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The repercussion of increased electric vehicle (EV) charging demand is notable at the distribution grid especially during the cold morning, while users tend to precondition their vehicles before leaving their premises. Moreover, due to the price declination, a tendency of installing level 2 chargers in residential premises is anticipated, which should stimulate the appearance of a new peak to the residential load profile. Hence, multiple scenarios of preconditioning are simulated, and the corresponding network’s quality metrics (e.g., voltage level and power losses) are assessed to analyze the impact. And a remarkable consequence is observed. As a consequence, to mitigate the consequences and manage the new peak load, the optimal reconfiguration of network is implemented, and unfortunately, with a larger number of EVs, this technique fails to attain the minimum voltage level. Therefore, leveraging this high number of EVs, instead of relying on the network reconfiguration, power is assumed to be injected from idle EVs through vehicle-to-grid (V2G) energy transmission. An integer linear program is formed to schedule a set of EVs in participating in V2G, and the outcome indicates that V2G alone could not compensate for the disturbance in the network. Accordingly, a hybrid method of V2G and reconfiguration is proposed and evaluated to assist the network in handling the new peak load, and this hybrid solution reduces power losses in the network by 50% on average and maintains the voltage level above the operational threshold of 0.95 p.u. [ABSTRACT FROM AUTHOR]

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we investigate the issue of semantics in non real-time communication scenarios, that is, without stringent information delivery constraints. While semantics has been widely used in the context of communications, the definition of the semantic metrics has been limited to the real-time applications. We thus revise the basic concepts of the timeliness and the accuracy and propose their formal definitions in the case of non real-time applications characterized by a possibility of non-causal signal reconstruction at the destination. In particular, we link the concept of accuracy to the canonical first-order auto-regressive model of the signal, while the timeliness is defined by the time difference to reference samples of the signal. We compare the proposed semantic metrics in the case of different buffer management strategies and using numerical examples we show that the optimality of the transmission strategy changes when the real-time or non real-time applications are considered. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity. To overcome this issue, optimal and low complexity power allocation schemes are derived. In the simulation results, the performance of the proposed CoMP-assisted NOMA and CoMP-assisted C-NOMA schemes are compared with those of the CoMP-assisted orthogonal-multiple-access (OMA) scheme, the C-NOMA scheme and the NOMA scheme, where the superiority of the proposed schemes are demonstrated. Finally, the performance of the proposed schemes and the considered baselines is evaluated while varying various system parameters. [ABSTRACT FROM AUTHOR]

This Special Section (SI) features the latest research contributions regarding recent advances in the design and management of reliable communication networks. Communication networks are constantly increasing their complexity and scale to satisfy the requirements of network services. The current trend of convergence of networking and computing infrastructures (as in today’s cloud systems and softwarized networks) calls for novel advanced strategies and solutions to support reliable services, as the development of new data-driven solutions for reliable network automation and self-diagnostic tools to ensure resilient network management. [ABSTRACT FROM AUTHOR]

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, “fresh” information needs to be offloaded, processed, and delivered back to vehicles; in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles’ transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut; Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters. [ABSTRACT FROM AUTHOR]

The gaining momentum of Electric Vehicles’ (EV) market is hindered mainly due to the range anxiety. Accordingly, a ubiquitous charging station (CS) network is becoming indispensable. However, due to the lack of reservations or check-in policies in EV charging, users and operators are not provided proper information regarding waiting time at public CSs. This renders users reluctant to use public CSs. In addition, this incomplete information, creates difficulties in the deployment and operation of CSs. Evidently, there is a need to improve the Quality-of-Service (QoS) such as minimizing the waiting time or blocking probability. Therefore, CS owners rely during the designing stage on some theoretical distribution for the associated parameters assumption (i.e., battery capacity, charging demand, charging time, waiting time, etc.). To alleviate this situation, instead of depending on theoretical assumptions, real CSs usage data for EV charging are analyzed to acquire data driven distributions. Moreover, since the charging rate is dependent on the State of Charge (SoC), instead of a constant charging rate, a SoC dependent charging model based on real experimental data is proposed and evaluated with real data. Finally, exploiting the acquired distributions and charging model, variations of the $M/G/k$ queuing system to approximate the waiting time, reneging probability and blocking probability is implemented. A detailed simulation is placed and the findings provide a direction for CS owners in determining the capacity (i.e., number of outlets) or parking area size to enhance the QoS. [ABSTRACT FROM AUTHOR]

This article considers unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks, where low resource IoT devices periodically sample a stochastic process and need to upload more recent information to a Base Station (BS). Among the myriad of applications, there is a need for timely delivery of data (for example, status-updates) before the data becomes outdated and loses its value. Since transmission capabilities of IoT devices are limited, it may not always be feasible to transmit over one hop transmission to the BS. To address this challenge, UAVs with virtual queues are deployed as middle layer between IoT devices and the BS to relay recent information over unreliable channels. In the absence of channel conditions, the optimal online scheduling policy is investigated as well as dynamic UAV altitude control that maintains a fresh status of information at the BS. The objective of this paper is to minimize the Expected Weighted Sum Age of Information (EWSA) for IoT devices. First, the problem is formulated as an optimization problem that is however generally hard to solve. Second, an online model free Deep Reinforcement Learning (DRL) is proposed, where the deployed UAV obtains instantaneous channel state information (CSI) in real time along with any adjustment to its deployment altitude. Third, we formulate the online problem as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) algorithm, which is a highly stable state-of-the-art DRL algorithm, is leveraged to solve the formulated problem. Finally, extensive simulations are conducted to verify findings and comprehensive comparisons with other baseline approaches are provided to demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

We study the impact of the erroneous wireless control feedback channel on the Age of Information (AoI) performance. We consider a point-to-point communication setup employing packet combining strategies to transmit status update packets over an erroneous wireless data channel. The sender receives the positive acknowledgment (ACK) or negative acknowledgment (NACK) of packet reception over an error-prone wireless feedback channel. To mitigate the impact of the imperfect feedback channel on the system performance, we adopt an asymmetric signal detection model to control the detection accuracy of ACK and NACK signals. We then compute the explicit expressions for the average AoIs under preemptive and non-preemptive service management policies. We show the optimum parameter design for the control channel model in order to minimize the average AoI. The numerical results validate the analysis and provide detailed perspectives on the optimal signal detection setup minimizing the average AoI, and the possible trade-off between AoI and resource utilization. Generally, the analysis for a preemption setting illustrates that a better protection for the NACK messages compared to the ACK messages can preserve the minimum AoI performance. Especially, under a high noisy feedback channel setup, we show that the viable solution minimizing the average AoI is a blind transmission mechanism at the cost of increasing unnecessary utilization of the channel resources. Moreover, the analysis for a non-preemptive policy reveals the dependence of the optimal feedback signal detection design on the status packet generation rate at the sensor. Such a dependency makes the feedback signal detection approach to provide a more reliable ACK detection compared to NACK messages under the condition of more frequent packet arrival, whereas the opposite holds under the condition of less frequent packet arrival. [ABSTRACT FROM AUTHOR]

This paper investigates the downlink transmission of reconfigurable intelligent surface (RIS)-aided cooperative non-orthogonal-multiple-access (C-NOMA), where both half-duplex (HD) and full-duplex (FD) relaying modes are considered. The system model consists of one base station (BS), two users and one RIS. The goal is to minimize the total transmit power at both the BS and at the user-cooperating relay for each relaying mode by jointly optimizing the power allocation coefficients at the BS, the transmit power coefficient at the relay user, and the passive beamforming at the RIS, subject to power budget constraints, the successive interference cancellation constraint and the minimum required quality-of-service at both cellular users. To address the high-coupled optimization variables, an efficient algorithm is proposed by invoking an alternating optimization approach that decomposes the original problem into a power allocation sub-problem and a passive beamforming sub-problem, which are solved alternately. For the power allocation sub-problem, the optimal closed-form expressions for the power allocation coefficients are derived. Meanwhile, with the aid of difference-of-convex rank-one representation and successive convex approximation, an efficient solution for the passive beamforming is obtained. The simulation results validate the accuracy of the derived power control closed-form expressions and demonstrate the gain in the total transmit power brought by integrating the RIS in C-NOMA networks. [ABSTRACT FROM AUTHOR]

We consider a wireless network consisting of two adjacent cells, where the joint transmission (JT) coordinated multipoint (CoMP) is established to assist the user equipments (UEs) located at the edge of each cell. In addition, full-duplex (FD) cooperative non-orthogonal-multiple-access (C-NOMA) is invoked within each cell to improve the data rates of the UEs and to assist those at the cell edge. The UEs are categorized into two groups, namely, cell-center UEs ($CUs$) and cell-edge UEs ($EUs$). The $CUs$ are the UEs located around the center of each cell. Meanwhile, the $EUs$ are the UEs located at the edge of each cell, where the JT-CoMP is applied since they have less distinctive received power from two cells. In this paper, a framework to jointly optimize the power control and the UEs clustering of CoMP-assisted FD C-NOMA system is formulated as an optimization problem to maximize the network sum-rate while guaranteeing the required quality-of-service of UEs. The formulated problem is a non-convex mixed-integer non-linear program that cannot be solved in a straightforward manner. To tackle this issue, the formulated problem is decomposed into an inner power allocation problem and an outer UEs clustering problem. For the inner problem, a computational-efficient solution is obtained. Meanwhile, the outer problem is reformulated as a one-to-one three-sided matching game. Then, a low-complexity near-optimal clustering algorithm is proposed. The simulation results demonstrate that 1) the optimality of the power control solution; 2) the CoMP-assisted FD C-NOMA has a superior performance compared to CoMP-assisted half-duplex (HD) C-NOMA and CoMP NOMA schemes for moderate values of self-interference. It has been also shown that the proposed solution achieves around 96.5% of the average achievable network sum-rate of the optimal solution. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs. [ABSTRACT FROM AUTHOR]

Given its ability to control and manipulate wireless environments, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has emerged as a key enabler technology for the six-generation (6G) cellular networks. In the meantime, vehicular environment radio propagation is negatively influenced by a large set of objects that cause transmission distortion such as high buildings. Therefore, this work is devoted to explore the area of RIS technology integration with vehicular communications while considering the dynamic nature of such communication environment. Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS to provide indirect wireless transmissions to disconnected areas, known as dark zones. Dark zones are spots within RSU coverage where the communication links are blocked due to the existence of blockages. In details, a discrete RIS is utilized to provide communication links between the RSU and the vehicles passing through out-of-service zones. Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate. The formulated problem is mixed integer non-convex program which is difficult to be solved and does not account for the uncertain dynamic environment in vehicular networks. Thereby, we resort to alternative methods based on Deep Reinforcement Learning to determine RSU wireless scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift matrix, i.e., passive beamforming, of the RIS. The Markov Decision Process (MDP) is defined and the complexity of the solution approach is discussed. Our numerical results demonstrate the superiority of our proposed approach over baseline techniques. [ABSTRACT FROM AUTHOR]

Ransomware attacks are among the most disruptive cyber threats, causing significant financial losses while impacting productivity, accessibility, and reputation. Despite their end goals (encryption/locking), ransomware are often designed to evade detection by executing a series of pre-attack API calls, namely “paranoia” activities, for determining a suitable execution environment. In this work, we present a first-of-a-kind effort to utilize such paranoia activities for characterizing ransomware distinguishable behaviors. To this end, we draw-upon more than 3K samples from recent/prominent ransomware families to fingerprint their uniquely leveraged paranoia activities. Specifically, by leveraging techniques rooted in Natural Language Processing (NLP) such as Occurrence of Words (OoW), we model ransomware-generated evasion API calls while tailoring various machine and deep learning algorithms to perform ransomware classification. The thoroughly conducted evaluations demonstrate the effectiveness of the implemented approach, with the Random Forest (RF) and OoW techniques producing an optimal classification accuracy (94.92%). The insights/findings from this work not only shed light on contemporary ransomware-specific evasion methods, but also (i) indicates that such tactics could be employed effectively as features for ransomware family attribution while (ii) laying the foundation for implementing proactive and portable countermeasures for further ransomware attack detection/mitigation by solely utilizing ransomware-generated paranoia activities. [ABSTRACT FROM AUTHOR]

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate. [ABSTRACT FROM AUTHOR]

Despite the advantages of multi-access edge computing in enabling latency-sensitive services and extending the limited computing capabilities of network devices, access communication issues are still often causing the quality of the wireless channels to be severely degraded, preventing the edge resources from being efficiently utilized. Through the deployment of low-cost passive reflecting elements, the recent studies of intelligent reflecting surfaces (IRSs) in wireless networks have shown a great potential for enhancing the quality of the wireless channels and the transmission rates. In this work, motivated by the recent findings, we study the use of an IRS-aided edge computing system for enabling low latency and high reliability computation offloading in the context of a single-user network. Specifically, we optimize the phase shift of the IRS elements along with the device’s transmit power and offloading decision, with the objective of minimizing the device’s energy consumption. Due to the non-convexity of the problem, we propose a customized sub-optimal solution based on the alternating optimization approach, utilizing novel successive convex approximation techniques. Numerical analysis demonstrates the energy reduction and saving in network resources provided by the optimized use of the IRS, especially for offloading services with higher reliability. [ABSTRACT FROM AUTHOR]

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond (also called 6G) to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets (typically sporadic and of short size) over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station (BS) scans the eMBB traffic’ symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability, 4) and the proposed strategy has linear time complexity making it an efficient solution to be used in practice. [ABSTRACT FROM AUTHOR]

Subsynchronous Interaction (SSI) phenomenon is known to be one of the most frequent and severe stability issues of a Wind Park (WP), and can potentially lead to a significant loss of power generation. The broad impacts of this phenomenon on a power grid have made WPs interesting targets for cyber attacks. To initiate the SSI, an adversary can target either the power grid (external attacks) or the cyber system of WPs (internal attacks). This paper proposes a mitigation scheme for attacks that initiate the SSI phenomenon in series compensated doubly-fed induction generator (DFIG)-based WPs. External attacks are addressed by employing a robust static-output-feedback Subsynchronous Damping Controller (SSDC), which is designed based on the insensitive strip region and Linear Matrix Inequality (LMI) techniques. Internal attacks, however, are detected by comparing the estimated and measured converters’ currents. Once the compromised measurements are detected, the designed SSDC is restructured to mitigate the attacks. The effectiveness of the proposed method is demonstrated using detailed Electromagnetic Transient (EMT) simulations for both internal and external cyber attacks. Additionally, the performance of the proposed method is corroborated using a real-time co-simulation framework. [ABSTRACT FROM AUTHOR]

Modern 5G services with stringent reliability and latency requirements such as smart healthcare and industrial automation have become possible through the advancement of Multi-access Edge Computing (MEC). However, the rigidity of ground MEC and its susceptibility to infrastructure failure would prevent satisfying the resiliency and strict requirements of those services. Unmanned Aerial Vehicles (UAVs) have been proposed for providing flexible edge computing capability through UAV-mounted cloudlets, harnessing their advantages such as mobility, low-cost, and line-of-sight communication. However, UAV-mounted cloudlets may have failure rates that would impact mission-critical applications, necessitating a novel study for the provisioned reliability considering UAV node reliability and task redundancy. In this paper, we investigate the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements. We aim at maximizing the rate of served requests, by optimizing the UAVs’ positions, the offloading decisions, and the allocated resources while respecting the stringent latency and reliability requirements. To do so, the problem is divided into two phases, the first being a planning problem to optimize the placement of UAVs and the second an operational problem to make optimized offloading and resource allocation decisions with constrained UAVs’ energy. We formulate both problems associated with each phase as non-convex mixed-integer programs, and due to their non-convexity, we propose a two-stage approximate algorithm where the two problems are transformed into approximate convex programs. Further, we approach the problem considering the task partitioning model which will be prevalent in 5G networks. Through numerical analysis, we demonstrate the efficiency of our solution considering various scenarios, and compare it to other baseline approaches. [ABSTRACT FROM AUTHOR]

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. Motivated by the above, in this paper, we introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered by other infrastructure. However, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment (e.g., the vehicles’ instantaneous location). Therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning (DRL) to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the vehicular coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings and demonstrate the effectiveness of the proposed design and show that during the mission time, the deployed UAVs adapt their velocities in order to cover the vehicles. [ABSTRACT FROM AUTHOR]

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme. [ABSTRACT FROM AUTHOR]

The new surge of interest towards mass integration of Electric Vehicles (EVs) in distribution smart grids can expose the high-voltage grid to instability conditions, for instance, through cyber threats initiated from the residential or public EV Supply Equipment (EVSE). This paper (i) investigates the impact of switching attacks on EV charging infrastructure and their impacts on the inter-area stability of the transmission grid, and (ii) proposes a two-stage detection and mitigation technique for those attacks. Initially, we demonstrate that leveraging the existing vulnerabilities in charging stations’ cyberspace and the topology of the grid, an adversary can switch the injected or absorbed power of EVs with inter-area frequency and cause a blackout by destabilizing the angular speed of the grid’s generators. Then, a Back Propagation Neural Network (BPNN) scheme is designed and hosted at the central management system (CMS) of a public EVSE network. Using this BPNN scheme, the switching attacks are accurately detected by analyzing the features of charging/discharging requests. Moreover, the detected attacks are mitigated by delaying or discarding the request execution. Finally, to cope with the conditions where the residential chargers are under-attack, or when the BPNN fails to provide accurate detection, a wide area $\text{H}^{\infty }$ controller is designed to keep the angular speed of the synchronous generators within the acceptable limits. The effectiveness of the proposed techniques is evaluated using two-area Kundur and 5-area Australian grids. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes. [ABSTRACT FROM AUTHOR]

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error. [ABSTRACT FROM AUTHOR]

We investigate the benefits of integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surface (RIS) elements to passively relay information sampled by Internet of Things devices (IoTDs) to the base station (BS). In order to maintain the freshness of relayed information, an optimization problem with the objective of minimizing the expected sum Age-of-Information (AoI) is formulated to optimize the altitude of the UAV, the communication schedule, and phases-shift of RIS elements. In the absence of prior knowledge of the activation pattern of the IoTDs, proximal policy optimization algorithm is developed to solve this mixed-integer non-convex optimization problem. Numerical results show that our proposed algorithm outperforms all others in terms of AoI. [ABSTRACT FROM AUTHOR]