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This article addresses the event-triggered privacy preserving leader-follower consensus control problem for linear Multi-Agent Cyber-Physical Systems (MACPS) subject to cyber-attacks. A novel framework is proposed that combines virtual dynamics, event-triggered communication, and cyber-attack estimation to achieve output consensus while preserving agent privacy and system resilience against cyber-attacks. By introducing event-triggered virtual nodes, we enhance privacy of the agents while also managing communication resources efficiently by reducing data transmissions. To mitigate the effects of cyber-attacks, auxiliary systems are employed to estimate cyber-attack signals, enabling the design of a resilient control law to achieve leader-follower consensus in a Uniformly Ultimately Bounded (UUB) sense. Finally, Simulation results demonstrate the effectiveness of the proposed method in achieving leader-follower consensus despite cyber-attacks and communication limitations.

In this paper, we investigate the problem of event-based covert attack detection in cyber-physical systems. We first investigate event-based covert attacks and discuss how covert attacks can be launched in event-based systems. We then use the capabilities of the event-based communication to detect these types of cyber-attacks. The notion of the auxiliary systems is used in our detection mechanism along with considering several anomaly detectors which could better detect anomalies in comparison to a single anomaly detector. Furthermore, prevalent assumptions in the literature about capabilities of the system (e.g. having secure channels or considering dynamics to be unknown to the attacker) are relaxed and finally, our approach is verified by providing several numerical case studies.

The prediction of gene mutation of bacteria when exposed to different conditions is beneficial in the development of drugs and vaccines. However, the existing prediction models are suboptimal in their performance. In this work, we propose a time-series approach where we model mutation occurrence sequentially. In our approach, the most connected cofunctional genes are predicted first, and used as additional input features in subsequent predictions. We train a neural network to predict gene mutations in Escherichia coli from the exposure conditions and the predicted state of the cofunctional genes. We test the performance on adaptive laboratory evolution experiments curated from the literature. Results show the cofunctional network-based features improved the predictive performance by a maximum 33.53% and 45.18% in the area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC), respectively. This study supports the feasibility of gene mutation prediction and demonstrates that the functional relationship between genes can be predicted first and then used as features to boost the predictability of its node genes.

BACKGROUND: The desire to understand genomic functions and the behavior of complex gene regulatory networks has recently been a major research focus in systems biology. As a result, a plethora of computational and modeling tools have been proposed to identify and infer interactions among biological entities. Here, we consider the general question of the effect of perturbation on the global dynamical network behavior as well as error propagation in biological networks to incite research pertaining to intervention strategies. RESULTS: This paper introduces a computational framework that combines the formulation of Boolean networks and factor graphs to explore the global dynamical features of biological systems. A message-passing algorithm is proposed for this formalism to evolve network states as messages in the graph. In addition, the mathematical formulation allows us to describe the dynamics and behavior of error propagation in gene regulatory networks by conducting a density evolution (DE) analysis. The model is applied to assess the network state progression and the impact of gene deletion in the budding yeast cell cycle. Simulation results show that our model predictions match published experimental data. Also, our findings reveal that the sample yeast cell-cycle network is not only robust but also consistent with real high-throughput expression data. Finally, our DE analysis serves as a tool to find the optimal values of network parameters for resilience against perturbations, especially in the inference of genetic graphs. CONCLUSION: Our computational framework provides a useful graphical model and analytical tools to study biological networks. It can be a powerful tool to predict the consequences of gene deletions before conducting wet bench experiments because it proves to be a quick route to predicting biologically relevant dynamic properties without tunable kinetic parameters.

MOTIVATION: The inference of gene regulatory networks (GRNs) from DNA microarray measurements forms a core element of systems biology-based phenotyping. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today's biology. However, little focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression profiles. RESULTS: Here, we present a computational framework that combines the formulation of probabilistic graphical modeling, standard statistical estimation, and integration of high-throughput biological data to explore the global behavior of biological systems and the global consistency between experimentally verified GRNs and corresponding large microarray compendium data. The model is represented as a probabilistic bipartite graph, which can handle highly complex network systems and accommodates partial measurements of diverse biological entities, e.g. messengerRNAs, proteins, metabolites and various stimulators participating in regulatory networks. This method was tested on microarray expression data from the M3D database, corresponding to sub-networks on one of the best researched model organisms, Escherichia coli. Results show a surprisingly high correlation between the observed states and the inferred system's behavior under various experimental conditions. AVAILABILITY AND IMPLEMENTATION: Processed data and software implementation using Matlab are freely available at https://github.com/kotiang54/PgmGRNs. Full dataset available from the M3D database.

From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this article demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called "neuroembryogenesis." This framework shares some of the key behavioral aspects of the biological brain, such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of this article demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open source, and the code is shared with the scientific community at http://www.feagi.org.

Recent developments have made autonomous vehicles (AVs) closer to hitting our roads. However, their security is still a major concern among drivers as well as manufacturers. Although some work has been done to identify threats and possible solutions, a theoretical framework is needed to measure the security of AVs. In this paper, a simple security model based on defense graphs is proposed to quantitatively assess the likelihood of threats on components of an AV in the presence of available countermeasures. A Bayesian network (BN) analysis is then applied to obtain the associated security risk. In a case study, the model and the analysis are studied for GPS spoofing attacks, to demonstrate the effectiveness of the proposed approach for a highly vulnerable component.

Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, which can lead to large-scale cascading failures. However, the self-healing ability emerges as a valuable opportunity where the overlaying cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, this paper considers a graphical model representation of an interdependent cyber-physical system, in which nodes represent various cyber or physical functionalities, and edges capture the interactions between the nodes. A message-passing algorithm is proposed for this representation to study the dynamics of failure propagation and healing. By conducting a density evolution analysis for this algorithm, network reaction to initial disruptions is investigated. It is proved that as the number of message-passing iterations increases, the network reaches a steady-state condition that would be either a complete healing or a complete collapse. Then, a sufficient condition is derived to select the network parameters to guarantee the complete healing of the system. The result of the density evolution analysis is further employed to jointly optimize the design of cyber and physical networks for maximum resiliency. This analytical framework is then extended to the cases where the propagation of failures in the physical network is faster than the healing responses of the cyber network. Such scenarios are of interest in many real-life applications such as smart grid. Finally, extensive numerical results are presented to verify the analysis and investigate the impact of the network parameters on the resiliency of the network.

Coupling cyber and physical systems gives rise to numerous engineering challenges and opportunities. An important challenge is the contagion of failure from one system to another, that can lead to large scale cascading failures. On the other hand, self-healing ability emerges as a valuable opportunity where the overlay cyber network can cure failures in the underlying physical network. To capture both self-healing and contagion, we introduce a factor graph representation of inter-dependent cyber-physical systems in which factor nodes represent various node functionalities and the edges capture the interactions between the nodes. We develop a message passing algorithm to study the dynamics of failure propagation and healing in this representation. Through applying a fixed point analysis to this algorithm, we investigate the network reaction to initial disruptions. Our analysis provides simple yet critical guidelines for choosing network parameters to achieve resiliency against cascading failures.

In this paper we consider a cognitive radio network with access to N licensed primary frequency bands and their usage statistics, where the decentralized secondary users are subject to certain inter-network interference constraint. In particular, to limit the interference to the primary network, secondary users are equipped with spectrum sensors and are capable of sensing and accessing a limited number of channels at the same time due to hardware limitations. We consider both the error-free and erroneous spectrum sensing scenarios, and establish the jointly optimal random sensing and access scheme, which maximizes the secondary network expected sum throughput while honoring the primary interference constraint. We show that under certain conditions the optimal sensing and access scheme is independent of the primary frequency bandwidths and usage statistics; otherwise, they follow water-filling-like strategies. Moreover, we show that the performance of the secondary network depends on the ratio between the “opportunity-detection” probability and the “mis-detection” probability if the former is larger; otherwise, it depends on the ratio between the “false-alarm” probability and the “detection” probability. Finally, we demonstrate a binary behavior for the optimal access scheme at each channel, depending on whether the opportunity-detection probability or mis-detection probability is larger in that channel.

This paper investigates properties of polar codes that can be potentially useful in real-world applications. We start with analyzing the performance of finite-length polar codes over the binary erasure channel (BEC), while assuming belief propagation as the decoding method. We provide a stopping set analysis for the factor graph of polar codes, where we find the size of the minimum stopping set. We also find the girth of the graph for polar codes. Our analysis along with bit error rate (BER) simulations demonstrate that finite-length polar codes show superior error floor performance compared to the conventional capacity-approaching coding techniques. In order to take advantage from this property while avoiding the shortcomings of polar codes, we consider the idea of combining polar codes with other coding schemes. We propose a polar code-based concatenated scheme to be used in Optical Transport Networks (OTNs) as a potential real-world application. Comparing against conventional concatenation techniques for OTNs, we show that the proposed scheme outperforms the existing methods by closing the gap to the capacity while avoiding error floor, and maintaining a low complexity at the same time.

Many analytic results for the connectivity, coverage, and capacity of wireless networks have been reported for the case where the number of nodes, n , tends to infinity (large-scale networks). The majority of these results have not been extended for small or moderate values of n ; whereas in many practical networks, n is not very large. In this article, we consider finite (small-scale) wireless sensor networks. We first show that previous asymptotic results provide poor approximations for such networks. We provide a set of differences between small-scale and large-scale analysis and propose a methodology for analysis of finite sensor networks. Furthermore, we consider two models for such networks: unreliable sensor grids and sensor networks with random node deployment. We provide easily computable expressions for bounds on the coverage and connectivity of these networks. With validation from simulations, we show that the derived analytic expressions give very good estimates of such quantities for finite sensor networks. Our investigation confirms the fact that small-scale networks possess unique characteristics different from their large-scale counterparts, necessitating the development of a new framework for their analysis and design.

Generalized Extrinsic Information Transfer (GEXIT) charts were introduced as an extension of EXIT charts which have an extensive use in analysis and design of many iterative schemes including Low-Density Parity-Check (LDPC) codes. While a powerful as well as an insightful concept, their full potential as a designing tool for LDPC code ensembles has not been realized due to some missing steps. This papers aims at filling these gaps by proving some important properties of GEXIT charts and using them to design capacity-approaching LDPC code ensembles. The primary results on GEXIT charts are limited to regular variable and check node degrees. Moreover, variable node GEXIT curves have only been derived for the case where no physical channel is present. In a recent paper, GEXIT curves for irregular variable node and check node degree distributions have been derived. In this paper, we derive GEXIT charts of LDPC code ensembles over binary-input output-symmetric memoryless channels with any channel parameter. For the case of binary symmetric channel, we derive closed form expression for the GEXIT curve of variable nodes. We also propose to use an alternative representation of GEXIT charts in which we plot the inverse of variable node GEXIT curve together with dual GEXIT curve of the check node. We prove that the area theorem still holds in this case. Using these results, we analyze and design capacity-approaching LDPC codes using GEXIT charts.

In this paper, we study polar codes from a practical point of view. In particular, we study concatenated polar codes and rate-compatible polar codes. First, we propose a concatenation scheme including polar codes and Low-Density Parity-Check (LDPC) codes. We will show that our proposed scheme outperforms conventional concatenation schemes formed by LDPC and Reed-Solomon (RS) codes. We then study two rate-compatible coding schemes using polar codes. We will see that polar codes can be designed as universally capacity achieving rate-compatible codes over a set of physically degraded channels. We also study the effect of puncturing on polar codes to design rate-compatible codes.

Analysis of finite wireless networks is a fundamental problem in the area of wireless networking. Today, due to the vast amount of literature on large-scale wireless networks, we have a fair understanding of the asymptotic behavior of such networks. However, in real world we have to face finite networks for which the asymptotic results cease to be valid. We refer to networks as being finite when the number of nodes is less than a few hundred. Here we study a model of wireless networks, represented by random geometric graphs. In order to address a wide class of the network's properties, we study the threshold phenomena. Being extensively studied in the asymptotic case, the threshold phenomena occurs when a graph theoretic property (such as connectivity) of the network experiences rapid changes over a specific interval of the underlying parameter. Here, we find an upper bound for the threshold width of finite line networks represented by random geometric graphs. These bounds hold for all monotone properties of such networks. We then turn our attention to an important non-monotone characteristic of line networks which is the Medium Access (MAC) layer capacity, i.e. the maximum number of possible concurrent transmissions. Towards this goal, we provide a linear time algorithm which finds a maximal set of concurrent non-interfering transmissions and further derive lower and upper bounds for the cardinality of the set. Using simulations, we show that these bounds serve as reasonable estimates for the actual value of the MAC-layer capacity.

Conventional hybrid RF and optical wireless communication systems make use of parallel Free Space Optical (FSO) and Radio Frequency (RF) channels to achieve higher reliability than individual channels. True hybridization can be accomplished when both channels collaboratively compensate the shortcomings of each other and thereby improve the performance of the system as a whole. In this paper, we propose a novel coding paradigm called "Hybrid Channel Coding" that not only optimally achieves the capacity of the combined FSO and RF channels but also can potentially provide carrier grade reliability (99.999%) for hybrid FSO/RF systems. The proposed mechanism uses non-uniform and rate-compatible LDPC codes to achieve the desired reliability and capacity limits. We propose a design methodology for constructing these Hybrid Channel Codes. Using analysis and simulation, we show that by using Hybrid Channel Codes, we can obtain significantly better availability results in terms of the required link margin while the average throughput obtained is more than 33% better than the currently existing systems. Also by avoiding data duplication, we preserve to a great extent the crucial security benefits of FSO communications. Simulations also show that Hybrid Channel Codes can achieve more than two orders of magnitude improvement in bit error rate compared to present systems.

In this paper we propose a framework to study the asymptotical capacity of vehicular ad hoc networks (VANET)s when nodes are expected to communicate only when they reside in a certain distance of each other. This is quite a favorable scenario for VANETs when they are utilized for accident avoidance and safety applications. Moreover we develop formulations to predict the behavior of VANETs with specific geometrical shapes like the single road and grid topologies. Also, the capacity scaling behavior of VANETs when a node needs to transmit to all its neighbors within a certain range, is studied. Results are obtained by combining geometrical analysis, network flow arguments, and probabilistic study of VANETs.