Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Layer Security Optimization for MIMO Enabled Visible Light Communication Networks

This paper investigates the optimization of physical layer security in multiple-input multiple-output (MIMO) enabled visible light communication (VLC) networks. In the considered model, one transmitter equipped with light-emitting diodes (LEDs) intends to send confidential messages to legitimate users while one eavesdropper attempts to eavesdrop on the communication between the transmitter and legitimate users. This security problem is formulated as an optimization problem whose goal is to minimize the sum mean-square-error (MSE) of all legitimate users while meeting the MSE requirement of the eavesdropper thus ensuring the security. To solve this problem, the original optimization problem is first transformed to a convex problem using successive convex approximation. An iterative algorithm with low complexity is proposed to solve this optimization problem. Simulation results show that the proposed algorithm can reduce the sum MSE of legitimate users by up to 40% compared to a conventional zero forcing scheme.
Robust Distributed Intrusion Detection System for Edge of Things

The edge computing paradigm has been adopted in many Internet-of-Things (IoT) applications to improve responsiveness and conserve communication resources. However, such high agility and efficiency come with increased cyber threats. Intrusion detection systems (IDS) have been the primary means for guarding networked computing assets against hacking attempts. The popular design methodology for IDS relies on the application of machine learning (ML) techniques that use intelligence data to classify malicious activities. However, in the realm of IoT, insufficient data is available to build IDS; hence a distributed intrusion system with continual data collection is primordial to refine the detection model. Such IDS is also subject to privacy constraints and should sustain robustness against data manipulation from internal attackers that degrade the ML model. This paper opts to fulfill these requirements by proposing a novel distributed IDS for IoT. The proposed system employs federated learning to enable privacy preservation and diminish the communication overhead. Our system promotes a reinforcement mechanism to ensure resiliency to data manipulation attacks by single or colluding internal actors. The validation results using recently released datasets demonstrate the effectiveness of our approach.
Max-Min Energy Efficiency for RIS-aided HetNets with Hardware Impairments and Imperfect CSI

Beamforming design is crucial to reconfigurable intelligent surface (RIS)-aided communication networks. However, most of the existing works assume ideal hardware and perfect channel state information (CSI), which are unrealistic assumptions in practical systems. In order to improve system robustness and user fairness, in this paper, we firstly study the max-min energy efficiency problem for RIS-aided heterogeneous networks under non-ideal hardware and imperfect CSI. Specifically, the joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase shift matrices of RISs is formulated as a nonconvex problem to maximize the minimum energy efficiency of femtocells subject to the constraints of the maximum transmit power of FBSs, the maximum cross-tier interference power of macrocell users, the minimum rates of femtocell users, and unit modulus of RISs. To facilitate the design, we develop an iterative block coordinate descent-based algorithm which exploits the semidefinite relaxation, the S-procedure, the successive convex approximation method, and the singular value decomposition method. Simulation results demonstrate the superiority of the proposed algorithm.
GCN-CAM: A new graph convolutional network-based fault diagnosis method with its interpretability analysis

Recently, graph neural network (GCN)-based fault diagnosis methods have been received increasing attention. However, regarding the obtained results, existing methods are often lack of interpretability. To solve this problem, in this paper, a graph convolutional network-class activation mapping (GCNCAM) model is established, which can interpret the results of GCN-based fault diagnosis method. Specifically, the definition of interpretability in GCN is firstly clarified, then the model uses GCN layer to extract features ZGCN of input measurements, it uses ZGCN to obtain the diagnostic results through the global average pooling (GAP) layer and fully connected layer. Next, the trained fully connected layer and a softmax layer are used to process the output value ZGCN directly, and the normalized interpretability vectors of measurements are obtained. To verify the effectiveness of the method, an experiment on a hardware-in-the-loop platform of the rectifier of a high-speed train is carried out. The experimental results show that the proposed GCN-CAM model can not only complete the task of fault diagnosis, but also provide a convincing explanation for the diagnosis results
Edge-cloud Collaborative Heterogeneous Task Scheduling in Multilayer Elastic Optical Networks

With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heteroge-neous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.
A diagnosis method for open-circuit faults in NPC three-level inverters based on novel sliding mode observer

To improve the robustness and rapidity of neutral point clamped (NPC) three-level inverter fault diagnosis, a novel sliding mode observer-based fault diagnosis method is proposed in this paper. Firstly, the mixed logic dynamic model of the inverter was established by using the current flow direction of the switch under normal and fault conditions, and a new sliding mode observer is designed to estimate the output current of the inverter under normal conditions. The current residual which is obtained by comparing the output current from the actual system and the observer is used to detect fault, and then combined with the actual output current, fault location is carried out. The new sliding mode observer can not only improve the convergence speed of the observer, but also effectively reduce chattering and improve the robustness of the fault diagnosis system. Finally, the effectiveness and feasibility of the proposed method are verified by simulation.
Honeypot-Enabled Optimal Defense Strategy Selection for Smart Grids

Smart grids have been increasingly spotted as high-profile targets of cyber assaults over the years. To better understand the cyber threat landscape, honeypots have been widely used in the smart grid security community, i.e., identifying unauthorized penetration attempts and observing the behaviors in such activities. In this paper, we propose a honeypot-enabled optimal defense strategy selection approach for smart grids, based on a novel stochastic game. Specifically, the interactions between the attacker and smart grid defender are captured using our designed stochastic game, a non-cooperative two-player game with incomplete information. We take into account various possible defenses from a smart grid defender and offensive strate-gies from the attacker. Then the Nash equilibrium is calculated by the stochastic game model, which is derived exhibiting an optimal defense strategy for the smart grid defender. Extensive simulation experiments demonstrate the effectiveness of the proposed scheme.
Offloading Optimization with Delay Constraint in the 3-tier Federated Cloud, Edge, and Fog Systems

Mobile edge computing and fog computing are promising techniques providing computation service closer to users to achieve lower latency. In this work, we study the optimal offloading strategy in the three-tier federated computation offloading system. We first present queueing models and closed-form solutions for computing the service delay distribution and the probability of the delay of a task exceeding a given threshold. We then propose an optimal offloading probability algorithm based on the sub-gradient method. Our numerical results show that our simulation results match very well with that of our closed-form solutions, and our sub-gradient-based search algorithm can find the optimal offloading probabilities. Specifically, for the given system parameters, our algorithm yields the optimal QoS violating probability of 0.188 with offloading probabilities of 0.675 and 0.37 from Fog to edge and from edge to cloud, respectively.
Study on Characteristics of Metric-aware Multipath Algorithms in Real Heterogeneous Networks

Multipath transmission is considered one of the promising solutions to improve wireless resource utilization where there are many kinds of heterogeneous networks around. Most scheduling algorithms rely on real-time network metrics, including delay, packet loss, and arrival rates, and achieve satisfying results in simulation or wired environments. However, the implicit premise of a scheduling algorithm may conflict with the characteristics of real heterogeneous wireless networks, which has been ignored before. This paper analyzes the real network metrics of three Chinese heterogeneous wireless networks under different transmission rates. To make the results more convincing, we conduct experiments in various scenarios, including different locations, different times of the day, different numbers of users, and different motion speeds. Further, we verify the suitability of a typical delay-aware multipath scheduling algorithm, Lowest Round Trip Time, in heterogeneous networks based on the actual data measured above. Finally, we conclude the characteristics of heterogeneous wireless networks, which need to be considered in a well-designed multipath scheduling algorithm.
On Strict Homogeneous Lyapunov Function for Generalized Homogeneous PI Controller

This paper investigates a generalized homogeneous PI (Proportional-Integral) control for a linear plant. A Lyapunov function for analysis of the closed-loop system is designed. For negative homogeneity degree, the obtained Lyapunov function becomes a strict Lyapunov function allowing an advanced analysis to be provided. In particular, a class of the disturbances to be rejected by the control law is characterized, a maximum control magnitude and the settling-time of the closed-loop system are estimated.
A Novel Cross-domain Access Control Protocol in Mobile Edge Computing

With the development of smart mobile terminals and mobile communication technologies, Mobile Edge Computing (MEC) has been applied to a variety of fields. However, MEC also brings new data security threats including the data access threat. To solve the cross-domain access control problem in MEC, this paper proposes a cross-domain access control protocol, named CDAC. In CDAC, a new user reputation evaluation strategy is proposed, which dynamically evaluates the comprehensive reputation of users based on different access behaviors of users, so that gateway nodes can evaluate user cross-domain requests. Meanwhile, different priorities are assigned according to user security levels to encourage users to regulate access behaviors to improve their reputations. Then, different gateway nodes implement cross-domain access control for users. The experiment results show that the proposed CDAC can provide efficient cross-domain access controls and achieve excellent system performances.
Safe Motion Planning with Tubes and Contraction Metrics

The recent proliferation of model predictive control (MPC) in safety-critical systems has placed additional emphasis on developing algorithms that have strict performance guarantees despite the presence of modeling error or external disturbances. This tutorial summarizes key results of combining contraction theory with MPC to enable provably-safe motion planning for robotic and aerospace systems. After a brief review of control contraction metrics, we summarize the fundamental result that any closed-loop contracting system has an associated invariant tube centered around a desired trajectory. It is then shown how these tubes can be systematically incorporated into the motion planning problem as an additional safety margin for systems with uncertain dynamics. Finally, several future research directions are discussed.
Antennas/PINs Selection and Joint Beamforming for High Rank LOS MmWave Communications

The reconfigurable intelligent surface (RIS), which converts the wireless channel into an intelligent transmission entity, has been regarded as one of the new paradigms for beyond fifth-generation (5G) wireless communication. One of the main functions of RIS is converting the none-line-of-sight (NLOS) channel to the line-of-sight (LOS) channel. However, the natural combination of RIS and millimeter wave (mmWave) communication results in a low rank channel caused by the high correlation of LOS channel and sparsely scattered mmWave channel. To tackle this problem, we investigate the uniform circular array (UCA) to leverage the potential circulant characteristic of two parallel UCAs. To be compatible with existent uniform planar array (UPA) systems, we develop the antennas/PINs selection and joint beamforming scheme which merely needs the relative position among transmitter, receiver, and RIS instead of real-time channel information to derive equivalent UCAs in traditional UPA systems. As the result, the low rank LOS channel can be converted to high rank channel due to the full rank feature of circulant matrix. Numerical results verify the superiority of our antennas/PINs selection and joint beamforming scheme.
Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space

We propose a data-driven control method for nonlinear dynamical systems based on the Koopman operator theory. Existing Koopman-based control methods apply linear optimal control methods after system identification by approximating the original cost function in the Koopman space. Therefore, errors in system identification and cost approximation deteriorate the control performance. On the other hand, the proposed method directly maximizes the control performance with reinforcement learning, where a controller is modeled by a neural network that consists of a linear quadratic regulator and an encoder that embeds data into the Koopman space. We experimentally demonstrate the effectiveness of the pro-posed method over existing Koopman-based and reinforcement learning-based methods with two nonlinear dynamical systems.
Empirical Analysis of Aging Effects on Preferential Attachment with a Massive Twitter Dataset

Social networks are prominently studied for addressing many pressing challenges, including the spread of diseases and misinformation, orchestrated influence campaigns, and evolution of biological species. A striking common feature of these diverse real networks is the growth of topological structure that is widely understood by the preferential attachment property of scale-free networks that conform to the power law. However, preferential attachment by itself cannot fully explain the phenomenon of aging observed in many complex networks. Therefore, in this paper, we empirically analyzed a massive Twitter dataset and proved that the popularity of nodes tends to “age” over time and that recent nodes get a better chance of forming new connections. We further corroborate these findings with a simple yet effective optimization technique that generates a more accurate in-degree distribution of the Twitter content network. Results show that our approach optimizes the degree distribution of preferential attachment, reducing the RH distance measurement below 0.5 for more than 50% of the Twitter topics in the dataset. By quantifying as an aging coefficient, we also demonstrate that the aging effect is non-uniform across the network during the same time period.
THE NEW TURBIGO-RHO 380 kV TRANSMISSION LINE: AN EXAMPLE OF THE USE OF UNDERGROUND XLPE CABLES IN A MESHED TRANSMISSION GRID

SUMMARY The need of a 380 kV transmission line between the Power plant of Turbigo, near Milan, and the substations at Ospiate and Bovisio was highlighted from several years ago, with the primary scope to avoid congestions consequent to the high power transiting in the area and to reinforce the West-East transmission axis in the North of Italy. Other benefits of this interconnection are the reduction of the overall transmission losses, the increase of power availability during summer and winter peak loads and in terms of better flexibility of operation. The project construction started at end of March 2005 and will be in service in Spring 2006. Due to the necessity to cross a highly urbanized area and to fulfil the stringent requirements in terms of environmental impact (electric and magnetic field limitation, visual impact, etc.) an underground XLPE cable system was decided to be installed throughout the whole sensitive area. The present paper describes in detail the Underground XLPE Cable system, which represents the first realization of this extension and importance in Italy and also one of the most important world-wide. The paper also describes an innovative solution adopted to minimize the Electromagnetic Field (EMF) when the cable systems are laid in the most sensitive areas; in this case the cables are installed in a trefoil configuration inside single plastic ducts and an effective EMF shield is provided all around the cables, constituted by a trough made of a special high magnetic permeability material. In order to increase safety of operation, special controls have been foreseen for the whole cable system, in particular the continuous monitoring of the temperature through fibre optics and the possibility to measure the partial discharges during the system life.
Time domain beamforming for UWB pulse signal

Based on analysis of principle of ultra wideband (UWB) space-time processing, a time domain beamforming approach for UWB pulse array reception is presented in the paper. The realization of variable delay circuits is addressed. The combination of digital delay line with fractional delay FIR filter for UWB signal digital processing in array reception is presented. The casual and stable filter is designed to compensate the channel dispersed delay accurately, which help to relieve the performance reduction caused by delay quantization error. In comparison with frequency domain processing, this method not only can improve the performance of directivity pattern remarkably but also have the advantage of easy design and low cost of computation.
Combined Forest: a New Supervised Approach for a Machine-Learning-based Botnets Detection

Nowadays, botnet-based attacks are the most preva-lent cyber-threats type. It is therefore essential to detect this kind of malware using efficient bots detection techniques. This paper presents our security anomalies detection system, based on a model that we named Combined Forest. Our approach consists of merging some pre-processed Decision Trees to highlight different kinds of botnet by detecting their intrinsic exchanges. Using a supervised data approach, each tree is built from a labelled dataset. In order to achieve this, we aggregate the IP-flows into Traffic-flows to extract key features and avoid over-fitting. Then, we tested different machine learning algorithms and selected the most suitable one. After that, many experiments have been done to determine the best parameters and design the most accurate, adantative and efficient model.
Privacy-Preserving Average Consensus in Finite Time

Average consensus has wide applications in distributed networks ranging from computing and control, where each node continually receives information from its neighbors and updates its state to reach the average. The existing average consensus algorithm may result in the disclosure of private information about the nodes as each node sends explicit state values to its neighbors. In this paper, we propose a state decomposition based privacy-preserving average consensus algorithm that allows a strongly connected directed network system to compute the accurate average value in a finite number of time steps while each node can avoid its initial state value being disclosed. More specifically, we improve on the classical ratio consensus algorithm, in which the average value can be computed in a finite number of time steps by the final value theorem. Numerical examples verify the effectiveness of our algorithm.
An orbital symmetry-based approach to observer design for systems with disturbances

We propose a framework for designing global observers for nonlinear systems with disturbances under geometric conditions based on orbital symmetries. Under some additional restrictions these orbital symmetry-based conditions boil down to geometric homogeneity (at infinity) conditions. Our observers are the result of the combination of a first filter, a state norm estimator, with a second filter adaptively tuned by the first and when compared with the existing literature have a completely novel structure, inherited by the orbital symmetry-based conditions. The second filter adaptively exploits the properties of orbital symmetries of the system to achieve global convergence properties by steering, first, the state estimate close to the state trajectory and acting locally afterwards.
Dynamic Service Migration with Partially Observable Information in Mobile Edge Computing

Service migration, determining when, where and how to migrate the ongoing service, is of paramount importance in mobile edge computing (MEC) for provisioning high quality of service to mobile users. With respect to high network dynamics and stringent delay requirements, service migration is a rather challenging issue in MEC. In this paper, we formulate service migration as a partially observable Markov decision process (POMDP) based on the fact that an edge server can only obtain partial users' information, or the information of its own serving users. A learning-based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm, where the latent space model is used to infer the full state of the environment based on the partial information observed, and the cross-entropy planning algorithm is used to search the best service migration strategy. Numerical results show that our proposed iSMA reduces the service delay by about 58% when compared with a well-known deep learning-based solution.