation algorithms with theoretical performance guarantee for solving each of them. To be specific, we claim the following contributions. Minimize maximum delay and average delay. First, we consider a single-unicast setting where in a multi-hop network a sender requires to use multiple paths to stream a flow at a fixed rate to a receiver. Two important delay metrics are the average sender-to-receiver delay and the maximum sender-to-receiver delay. Existing results say that the two delay metrics of a flow cannot be both within bounded-ratio gaps to the optimal. In comparison, we design three different flow solutions, each of which can minimize the two delay metrics simultaneously within a $(1/epsilon)$-ratio gap to the optimal, at a cost of only delivering $(1-epsilon)$-fraction of the flow, for any user-defined $epsilonin(0,1)$. The gap $(1/epsilon)$ is proven to be at least near-tight, and we further show that our solutions can be extended to the multiple-unicast setting. Minimize Age-of-Information (AoI). Second, we consider a single-unicast setting where in a multi-hop network a sender requires to use multiple paths to periodically send a batch of data to a receiver. We study a newly proposed delay-sensitive networking performance metric, AoI, defined as the elapsed time since the generation of the last received data. We consider the problem of minimizing AoI subject to throughput requirements, which we prove is NP-hard. We note that our AoI problem differs from existing ones in that we are the first to consider the batch generation of data and multi-path communication. We develop both an optimal algorithm with a pseudo-polynomial time complexity and an approximation framework with a polynomial time complexity. Our framework can build upon any polynomial-time $alpha$-approximation algorithm of the maximum delay minimization problem, to construct an $(alpha+c)$-approximate solution for minimizing AoI. Here $c$ is a constant dependent on throughput requirements. Maximize network utility. Third, we consider a multiple-unicast setting where in a multi-hop network there exist many network users. Each user requires a sender to use multiple paths to stream a flow to a receiver, incurring an utility that is a function of the experienced maximum delay or the achieved throughput. Our objective is to maximize the aggregate utility of all users under throughput requirements and maximum delay constraints. We observe that it is NP-complete either to construct an optimal solution under relaxed maximum delay constraints or relaxed throughput requirements, or to figure out a feasible solution with all constraints satisfied. Hence it is non-trivial even to obtain approximate solutions satisfying relaxed constraints in a polynomial time. We develop a polynomial-time approximation algorithm. Our algorithm obtains solutions with constant approximation ratios under realistic conditions, at the cost of violating constraints by up to constant-ratios. Minimize fuel consumption for a heavy truck to timely fulfill multiple transportation tasks. Finally, we consider a common truck operation scenario where a truck is driving in a national highway network to fulfill multiple transportation tasks in order. We study an NP-hard timely eco-routing problem of minimizing total fuel consumption under task pickup and delivery time window constraints. We note that optimizing task execution times is a new challenging design space for saving fuel in our multi-task setting, and it differentiates our study from existing ones under the single-task setting. We design a fast and efficient heuristic. We characterize conditions under which the solution of our heuristic must be optimal, and further prove its optimality gap in case the conditions are not met. We simulate a heavy-duty truck driving across the US national highway system, and empirically observe that the fuel consumption achieved by our heuristic can be $22%$ less than that achieved by the fastest-/shortest- path baselines. Furthermore, the fuel saving of our heuristic as compared to the baselines is robust to the number of tasks.
Design and Implementation of a Network Server in LibrettOS
Sung, Mincheol (Virginia Tech, 2018-12-13)
Traditional network stacks in monolithic kernels have reliability and security concerns. Any fault in a network stack affects the entire system owing to lack of isolation in the monolithic kernel. Moreover, the large code size of the network stack enlarges the attack surface of the system. A multiserver OS design solves this problem. In contrast to the traditional network stack, a multiserver OS pushes the network stack into the network server as a user process, which performs three enhancements: (i) allows the network server to run in user mode while having its own address space and isolating any fault occurring in the network server; (ii) minimizes the attack surface of the system because the trusted computing base contracts; (iii) enables failure recovery, which is an important feature supported by a multiserver OS. This thesis proposes a network server for LibrettOS, an operating system based on rumprun unikernels and the Xen Hypervisor developed by Virginia Tech. The proposed network server is a service domain providing an L2 frame forwarding service for application domains and based on rumprun such that the existing device drivers of NetBSD can be leveraged with little modification. In this model, the TCP/IP stack runs directly in the address space of applications. This allows retaining the client state even if the network server crashes and makes it possible to recover from a network server failure. We leverage the Xen PCI passthrough to access a NIC (Network Interface Controller) from the network server. Our experimental evaluation demonstrates that the performance of the network server is good and comparable with Linux and NetBSD. We also demonstrate the successful recovery after a failure.
Design Optimization Techniques for Time-Critical Cyber-Physical Systems
Zhao, Yecheng (Virginia Tech, 2020-01-20)
Cyber-Physical Systems (CPS) are widely deployed in critical applications which are subject to strict timing constraints. To ensure correct timing behavior, much of the effort has been dedicated to the development of validation and verification methods for CPS (e.g., system models and their timing and schedulability analysis). As CPS is becoming increasingly complex, there is an urgent need for efficient optimization techniques that can aid the design of large-scale systems. Specifically, techniques that can find good design options in a reasonable amount of time while meeting all the timing and other critical requirements are becoming vital. However, the current mindset is to use existing schedulability analysis and optimization techniques for the design optimization of time-critical CPS. This has resulted in two issues in today's CPS design: 1) Existing timing and schedulability analysis are very difficult and inefficient to be integrated into well-established optimization frameworks such as mathematical programming; 2) New system models and timing analysis are being developed in a way that is increasingly unfriendly to optimization. Due to these difficulties, existing practice for optimization mostly relies on meta or ad-hoc heuristics, which suffers either from sub-optimality or limited applicability. In this dissertation, we seek to address these issues and explore two new directions for developing optimization algorithms for time-critical CPS. The first is to develop {em optimization-oriented timing analysis}, that are efficient to formulate in mathematical programming framework. The second is a domain-specific optimization framework. The framework leverages domain-specific knowledge to provide methods that abstract timing analysis into a simple mathematical form. This allows to efficiently handle the complexity of timing analysis in optimization algorithms. The results on a number of case studies show that the proposed approaches have the potential to significantly improve upon scalability (several orders of magnitude faster) and solution quality, while being applicable to various system models, timing analysis techniques, and design optimization problems in time-critical CPS.
Design Space Exploration for Embedded Systems in Automotives
Joshi, Prachi (Virginia Tech, 2018-04-16)
With ever increasing contents (safety, driver assistance, infotainment, etc.) in today's automotive systems that rely on electronics and software, the supporting architecture is integrated by a complex set of heterogeneous data networks. A modern automobile contains up to 100 ECUs and several heterogeneous communication buses (such as CAN, FlexRay, etc.), exchanging thousands of signals. The automotive Original Equipment Manufacturers (OEMs) and suppliers face a number of challenges such as reliability, safety and cost to incorporate the growing functionalities in vehicles. Additionally, reliability, safety and cost are major concerns for the industry. One of the important challenges in automotive design is the efficient and reliable transmission of signals over communication networks such as CAN and CAN-FD. With the growing features in automotives, the OEMs already face the challenge of saturation of bus bandwidth hindering the reliability of communication and the inclusion of additional features. In this dissertation, we study the problem of optimization of bandwidth utilization (BU) over CAN-FD networks. Signals are transmitted over the CAN/CAN-FD bus in entities called frames. The signal-to-frame-packing has been studied in the literature and it is compared to the bin packing problem which is known to be NP-hard. By carefully optimizing signal-to-frame packing, the CAN-FD BU can be reduced. In Chapter 3, we propose a method for offset assignment to signals and show its importance in improving BU. One of our contributions for an industrial setting is a modest improvement in BU of about 2.3%. Even with this modest improvement, the architecture's lifetime could potentially be extended by several product cycles, which may translate to saving millions of dollars for the OEM. Therefore, the optimization of signal-to-frame packing in CAN-FD is the major focus of this dissertation. Another challenge addressed in this dissertation is the reliable mapping of a task model onto a given architecture, such that the end-to-end latency requirements are satisfied. This avoids costly redesign and redevelopment due to system design errors.
Developing and Testing a Novel De-centralized Cycle-free Game Theoretic Traffic Signal Controller: A Traffic Efficiency and Environmental Perspective
Abdelghaffar, Hossam Mohamed Abdelwahed (Virginia Tech, 2018-04-30)
Traffic congestion negatively affects traveler mobility and air quality. Stop and go vehicular movements associated with traffic jams typically result in higher fuel consumption levels compared to cruising at a constant speed. The first objective in the dissertation is to investigate the spatial relationship between air quality and traffic flow patterns. We developed and applied a recursive Bayesian estimation algorithm to estimate the source location (associated with traffic jam) of an airborne contaminant (aerosol) in a simulation environment. This algorithm was compared to the gradient descent algorithm and an extended Kalman filter algorithm. Results suggest that Bayesian estimation is less sensitive to the choice of the initial state and to the plume dispersion model. Consequently, Bayesian estimation was implemented to identify the location (correlated with traffic flows) of the aerosol (soot) that can be attributed to traffic in the vicinity of the Old Dominion University campus, using data collected from a remote sensing system. Results show that the source location of soot pollution is located at congested intersections, which demonstrate that air quality is correlated with traffic flows and congestion caused by signalized intersections. Sustainable mobility can help reduce traffic congestion and vehicle emissions, and thus, optimizing the performance of available infrastructure via advanced traffic signal controllers has become increasingly appealing. The second objective in the dissertation is to develop a novel de-centralized traffic signal controller, achieved using a Nash bargaining game-theoretic framework, that operates a flexible phasing sequence and free cycle length to adapt to dynamic changes in traffic demand levels. The developed controller was implemented and tested in the INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was compared to the operation of an optimum fixed-time coordinated plan, an actuated controller, a centralized adaptive phase split controller, a decentralized phase split and cycle length controller, and a fully coordinated adaptive phase split, cycle length, and offset optimization controller to evaluate its performance. Testing was initially conducted on an isolated intersection, showing a 77% reduction in queue length, a 17% reduction in vehicle emission levels, and a 64% reduction in total delay. In addition, the developed controller was tested on an arterial network producing statistically significant reductions in total delay ranging between 36% and 67% and vehicle emissions reductions ranging between 6% and 13%. Analysis of variance, Tukey, and pairwise comparison tests were conducted to establish the significance of the proposed controller. Moreover, the controller was tested on a network of 38 intersections producing significant reduction in the travel time by 23.6%, a reduction in the queue length by 37.6%, and a reduction in CO2 emissions by 10.4%. Finally, the controller was tested on the Los Angeles downtown network composed of 457 signalized intersections, producing a 35% reduction in travel time, a 54.7% reduction in queue length, and a 10% reduction in the CO2 emissions. The results demonstrate that the proposed decentralized controller produces major improvements over other state-of-the-art centralized and de-centralized controllers. The proposed controller is capable of alleviating congestion as well as reducing emissions and enhancing air quality.
Development of Open-Source Gantry-Plus Robot Systems for Plant Science research
Kaundanya, Adwait Anand (Virginia Tech, 2024-12-19)
Affordable and readily available automation options for plant research remain scarce, however with the availability of such a system, many research tasks can be streamlined. In this project, we demonstrate a prototype of such an open-source, low-cost, heterogeneous robotic system called Mini T-Rex. We combine two over-the-counter robots and leverage the ROS2 framework to control this heterogeneous system. This system provides a unique advantage of sensor-to-plant method to capture multi-view images at any angle and distance within the workspace. We demonstrate how making a digital twin in ROS2 can help to control a heterogeneous system via abstracted hardware control. We also talk about I2GROW Oasis which is a robotic system consisting of a remotely controlled robot with the ability to capture top-view images. In this thesis we describe the hardware and software design of both these robotic systems. To use this robotic system, the plants can be grown on a growth bed or a hydroponic system below the Mini T-Rex robot, and the camera will approach the plant without any contact with the plants due to the precise control of the robotic manipulator. We used the system to capture several large data sets of 3D phenotypic data for Solanum lycopersicum, Lactuca sativa, and Thlaspi. In conclusion, we have developed a 9-degree of freedom, fully open-source heterogeneous robotic system capable of multi-view, camera-to plant image capture for plant 3D model reconstruction called Mini T-Rex. We show how to use gantry like robots for phenotyping and create longitudinal datasets by automating these high precision robotic systems.
Distributed Intelligence for Multi-Agent Systems in Search and Rescue
Patnayak, Chinmaya (Virginia Tech, 2020-11-05)
Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses.
EASE-E: Edge-AI based System for Energy-Efficiency in Autonomous Driving (ADAS/AD)
Kothari, Aadi Jay (Virginia Tech, 2024-12-16)
The rise of Software-Defined Vehicles (SDVs) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicle (AV) technology. However, as compute and sensing architectures for SAE Level 2 vehicles increasingly lean towards fully centralized systems, significant concerns arise regarding their energy demands. This shift may have a negative impact on one of the most critical purchasing factors for Battery Electric Vehicles (BEVs): electric driving range. This thesis investigates the potential benefits of decentralization in automotive Electrical/Electronic (E/E) architecture, powered by System-on-Module (SoM) Edge-AI boards. By facilitating efficient deep learning processing locally, the proposed EASE-E (Edge-AI based System for Energy Efficiency) solution achieves up to a 5x reduction in power consumption while maintaining high processing performance. Through a combination of bench testing and Software-in-the-Loop (SiL) simulations, this research demonstrates that EASE-E enhances energy efficiency by 32.8% in highway driving, and 10.8% in urban environments. EASE-E also offers greater scalability and resilience when compared to the existing E/E architectures: distributed, domain, and zonal. The findings underscore the potential of this solution to preserve and extend the driving range of BEVs, presenting a compelling alternative to a fully centralized approach. These insights are crucial for the future design of scalable, energy efficient, and autonomous software-defined vehicles.
Efficiency of Logic Minimization Techniques for Cryptographic Hardware Implementation
Raghuraman, Shashank (Virginia Tech, 2019-07-15)
With significant research effort being directed towards designing lightweight cryptographic primitives, logical metrics such as gate count are extensively used in estimating their hardware quality. Specialized logic minimization tools have been built to make use of gate count as the primary optimization cost function. The first part of this thesis aims to investigate the effectiveness of such logical metrics in predicting hardware efficiency of corresponding circuits. Mapping a logical representation onto hardware depends on the standard cell technology used, and is driven by trade-offs between area, performance, and power. This work evaluates aforementioned parameters for circuits optimized for gate count, and compares them with a set of benchmark designs. Extensive analysis is performed over a wide range of frequencies at multiple levels of abstraction and system integration, to understand the different regions in the solution space where such logic minimization techniques are effective. A prototype System-on-Chip (SoC) is designed to benchmark the performance of these circuits on actual hardware. This SoC is built with an aim to include multiple other cryptographic blocks for analysis of their hardware efficiency. The second part of this thesis analyzes the overhead involved in integrating selected authenticated encryption ciphers onto an SoC, and explores different design alternatives for the same. Overall, this thesis is intended to serve as a comprehensive guideline on hardware factors that can be overlooked, but must be considered during logical-to-physical mapping and during the integration of standalone cryptographic blocks onto a complete system.

Browsing by Author "Zeng, Haibo"

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