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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]

The unavailability of end-to-end traffic in mesh networks with failure-independent path-protecting (FIPP) p-cycles is examined, and an availability-aware network design method is presented for the same. The efficiency of FIPP p-cycles is compared with that of span-protecting p-cycles when the network design limits unavailability, and results reveal that the FIPP p-cycles are less efficient with 8-13% higher redundancy than span-protecting p-cycles when the service unavailability is limited.

We address two issues of contemporary interest in the area of enhancing the performance of ad hoc networks, where the IEEE 802.11 distributed coordination function (DCF) is the adopted medium-access control (MAC). We investigate the effects that transmission power control and tuning carrier sensing (CS) threshold have on network throughput, and we study the interplay between them. To accomplish this, we develop a realistic analytical model that characterizes the transmission activities that are governed by the IEEE 802.11 DCF in a single-channel power-aware multihop wireless network. We observe that selecting a smaller CS threshold will severely impact the spatial reuse, whereas a larger CS threshold will yield excessive interference among concurrent transmissions. Accordingly, we discovered that performing power control has either a minor or negligible effect in both situations, respectively. Furthermore, when the CS threshold is selected appropriately, power control shows its effectiveness in reducing collisions and, hence, improving system performance. Finally, using our model, we demonstrate the potential adverse impacts of request-to-send/clear-to-send (RTS/CTS) control packets on the network capacity and compare its performance with the two-way basic access method. Index Terms--IEEE 802.11, modeling, physical carrier sensing (PCS), power control, throughput.

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]

Wireless access protocols currently deployed in mobile ad hoc networks use distributed contention resolution mechanisms for sharing the wireless channel. In such an environment, selfish hosts that fail to adhere to the medium access control (MAC) protocol may obtain an unfair share of the channel bandwidth at the expense of performance degradation of well-behaved hosts. We present a novel access method, called predictable random backoff (PRB), that is capable of mitigating the misbehavior of selfish hosts, particularly hosts that deliberately do not respect the random deferment of the transmission of their packets. PRB is based on minor modifications of the IEEE 802.11 binary exponential backoff (BEB) and forces each node to generate a predictable backoff interval. The key idea is to adjust, in a predictable manner, the lower bound of the contention window to enhance the per-station fairness in selfish environments. Hosts that do not follow the operation of PRB are therefore easily detected and isolated. We present an accurate analytical model to compute the system throughput using a 3-D Markov chain. We evaluate the performance of PRB under the normal case and in the presence of selfish hosts. Our results show that PRB and BEB similarly perform in the former case. Selfish hosts, however, achieve substantially higher throughput than well-behaved hosts under BEB. PRB, on the other hand, can effectively enhance IEEE 802.11 BEB by mitigating the impacts of these MAC selfish misbehaviors and guarantee a fair share of the wireless channel for well-behaved hosts. Index Terms--Ad hoc networks, IEEE 802.11, medium access control (MAC), performance evaluation, selfish misbehavior.

Medium access control (MAC) protocols simulta. neously integrating transmission power (TP) control with directional antennas have the potential to enhance both energy savings and capacity throughput in wireless multihop Mobile Ad hoc NETworks (MANETs). In this paper, we present a model to calculate future interference in networks with directional antennas, and based on this model, we derive some relations that should exist between the required TP of RTS, CTS, DATA, and ACK frames for successful data packet delivery in MANETs based on the directional version of the IEEE 802.11 distributed coordination function. From these relations, we propose a distributed power control scheme. Furthermore, we show, via simulations, that the true potentials from the proposed control scheme cannot be shown due to the imperfection of the derived model. Based on these observations, we introduce another class of power control algorithm that instead deploys a prediction filter (Kalman or extended Kalman) to estimate future interference. Simulation experiments for different topologies are used to verify the significant throughput and energy gains that can be obtained by the proposed power control schemes. Index Terms--Directional antenna, interference, Kalman filter, Mobile Ad hoc NETwork (MANET), power control.

The emerging IEEE 802.11e standard is expected to provide service differentiation and resource allocation for realtime traffic. To support the transmission of voice and multimedia data with performance guarantees, it is crucial to design efficient algorithms for admission control and resource allocation (particularly under heavy load), and several methods have been proposed to date. However, most of these proposed methods may not be efficient, because they assign channel access parameters (CAPs) according to the access category into which a flow is mapped rather than the absolute quality-of-service (QoS) requirements of the flow. Using simulations, we highlight the shortcomings of current admission control methods, and accordingly, we propose a flow-based service differentiation mechanism. We introduce the proper 'per-flow' selection of CAPs based on the QoS requirements of traffic flows. Furthermore, the new flow-based admission can control and adjust CAPs with the channel condition variations. We validate our reasoning through extensive simulations and a thorough comparison with other schemes. Index Terms--Admission control, IEEE 802.11e, wireless networks.

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]