Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Knowledge aided Neural Network for Wireless Channel Estimation

Channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) transmission is well investigated with model based approaches. Recent effort also explores the data driven approaches to exploit the capabilities of Neural Networks (NNs) to estimate the channel. These models are mostly being developed as black box without any anchor to the theory of wireless signal propagation. We propose a NN model, where the structure and parameters are derived from the domain knowledge of wireless signal and channel characteristics. Our model is developed in two stages: the first stage handles the noise reduction, while the second stage extracts the channel characteristics to reduce error caused by multipath. We have used the knowledge of signal to noise ratio and subcarrier correlation due to channel delay spread to empower the two stages of the proposed model. Our results also show that induction of domain knowledge results in reduction of data dependency by 60%. Our model outperforms the practical model based methods as well as blind data driven approaches. It achieves ∼10 dB improvement over Least Square channel estimation.
Global Solution to an H-infinity Control Problem with Input Nonlinearity

This paper gives a global solution to an H-infinity control problem for systems with symmetric state matrix and state-dependent input matrix. A simple, closed-form expression for the minimizing controller is obtained. This is in contrast to already established theory in which nonlinear H-infinity problems are solved locally. The result is then illustrated through an example, and the potential for controller sparsity is highlighted. Applications to large-scale systems are discussed.
Ear in the Sky: Terrestrial Mobile Jamming to Prevent Aerial Eavesdropping

The emerging unmanned aerial vehicles (UAVs) pose a potential security threat for terrestrial communications when UAVs can be maliciously employed as UAV-eavesdroppers to wiretap confidential communications. To address such an aerial security threat, we present a friendly jamming scheme named terrestrial mobile jamming (TMJ) to protect terrestrial confidential communications from UAV eavesdropping. In our TMJ scheme, a jammer moving along the protection area can emit jamming signals toward the UAV-eavesdropper so as to reduce the eavesdropping risk. We evaluate the performance of our scheme by analyzing a secrecy-capacity maximization problem subject to the legitimate connectivity and eavesdropping probability. In addition, we investigate the optimized position for the jammer as well as its jamming power. Simulation results verify the effectiveness of the proposed scheme.
Adaptive Access Mode Selection in Space-Ground Integrated Vehicular Networks

Space-ground integrated vehicular networks (SGIVNs) are envisioned as a promising architecture to support multifarious vehicular services with enhanced network flexibility and reliability. Access mode selection (AMS) is of capital importance in the SGIVN for the ingenious cooperation among different network segments to exploit their complementary advantages. In this paper, we investigate the AMS problem for vehicles in the SGIVN by taking distinct features of satellite networks (long propagation delay) and terrestrial networks (frequent handover) into account. In light of the high vehicle/satellite mobility and dynamic data packet arrivals, we formulate a stochastic integer programming problem of sequential AMS to maximize vehicles' long-term data rate. To cope with the time-varying network dynamics, we leverage a Markov decision process framework to model the evolution of vehicle states. For the special case with known stochastic model of data packet arrivals, we transform the problem into a linear programming problem that can be solved with low complexity. For the general case without the data packet arrival model, we propose a reinforcement learning-based algorithm to make adaptive AMS decisions to keep pace with network dynamics. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of data rate under different data packet arrival patterns and vehicle velocities.
A Synchronization Protocol for Multi-User Cell Signaling-Based Molecular Communication

Molecular Communications (MC) networks comprise multiple devices performing coordinated complex tasks, such as detecting types of cancer and smart drug delivery. Signaling-based MC uses molecules as information carriers between signaling cells. In this context, synchronization is jointly paramount and challenging since the system must overcome the limitation of molecular propagation to make sure computationally deprived bio-devices can communicate. On top of that, a multi-user increases this system challenges as possible co-channel interference causes errors or failures. Bio-devices present severe computational and communication limitations, being this last one essentially unidirectional. This paper presents the first synchronization protocol between signaling cells for multi-user MC. Results have shown the convergence time concerning different network sizes from 12 to 60 nodes.
Monitoring and Diagnosis of Health State of Converter Tilting Trunnion Bearing Based on Stress Wave Analysis Technology

Converter tilting is one of the key equipment for steelmaking production, which belongs to the “start-up work system”. The weight of the heavy tilting part of the converter can reach hundreds of tons. In addition to the basic static load, the mechanical structure also bears the dynamic load caused by starting and braking. Due to the working characteristics of the converter tilting “low speed and heavy load”, the previous vibration monitoring methods were difficult to be effective, so stress wave analysis technology was introduced for diagnosis. During equipment maintenance, through the verification of opening the cover, it was effectively diagnosed that there is lubricating oil pollution inside the converter trunnion bearing, and the abnormal wear of the inner and outer rings of the bearing caused the early failure phenomena such as excessive clearance of the fit. The opening phenomenon and the stress wave before the opening The diagnostic conclusions drawn from the technical analysis are highly consistent. Fully verify the actual effect of stress wave analysis technology on converter tilting, and carry out preventive maintenance in the early stage of equipment failure, effectively avoid the occurrence of major equipment safety accidents and unplanned emergency repairs, and ensure the normal operation of the enterprise.
Profiling IoT Botnet Activity in the Wild

Undoubtedly, the Internet of Things (IoT) contributes significantly to daily mission-critical processes underpinning a number of socio-technical systems. Conversely, its rapid adoption has extensively broadened the cyber-threat landscape by virtue of low-cost IoT devices that are manufactured and deployed with minimal security. Evidently, vulnerable IoT devices are utilised by attackers to participate into Internet-wide botnets in order to instrument large-scale cyber-attacks and disrupt critical Internet services. Since the 2016 outbreak of the first IoT Mirai botnet there has been a continuous evolution of Mirai-like variants. Tracking these botnets is challenging due to their varying structural characteristics, and also due to the fact that malicious actors continuously adopt new evasion and propagation strategies. This work provides a new measurement study highlighting specific behavioural properties of Mirai-like botnets in terms of their propagation. We provide a comprehensive analysis conducted on real Cyber Threat Intelligence (CTI) feeds gathered for a period of 7 months from globally distributed attack honeypots and pinpoint the evolutionary port scanning patterns, targeted vulnerabilities and preferred services pursued by Mirai-like botnets. We identify the most frequently active Mirai-like malware binaries and we are the first to report the evolution of a new, P2P-based variant. In parallel, we provide evidence related to the lack of vendor-specific patching through highlighting unpatched vulnerabilities. Moreover, we pinpoint the inadequacy of widely used IP blacklisting databases to timely list malicious IP addresses. Thus, arguing in fair of integrating honeypot information from diverse Internet vantage points within the design of next generation botnet defence mechanisms.
On feedforward control of piezoelectric dual-stage actuator systems

The feedforward control design problem for a single-axis dual-stage actuator system with piezoelectric actuator at the short-stroke is analyzed in this paper. With such actuator layout, the main question is how to balance the contribution of the individual actuators in a efficient manner, while complying to actuators limitations. A control configuration and a sequential design methodology are proposed to take into account interactions between actuators. In addition, various feedforward controller design strategies that conform to the configuration proposed are presented, such as inversion based feedforward, mass feedforward and standard compliance compensation. Based on observed shortcomings of each feedforward design, a novel mixed compliance compensation feedforward controller is presented. Results are analyzed in terms of their physical interpretations and simulation studies.
NGS: Mitigating DDoS Attacks using SDN-based Network Gate Shield

The Internet of Things (IoT) implements a tremendous environment of extensive data streams, whereby any suspicious activities should be detected to safeguard systems' reliability and availability. Distributed Denial of Service (DDoS) attack is a major threat on computer networks, in which an attacker can send huge traffic with multiple IP addresses or machines. In this work, we focus on detecting DDoS attacks, and design Network Gate Shield (NGS), a tool that works on SDN architecture based on the RYU controller. It can examine the traffic trustworthiness, and then determine whether the current traffic is normal based on packet specification, such as the average packet size and the packet per-sec threshold. In the evaluation with an emulated environment, our experimental results indicate that NGS is viable and effective in mitigating DDoS traffic compared with several similar detection approaches.
Machine Learning based Root Cause Analysis for SDN Network

Nowadays, the rapid growth of the Internet makes network management more complex due to various and com-plicated network problems. In the past, network administrators implemented troubleshooting approaches (e.g., ping, traceroute, etc.) manually to identify the root cause of problems. However, it is not effective due to human intervention and an increase of network devices. Consequently, the root cause analysis is considered by the research community. There are existing studies for the root cause analysis without human intervention (e.g., statistical approaches, heuristic algorithms, etc.). However, these approaches show limited performance (e.g., due to complex threshold identifcation, etc.). The emerging of machine learning (ML) and deep learning is a potential solution to overcome this obstacle, offering an opportunity to develop an effective root cause analysis approach. Therefore, in this paper, we propose a root cause analysis approach using ML and time-series network parameters to identify the root cause of problems in the network. In this approach, we consider balancing the accuracy and the time complexity of ML algorithms to select an appropriate ML technique. Moreover, we contribute troubleshooting datasets to identify three kinds of root causes including link failure, switch failure and buffer overload. The experimental results show that the proposal can achieve approximately 97 percent of precision, recall and f1-score in considered scenarios and require less processing time (only require 0.00143 ms for a sample) in comparison with other ML algorithms.
Delay-Aware Power Control for Downlink Multi-User MIMO via Constrained Deep Reinforcement Learning

We investigate the downlink transmission for multi-user multi-input multi-out (MU-MIMO) system, in which the regularized zero forcing (RZF) precoder is adopted and the power allocation and regularization factor are optimized. Our aim is to find a power allocation and regularization factor control policy that can minimize the long-term average power consumption subject to long-term delay constraint for each user. The induced optimization problem is formulated as a constrained Markov decision process (CMDP), which is efficiently solved by the proposed constrained deep reinforcement learning algorithm, called successive convex approximation policy optimization (SCAPO). The SCAPO is based on solving a sequence of convex objective/feasibility optimization problems obtained by replacing the objective and constraint functions in the original problems with convex surrogate functions. At each iteration, the SCAPO merely needs to estimate the first-order information and solve a convex surrogate problem that can be efficiently parallel tackled. Moreover, the SCAPO enables to reuse old experiences from previous updates, thereby significantly reducing the implementation cost. Numerical results have shown that the novel SCAPO can achieve the state-of-the-art performance over advanced baselines.
A Fog-Aided Privacy-Preserving Truth Discovery Framework over Crowdsensed Data Streams

With the proliferation of mobile and wearable devices, mobile crowdsensing (MCS) is becoming a new paradigm for data collection and analysis. To effectively identify truthful information from crowdsensed data without privacy leakage, privacy-preserving truth discovery (PPTD) has gained much attention recently. Existing works either didn't consider real-time applications over data streams or failed to achieve enough efficiency for a large group of workers. In this paper, we propose FPTD, a Fog-aided Privacy-preserving Truth Discovery framework which is secure and efficient in handling real-time applications with a large group of workers. To reduce overhead, we adopt cloud-fog computing architecture to divide the complete worker group into many smaller ones. Then we design a unique secure aggregation protocol SecAgg which can securely and efficiently aggregate inputs from workers in smaller groups. Finally, we give detailed construction of FPTD, an efficient truth discovery framework based on SecAgg for real-time applications. Through extensive experiments and security analysis, we demonstrate that both SecAgg and FPTD are secure and efficient.
Latency-aware VNF Protection for Network Function Virtualization in Elastic Optical Networks

In network function virtualization (NFV), the customer may request a set of virtual network functions (VNFs) that the customer traffic will go through. To accommodate such requests, the service providers have to embed the requested VNFs onto the substrate network (SN) to form an actual traffic forwarding path called service function path (SFP). In the elastic optical network (EON), how to protect the running network services against VNF failures becomes an attractive research focus. Most existing work concentrates on the protection or restoration of the physical node or fiber link failures in the SN. Few research attention has been paid to the failure of the virtual machines running a VNF. In this paper, we study how to protect VNFs when a VNF failure occurs in an EON. We define a new VNF failure protection cover (VFPC) problem and mathematically formulate VFPC. We propose a protection cover list based VNF protection (PCL-VP) algorithm against any single VNF failure while satisfying the latency requirement. Extensive simulations and analysis show the effectiveness of the proposed algorithm.
On how neural networks enhance quantum state tomography with limited resources*

Quantum state tomography is defined as a process of reconstructing the density matrix of a quantum state and is an important task for various emerging quantum technologies. In this work, we propose a general quantum state tomography framework that employs deep neural networks to reconstruct quantum states from a set of measurements with high efficiency. In particular, we apply it to two cases, including few measurement copies and incomplete measurement. Numerical results demonstrate that the proposed method exhibits a significant potential to achieve high fidelity for quantum state tomography when measurement resources are limited.
NeuralMon: Graph Neural Network for Flow Measurement Allocation

Fine-grained and accurate network flow measurements are essential for various network management tasks. In recent years, the evolution of programmable networks enables flow measurement on the switch. However, limited hardware resources on programmable switches drive the shift of measurement from a single switch to network-wide coordinations. This paper aims to optimize the allocation strategy of flow measurement among switches under the objective of measurement coverage and accuracy in network-wide measurement scenarios. We design a Graph Neural Network model, NeuralMon, that can model and solve the above problem precisely. NeuralMon converts network topologies and network flows into a hypergraph and transforms the flow measurement task allocation problem into a node classification problem. NeuralMon is effective in learning the task allocation solution from the network topologies and flows directly. Even on untrained real-world network topologies, NeuralMon still provides excellent performance.
Machine Learning Aided Path Loss Estimator and Jammer Detector for Heterogeneous Vehicular Networks

Heterogeneous vehicular communications aim to improve the reliability, security and delay performance of vehicle-to-vehicle (V2V) communications, by utilizing multiple commu-nication technologies. Predicting the path loss through conventional fitting based models and radio frequency (RF) jamming detection through rule based models of different communication schemes fail to address comprehensive mobility and jamming scenarios. In this paper, we propose a machine learning based adaptive link quality estimation and jamming detection scheme for the optimum selection and aggregation of IEEE 802.11p and Vehicular Visible Light Communications (V-VLC) technologies targeting reliable V2V communications. We propose to use Random Forest regression and classifier based algorithms, where multiple individual learners with diversity are trained by using measurement data and the final result is obtained by averaging outputs of all learners. We test our framework on real-world road measurement data, demonstrating up to 2.34 dB and 0.56 dB Mean Absolute Error (MAE) improvement for V-VLC and IEEE 802.11p path loss prediction compared to fitting based models, respectively. The proposed jamming presence detection scheme yields 88.3% accuracy to detect noise interference injection for IEEE 802.11p links, yielding 3% better prediction performance than previously proposed deep convolutional neural network (DCNN) based scheme.
Uninterrupted Connectivity Time in THz Systems Under User Micromobility and Blockage

Terahertz (THz) band is considered as the main candidate for new radio access technology in sixth-generation (6G) cellular systems. However, the performance of these systems will be severely affected by not only blockage but user equipment (UE) micromobility in hands of a user. The negative effects of these phenomena can be alleviated by utilizing the multi-connectivity functionality that allows UE to maintain two or more links to nearby base stations (BS) and use them when the currently active link is lost. By accounting for THz specific propagation, antenna and beamsearching design, the density of THz BS deployment, and multi-connectivity operation, we investigate the successful session completion probability under both types of impairments. Our results indicate that the gains of multi-connectivity are observed up to 5 simultaneously supported links and heavily depend on the application outage tolerance time and is mostly affected by micromobility. To improve it, one needs to ensure that the application may tolerate outage caused by beamsearching time which is on the order of milliseconds.
DeepDRAMA: Deep Reinforcement Learning-based Disaster Recovery with Mitigation Awareness in EONs

Elastic Optical Networks (EONs) have become a promising solution to satisfy the dramatic growth of bandwidth demand due to 5G and cloud applications. Due to the flexibility of resource allocation, EONs provide high spectrum utilization efficiency, and because of this, developing efficient policies to ensure the survivability of EONs is a challenging problem. A well-designed disaster management plan is needed to prevent data loss during network failures and large-scale disasters. The bottleneck problem caused by disabled parts of the network causes difficulties for disaster recovery. Depending on the disaster, even traffic that may be far away from the disaster may be impacted by it. In this paper, we propose a new approach to disaster management using machine learning to facilitate efficient recovery. In addition to traffic immediately affected by the disaster, all traffic which is “close to” the disaster is re-routed and re-assigned with possibly degraded service, while requests “far from” the disaster are left unaffected. A deep reinforcement learning disaster recovery algorithm with mitigation awareness (DeepDRAMA) is proposed for recovery. A novel deep reinforcement learning agent is designed and trained for the agent to select the appropriate level of service degradation for re-assigned traffic. Simulation results show the performance improvement with DeepDRAMA.
A Study on Electric Vehicle Battery Ageing Through Smart Charge and Vehicle-to-Grid Operation

Electrification of transportation means brings positive impacts to the environment because of reduced fossil fuel depletion and related carbon emissions. Critical obstacles remain in terms of battery costs and their expected life. Vehicle-to-grid technologies can deliver benefits to support electrical power grid and vehicle owner, while their practical implementation faces challenges due to the concerns over accelerated battery degradation. This study presents the evaluation of battery degradation through different smart charge strategies and vehicle-to-grid scenarios. The simulation results show that the developed smart charge schemes can mitigate the battery ageing up to 5% while lowering the charge cost from 30 - 60% as comparing to the conventional charge method within the first five days operation of the battery. In addition, the calendar ageing can be diminished upto 80% by participating in suitable V2G scenario.
Reinforcement Learning for Task Placement in Collaborative Cloud- Edge Computing

With the advantage of being close to the network, edge cloud-enabled computing mode brings flexibility to task scheduling. However, with the heterogeneity of computing resources between cloud and edge cloud, and the complexity of computing and communication processes between multi-edge cloud, challenges have been brought to the deployment and computing of tasks in cloud-edge collaborative environments. In order to solve this challenge, firstly a deep reinforcement learning controller based cloud-edge collaborative computing framework has been proposed. Then a system QoS model has been estab-lished considering both the user benefits and the service provider benefits. By using deep Q-network, a deep reinforcement learning based collaborative task placement algorithm has been proposed for dynamically optimizing the target system utility. Finally, the experimental results show that the proposed method has a good learning ability for the computing cost of cloud and edge cloud as well as the communication cost between multi-edge cloud. In addition, compared with Q-table learning, random computing and cloud computing, a 10% improvement of system utility has been achieved with the proposed method.