As embedded systems with manycores and Network-on-Chips (NoCs) become ubiquitous, emerging hardware and software vulnerabilities have made it challenging to ensure system integrity especially when third-party intellectual property (IP) is used for rapid prototyping. Prior works have evaluated hardware monitors for ensuring correctness of the system by threat assessment and effective mitigation. However, none have evaluated models that combine both computation (processor pipeline) and communication (NoC) vulnerabilities simultaneously. In this paper, we propose a high-level policy language called d-GUARD that is used to define runtime security policies that can be compiled into hardware monitors. The advantage of this new language is the ability to dynamically change policies based on program’s runtime behavior. To translate high-level policies into low-level hardware monitors, we describe a compiler for d-GUARD that synthesizes policies into Verilog modules. Instead of simply evaluating the design of secure policies for processor pipelines, we extend to secure NoC microarchitectures, including policies for links and routers, as well as policies to prevent Denial-of-Service (DoS) attacks. To mitigate attacks against secure microarchitectures, we also propose fault-tolerant routing approaches to avoid rogue routers when the number of policy violations exceeds a certain threshold. Our secure policies for processor pipelines and NoC microarchitectures consume marginal area and power overhead when compared to baseline making it well suited for low-cost embedded systems. • A high-level security policy language called D-GUARD, which is expressive, modular and compatible with power-efficient hardware is proposed. • The proposed dynamic policies implemented at various levels of the embedded enable low-cost monitors that can be modified based on application demands. • The runtime costs of the processor pipeline using OptimSoC, an open-source implementation of OpenRISC1000 architecture where processor policies are evaluated, which showed less than 1% overhead in power and area when implemented in 14 nm and 45 nm technologies and no performance overhead when implemented on BEEBS benchmark suite.

As low-level embedded systems are vulnerable to attacks that exploit flaws in either hardware or software, it is essential to enforce secure policies to protect the system from malicious instructions that significantly alter program behavior. To improve efficiency of implementation, high-level secure policy languages have been defined such that the policies can be directly synthesized into hardware monitors. However, the language semantics define policies that are static throughout the program execution which limits the flexibility. Moreover, secure policies target processor pipelines and not the network-on-chip (NoC) connecting several processor where denial-of-service attacks could originate. In this paper, we enable dynamically reconfigurable security policies through a high-level language called D-GUARD that target both processor pipeline and NoC architecture in mutlicore embedded systems. Alongside static policies, D-GUARD’s semantics support policies that dynamically change behavior in response to program conditions at runtime. In addition, we also propose policies to thwart denial-of-service attacks by rate limiting the packet flow into the network using the same dynamic policies expressed by D-GUARD. We describe a Verilog compiler to support realizing policies as hardware monitors for both processor pipelines and network interfaces. D-GUARD is developed using the Coq proof assistant, enabling the formal verification of policy correctness and other properties. This approach takes advantage of the abstractions and expressiveness of a higher-level language while minimizing the overhead that comes with other general-purpose approaches implemented purely in hardware, as well as offering the groundwork for a formally verified tool chain.

In this paper, we recount efforts in developing cybersecurity workshops for K-12 teachers, intended to learn skills to better educate the cybersecurity workers of tomorrow. In 2021, we provided two one-day virtual workshops and in 2022 we provided one two-day in-person workshop to high school teachers to increase cybersecurity awareness in three areas: general cybersecurity issues, software security, and hardware security. Both the online and in-person workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with Raspberry Pi Zeros to use as part of the workshops. This paper describes the design and implementation of the workshops and also provides evidence demonstrating the effectiveness of the workshops, as well as commentary to provide guidance for future efforts.

In traditional radio frequency networks, the quality of a link is typical characterized by metrics which are based on variations of the signal to noise ratio. In highly directional optical wireless networks, directly applying this approach to link quality indication does not fully characterize the nature of the channel impairment. In this paper, we attempt to develop a receiver-side link quality indication metric capable of determining non-ideal channel conditions using a cross correlation between measurements obtained from a training spot and a channel impaired spot formed on a receiver equipped with a MicroElectroMechanical Systems (MEMS) mirror array.

In this paper, we describe efforts to promote a robust cyber security workforce through a series of online workshops for K-12 teachers and grades 7-12 students. In 2021, we provided virtual workshops to high school teachers and students to increase cyber security awareness in three areas, (i) general cybersecurity issues, (ii) software security, and (iii) hardware security. The workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with hardware (Raspberry Pi Zeros) to use as part of the workshops. We describe the design and implementation of the workshops and share evidence to demonstrate the effectiveness of the workshops and provide insights for future professional development for teachers. Our central question was: What impact do workshops have on teachers' preparation to effectively teach cybersecurity topics to their students and what do teachers report learning from their experiences in the workshop? We provide insights into workshops' effectiveness for computer science teachers and for STEM teachers who are not computer science teachers.

As model size and the number of layers increase, Deep Neural Networks (DNNs) demand enormous computational power and throughput to meet exceedingly high prediction accuracy’s of today’s machine learning (ML) applications. Spatial hardware accelerators have been proposed that optimize the dataflow and exploit sparsity to provide a significant decrease in power consumption. As spatial architectures are traditionally designed with metallic interconnects, significant power is expended for data movement for different dataflows. In this paper, we exploit extended wireless technology to design a power-efficient and high-throughput DNN accelerator, e-WiNN, that can be configured for all representative dataflows and arithmetic precisions. We leverage novel circuit design by utilizing Dadda-algorithm based Multiply-and-Accumulate (MAC) circuits for 4-bit, 8-bit and 16-bit inputs to reduce area, power and delay constraints in 14 nm predictive technology. Our novel wireless transmitter integrates on- off keying (OOK) modulator with power amplifier that results in significant energy savings. To reduce the area overhead, we cluster wireless transceivers into groups of four such that both weights and input features can be effectively multicast to reduce the data movement. The energy efficient transceiver circuit is implemented in state-of-the-art BSIM 32 nm FinFET technology model and our link budget considers required RF power for different frequencies and inter-PE distance at three different antenna directivities including isotropic. Our detailed RTL modeling and cycle-accurate simulation results show that e-WiNN achieves 36.3% latency reduction and 76.1% energy saving when compared to state-of-art wire interconnected accelerators; 70.3% area reduction and 41.6% energy saving at the cost of 11% latency increase when compared to prior wireless accelerators on various neural networks (AlexNet, VGG16, and ResNet-9/50).

In a cognitive radio network, what is the trade off between statistical signal processing and machine learning algorithms that perform the same task? When should a system use the former and when should it use the latter? In this paper we present an empirical comparison study of different techniques for two tasks: detecting multiple transmitters in the same time-frequency domain, and automatic modulation classification. We develop and improve upon an unsupervised learning technique for the former, based on the log-Rayleigh distribution. Results are based on data generated from GNU Radio Companion, and implementations of these algorithms in software. They show that there is a tradeoff between accuracy and computation/implementation complexity - signal processing has a several orders of magnitude advantage over machine learning, but slightly lower accuracy. Thus, there is a need for an overarching framework that can meld machine learning and statistical signal processing.

The use of highly directional antennae in wireless networks has been shown to increase network capacity. As the beamwidth decreases, near perfect alignment is required to achieve these capacity gains. This becomes particularly challenging in mobile adhoc networks, leading to high alignment delay. In this paper, we explore ways of mitigating alignment delay in practical delay tolerant networks (DTNs) where each node has both a radio frequency (RF) control and a free space optical (FSO) data channel. We show that overcoming alignment delay while multicasting data during contact opportunities is an abstraction of the minimum weighted set cover problem. An optimal multicast scheme is implemented in a DTN simulator over a new session-based MAC protocol. Thorough performance evaluation demonstrates that we can compensate for fixed alignment delay, paving the way for high capacity DTNs.

The multi-copy routing paradigm in Delay Tolerant Networks (DTNs) implies that increasing contact bandwidth leads to a decrease in data delivery delay and an improvement in throughput. With Hybrid Radio Frequency/Free Space Optical (RF/FSO) PHY layers, the high data rate FSO links can be used to increase the contact bandwidth. However, due to the highly directional nature of FSO links, broadcasting is difficult. A naive broadcast strategy where the beam divergence angle is increased to include many nodes in the broadcast set results in low data rate, and does not always result in the minimum achievable delay. In this work we develop an optimal multicast algorithm for hybrid RF/FSO networks. We show that the problem is an abstraction of the minimum weight set cover problem which is known to be NP- hard. A computationally cheap greedy local optimum heuristic is proposed. A comprehensive evaluation using delay, throughput and computation time as metrics is performed using various solutions. These extensive evaluations show that our solution outperforms both naive broadcast and multiple unicast, taking 95% less time as compared to the exact algorithm, while providing comparable performance.

Quality of Experience (QoE) measures a user's satisfaction with a service delivery. However QoE is a very subjective measure and is context dependent, making it difficult for a service provider to estimate and optimize user's QoE. In this paper, we look at how the provider can maximize QoE by optimizing wireless bandwidth allocation, especially for mobile cloud applications. The multi-stimuli version of the “IQX” hypothesis is used to model the QoE of a user, and this model is used in formulation of a nonlinear optimization problem, which is solved using NSGA-II. Simulations using realistic parameters based on 802.11n demonstrate a reduction in the required bandwidth by as much as 33% (i.e., more users can be accommodated by the system), while maintaining the same level of QoE. Our evolutionary-algorithm-based approach is able to discover the optimal bandwidth allocation. The problem of equalizing user QoE is explored and a tradeoff between QoE and fairness is studied, while being characterized using a Pareto front.

The explosion in the number of 802.11 and 802.15.4 deployments is exacerbating the coexistence problem, which has been reported in the literature to cause significant performance degradation in co-located networks employing the two different wireless standards. The wireless coexistence problem has, thus far, been studied primarily using hardware, due to the lack of analytical results and good wireless coexistence simulators. This paper presents the first analytical model for coexisting 802.11 and 802.15.4 networks. We derive analytically, using Markov chains, the normalized saturation throughput under coexistence. Additionally, we propose a performance tuning method that ensures QoS and a distributed Nash-equilibrium-based method that ensures fairness. We validate our model and the tuning methods using a coexistence simulator previously developed and presented by the authors. We demonstrate that our model has a low average error smaller than 10%.

Disaster Response Networks (DRNs) are disruption tolerant networks designed to deliver mission critical data during disaster recovery, while operating with limited energy resources. While Quality of Service is desired, it is difficult to offer guarantees because of the unpredictable nature of mobility in such DRNs. The variance of the packet delivery delay (PDV, more commonly called jitter), an important QoS metric which in DRNs is measured in tens of minutes instead of milliseconds, has not been sufficiently addressed in recent research. Smartphones used by first responders generate large data workloads, causing the PDV to further degrade. Reducing packet replication at these workloads will lower energy consumption, but reduces the packet delivery ratio (PDR). The complex interplay between these QoS metrics remains unclear, making their control difficult. We present Raven, a routing protocol for DRNs that offers control over QoS, especially the PDV. Stochastic graph theory which deals with probabilistic edge weights having a mean and variance is used to model mobility in the disaster area. A stochastic version of the K-Shortest Paths algorithm routes data over multiple paths simultaneously. Raven has been thoroughly evaluated in simulation using realistic settings. The dynamics between performance and energy consumption is analyzed mathematically, and its control is demonstrated.

The node localization problem in mobile sensor networks has received significant attention. Recently, particle filters adapted from robotics have produced good localization accuracies in conventional settings. In spite of these successes, state-of-the-art solutions suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio signal irregularity. New solutions are needed to address these challenges. We propose a fuzzy logic-based approach for mobile node localization in challenging environments. Localization is formulated as a fuzzy multilateration problem. For sparse networks with few available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based localization scheme is implemented in a simulator and compared to state-of-the-art solutions. Extensive simulation results demonstrate improvements in the localization accuracy from 20 to 40 percent when the radio irregularity is high. A hardware implementation running on Epic motes and transported by iRobot mobile hosts confirms simulation results and extends them to the real world.

Time is a critical factor in the Urban Search & Rescue operations immediately following natural and man-made disasters. Building on our collaboration with first responders we identify a set of areas for improving response times: victim detection in collapsed buildings, information storage and collection about buildings (collapsed or not), detection of first responder team separation and lost tools, and throughput and latency of data delivered to first responders. In this paper, we present the design (i.e., software/hardware architectures, and the guiding design principles), implementation and realistic evaluation of DistressNet, a system that targets the aforementioned areas for reducing the Urban Search & Rescue response time. DistressNet, built on COTS hardware and on open standards and protocols, pushes complexity that the very diverse Urban Search & Rescue scenarios pose, to user level applications (apps). Apps in DistressNet run on unmodified hardware ranging from smartphones, to motes and wireless routers. For the benefit of the research community, we also share some lessons learned during our experiences in the design, building and evaluation of DistressNet.

A wireless sensor network can get separated into multiple connected components due to the failure of some of its nodes, which is called a “cut.” In this paper, we consider the problem of detecting cuts by the remaining nodes of a wireless sensor network. We propose an algorithm that allows 1) every node to detect when the connectivity to a specially designated node has been lost, and 2) one or more nodes (that are connected to the special node after the cut) to detect the occurrence of the cut. The algorithm is distributed and asynchronous: every node needs to communicate with only those nodes that are within its communication range. The algorithm is based on the iterative computation of a fictitious “electrical potential” of the nodes. The convergence rate of the underlying iterative scheme is independent of the size and structure of the network. We demonstrate the effectiveness of the proposed algorithm through simulations and a real hardware implementation.

Neighbor discovery is an important part of many protocols for wireless adhoc networks, including localization and routing. When neighbor discovery fails, communications and protocols performance deteriorate. In networks affected by relay attacks, also known as wormholes, the failure may be more subtle. The wormhole may selectively deny or degrade communications. IIn this paper we present Mobile Secure Neighbor Discovery (MSND), which offers a measure of protection against wormholes by allowing participating mobile nodes to securely determine if they are neighbors. To the best of our knowledge, this work is the first to secure neighbor discovery in mobile adhoc networks. MSND leverages concepts of graph rigidity for wormhole detection.We prove security properties of our protocol, and demonstrate its effectiveness through extensive simulations and a real system evaluation employing Epic motes and iRobot robots.

Loss of connectivity in deployed wireless sensor networks can be quite disastrous for the network. A "cut" (which separates the network into two or more components incapable of communicating with each other) is usually hard to detect. An algorithm which enables each node in the network to detect whether a cut has occurred anywhere in the network is demonstrated.