This paper investigates covert multi-hop communication in wireless networks where an adversary employs a cyclostationary (cycle) detector to reveal hidden transmissions. The covert route employs direct sequence spread spectrum (DSSS) signaling to ensure either maximum end-to-end covertness maximization or minimum latency minimization-under quality-of-service (QoS) and link budget constraints. Optimal bandwidth, transmit power, and spreading gain for each hop jointly satisfy reliability and either rate or covertness requirements. We show the equivalence between the covertness and the detection SNR gain-based widest-path formulations, and, hence, enabling efficient route computation. Numerical simulations in a realistic 3D environment illustrate that (i) end-to-end latency increases exponentially with the covertness requirement, (ii) the end-to-end latency increase is super-linear with the packet size M, and (iii) cycle and energy detectors impose different latency behavior as a function of the message length and the covertness requirement. The proposed framework provides important insights into resource allocation and routing design for covert networks against advanced detection adversaries.

We consider a three party wireless network- Alice is a legitimate transmitter, Bob is a legitimate receiver, and Willie is a malicious adversary. Alice's goal is to communicate with Bob with a certain minimum Quality of Service (QoS) while evading detection from Willie. Alice transmits a Direct Sequence Spread Spectrum (DSSS) signal where the processing gain and transmit power are selected with the aim of evading detection by spreading the bit energy over a larger bandwidth while maintaining required QoS. Concurrently, Willie tries to detect the legitimate transmission by using either an energy or a cycle detector. We study this scenario within an adversarial optimization framework that aims at achieving a robust max-min covertness under link performance constraints. The framework is common to both detectors and hence allows for a comparative performance analysis that identifies common key performance parameters. The adversarial signal-to-noise ratio (SNR) detection gain enables direct trading of the DSSS processing gain for the (squared) channel quality ratio when aiming to improve link covertness, regardless of which detector Willie uses. The DSSS processing gain and SNR gain (and, hence, covertness) are limited by the bit rate and bit error rate link requirements, respectively. While both detectors' performance is limited by the SNR at Willie, the cycle detectors benefit significantly more from longer observation time of the legitimate transmission.

Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate applications.In this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge’s customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

In this paper, we develop an approach to exploit multiple disparate wireless communication technologies simultaneously to enhance covertness of a communication link. Specifically, given two available communication modalities between a pair of friendly nodes (Alice and Bob), the goal is to evade detection by an adversary (Willie) who is equipped with a radiometer covering the frequency bands of both modalities. We propose a joint detection threshold optimization technique from Willie's point of view. We also develop a joint transmit power optimization strategy for Alice to maximize covertness while meeting the throughput requirement at Bob. Through numerical simulations we show that the proposed scheme matches the performance of exhaustive search method while reducing the computational time by 98% and also improves the covertness by 56% compared to a naïve benchmark scheme.

We present a method of generating spatial maps of kinetic parameters from dynamic sequences of images collected in hyperpolarized carbon-13 magnetic resonance imaging (MRI) experiments. The technique exploits spatial correlations in the dynamic traces via regularization in the space of parameter maps. Similar techniques have proven successful in other dynamic imaging problems, such as dynamic contrast enhanced MRI. In this paper, we apply these techniques for the first time to hyperpolarized MRI problems, which are particularly challenging due to limited signal-to-noise ratio (SNR). We formulate the reconstruction as an optimization problem and present an efficient iterative algorithm for solving it based on the alternation direction method of multipliers. We demonstrate that this technique improves the qualitative appearance of parameter maps estimated from low SNR dynamic image sequences, first in simulation then on a number of data sets collected in vivo. The improvement this method provides is particularly pronounced at low SNR levels.

Video delivery from a server to a client across a network is an important component of many multimedia applications. While delivering a video stream across a resource constrained network, loss of frames may be unavoidable. Under such circumstances, it is desirable to find a server transmission schedule that can efficiently utilize the network resources while maximizing the perceived quality-of-service (QoS) at the client. To address this issue, we introduce the notion of selective frame discard at the server and formulate the optimal selective frame discard problem using a QoS based cost function. Given network bandwidth and client buffer constraints, we develop an O(N log N) algorithm to find the minimum number of frames that must be discarded in order to meet these constraints. The correctness of the algorithm is also formally established. Since the computational complexity of the optimal algorithm for solving the optimal selective frame discard problem is prohibitively high in general, we also develop several efficient heuristic algorithms for selective frame discard. These algorithms are evaluated using JPEG video traces.