This paper studies finding the secure path according to the secrecy connectivity probability (SCP) in the multi-hop ad hoc networks in the presence of inhomogeneous eavesdropper clusters. We consider both random and fixed eavesdropper clusters, where the former case assumes that there is no knowledge of the locations of the clusters and the latter case assumes that the locations of the clusters can be estimated accurately. Firstly, we derive the end-to-end SCP to characterize the secrecy performance of a given path in a general multi-hop wireless network with half-duplex (HD) randomize-and-forward relaying. Then we consider a full-duplex (FD) scheme at the legitimate receiver, which receives the useful information while broadcasting a jamming signal to the potential eavesdroppers to further enhance the secrecy connectivity. Then, a novel secure routing algorithm which can provide the maximum SCP for any legitimate transmitter/receiver pair in a distributed manner is proposed. The theoretical analysis is verified by Monte Carlo simulation results. The results show that our secure routing algorithm provides similar results compared to an exhaustive search. For the random eavesdropper cluster case, the optimal route is independent of the knowledge of the cluster, which is the same as the homogeneous eavesdropper case. However, for the case where eavesdropper clusters are fixed and their locations are known a priori, the optimal path selection depends on the radii and locations of the eavesdropper clusters and the average number of eavesdroppers in each cluster.

We propose a joint compressed sensing-based encoding and an iterative-decoding method, which we call turbo compressed sensing (turbo-CS), for the robust to noise transmission of sparse signals over an additive white Gaussian noise (AWGN) channel. The turbo-CS encoder applies 1-bit compressed sensing as a source encoder concatenated serially with a convolutional channel encoder. At the turbo-CS decoder, an iterative joint source-channel decoding method is proposed for signal reconstruction. We analyze, for the first time, the convergence of the turbo-CS decoder by determining an extrinsic information transfer chart of the constituent decoders. We modify the soft-outputs of the constituent source decoder to improve the signal reconstruction performance of the turbo-CS decoder. Our results show that for a fixed received signal to noise ratio (RSNR) of 10 dB more than 5 dB of improvement in the channel SNR is achieved after six iterations of the turbo-CS decoder. Alternatively, for a fixed SNR of-1 dB, 10 dB improvement in RSNR is achieved. Moreover, it is shown that the turbo-CS decoder outperforms the state-of-the-art algorithms for 1-bit compressed sensing reconstruction in the presence of AWGN channel in terms of signal reconstruction performance and complexity.

In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyzing the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic level and formulate the probability of accessibility from a node $i$ to a node $j$ after a certain number of discrete time units $T$. While solving the original problem is computationally intractable, we provide an upper and two lower bounds on this probability for a very general case with arbitrary time-varying probabilities of the links' existence. Moreover, for a special case where the links have identical probabilities across the network at each time slot, we obtain the exact probability of accessibility between any two nodes. Finally, we discuss scenarios where the information regarding the presence and absence of links is initially available in the form of time duration (of presence or absence intervals) continuous probability distributions rather than discrete probabilities over time slots. We provide a method for transforming such distributions to discrete probabilities, which enables us to apply the given bounds in this paper to a broader range of problem settings.

Many applications such as, software, and hardware generate an increasing volume of data and logs in real-time. Visual analytics is essential to support system monitoring and analysis of such data. For example, the world’s largest radio telescope, the Square Kilometer Array (SKA), is expected to generate an estimated 160 TB a second of raw data captured from different sources. Transporting large amounts of data from distributed sources to a web browser for visualization is time-consuming due to data transport latencies. In addition, visualizing real-time data in the browser is challenging and limited by the data rates which a web browser can handle. We propose a novel low latency data streaming architecture, which uses a messaging system for real-time data transport to the web browser. Based on this architecture, we propose techniques and provide a tool for analyzing the performance of serialization protocols and the web-visualization rendering pipeline. We empirically evaluate the performance of our architecture using three visualizations use cases relevant to the SKA. Our system proved extremely useful in streaming high-volume data in real-time with low latency and greatly enhanced the web-visualization performance by enabling streaming an optimal number of data points for different visualizations.

In a wide range of complex networks, the links between the nodes are temporal and may sporadically appear and disappear. This temporality is fundamental to analyze the formation of paths within such networks. Moreover, the presence of the links between the nodes is a random process induced by nature in many real-world networks. In this paper, we study random temporal networks at a microscopic level and formulate the \emph{probability of accessibility} from a node \emph{i} to a node \emph{j} after a certain number of discrete time units $T$. While solving the original problem is computationally intractable, we provide an upper and two lower bounds on this probability for a very general case with arbitrary time-varying probabilities of links' existence. Moreover, for a special case where the links have identical probabilities across the network at each time slot, we obtain the exact probability of accessibility between any two nodes. Finally, we discuss scenarios where the information regarding the presence and absence of links is initially available in the form of time duration (of presence or absence intervals) continuous probability distributions rather than discrete probabilities over time slots. We provide a method for transforming such distributions to discrete probabilities which enables us to apply the given bounds in this paper to a broader range of problem settings.