Search

Faced with the threat of climate change, the world is rapidly adopting Electric Vehicles (EVs). The EV ecosystem, however, is vulnerable to cyber-attacks putting it and the power grid at risk. In this article, we present a security-oriented real-time Co-simulation Testbed for the EV ecosystem and the power grid., Comment: "\c{opyright} 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works."

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.

This special section features the latest research contributions regarding the design and management of reliable networks. Reliability of communication infrastructure is a top priority for network operators. To ensure reliable network operation, new design and management techniques for reliable communications must be constantly devised to respond to the rapid network and service evolution. As a recent and relevant example, deployments of 5G communication networks will soon enter their second phase, during which the network infrastructure will require upgrades to support new Ultra-Reliable Low-Latency Communication (URLLC) services with availabilities of up to 6 nines to be guaranteed jointly with extremely low latencies. Even in the still preliminary vision of 6G communication networks, reliability is posed as one of the most critical requirements, as 6G networks will represent the communication platform of our future hyper-connected society, supporting essential services as smart mobility, e-health, and immersive environments with application in remote education and working, just to name a few. Similarly, disaster resiliency in communication networks is now attracting the attention of media, government and industry as never before (consider, e.g., the worldwide network traffic deluge to support remote working during the current Coronavirus pandemic). Luckily, several new technical directions can be leveraged to provide new solutions for network reliability as: increased network reconfigurability enabled by Software Defined Networking (SDN); integration/convergence of multiple technologies (optical, wireless satellite, datacenter networks); enhanced forms of data/service replication, supported by, e.g., edge computing; network slicing, used to carve highly-reliable logical partitions of network, computing and storage resources. These, and many others, technological transformations can be leveraged to enable next-generation high-reliability networks.

This paper presents a downlink reconfigurable intelligent surface (RIS)-assisted half-duplex (HD) cooperative rate splitting multiple access (C-RSMA) networks. The proposed system model is built up considering one base station (BS), one RIS, and two users. With the goal of minimizing the network energy consumption, a joint framework to optimize the precoding vectors at the BS, common stream split, relaying device transmit power, the time slot allocation, and the passive beamforming at the RIS subject to the power budget constraints at both the BS and the relaying node, the quality of service (QoS) constraints at both users, and common stream rate constraint is proposed. The formulated problem is a non-convex optimization problem due to the high coupling among the optimization variables. To tackle this challenge, an efficient algorithm is presented by invoking the alternating optimization technique, which decomposes the original problem into two sub-problems; namely, sub-problem-1 and sub-problem-2, which are alternatively solved. Specifically, sub-problem-1 is to jointly optimize the precoding vectors, common stream split, and relaying device power. Meanwhile, sub-problem-2 is to optimize the phase shift matrix at the RIS. In order to solve sub-problem-1, an efficient low-complexity solution based on the successive convex approximation (SCA) is proposed. Meanwhile, and with the aid of difference-of-convex rank-one representation and the SCA approach, an efficient solution for the phase shift matrix at the RIS is obtained. The simulation results demonstrate that the proposed RIS-assisted HD C-RSMA achieves a significant gain in minimizing the total energy consumption compared to the RIS-assisted RSMA scheme, RIS-assisted HD cooperative non-orthogonal multiple access (C-NOMA), RIS-assisted NOMA, HD C-RSMA without RIS, and HD C-NOMA without RIS.

This paper investigates the benefits of integrating multiple reconfigurable intelligent surfaces (RISs) in enhancing the timeliness performance of uplink Internet-of-Things (IoT) network, where IoT devices (IoTDs) upload their time-stamped status update information to a base station (BS) using non-orthogonal multiple access (NOMA). Accounting to the potential unreliable wireless channels due to the impurities of the propagation environments, such as deep fading, blockages, etc., multiple RISs are deployed in the considered IoT network to mitigate the propagation-induced impairments, to enhance the quality of the wireless links, and to ensure that the required freshness of information is achieved. In this setup, an optimization problem has been formulated to minimize the average sum Age of Information (AoI) by optimizing the transmit power of the IoTDs, the IoTDs clustering policy, and the RISs configurations. The formulated problem ends up to be a mixed-integer non-convex problem. In order to tackle this challenge, the RISs configurations are first obtained by adopting a semi-definite relaxation (SDR) approach. Then, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization, where the original problem is decomposed into an outer IoTDs clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is invoked to solve the outer problem. Numerical results demonstrate that our proposed approach achieves lowest AoI compared to the other baseline approaches., IEEE Global Communications Conference (Globecom), 2022, 6 pages, 4 Figures. arXiv admin note: substantial text overlap with arXiv:2202.13572

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.

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. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile users. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For mobile users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and the system performance.

In this paper, we investigate the integration between the coordinated multipoint (CoMP) transmission and the non-orthogonal multiple access (NOMA) in downlink heterogeneous cloud radio access networks (H-CRANs). In H-CRAN, low-power high-density small remote radio heads (SRRHs) are underlaid by high-power low-density macro RRH (MRRH). However, co-channel deployment of the different RRHs gives rise to the problem of inter-cell interference that significantly affects system performance especially the cell-edge users. Thus, the users are first categorized into Non-CoMP users and CoMP users based on the relation between the useful signal to the dominant interference signal. The Non-CoMP user is the user equipment (UEs) having high signal-to-interference-plus-noise-ratio ( $\mathtt {SINR}$ ) and hence associates with only one RRH. On the other hand, the CoMP user, cell-edge user, is the UE that experiences less distinctive received power with the best two RRHs. In the proposed CoMP-NOMA framework, each RRH schedules CoMP-UE and non-CoMP-UE over the same transmission channel using NOMA. We first design an analytical framework based on tools from the stochastic geometry to evaluate the performance of the proposed framework (CoMP-NOMA) which is based on H-CRAN in terms of the average achievable data rate for each NOMA UE. We then examine the spectral efficiency of the proposed CoMP-NOMA based H-CRAN. Simulation results are provided to validate the accuracy of the analytical models and to reveal the superiority of the proposed CoMP-NOMA framework compared with conventional CoMP orthogonal multiple access (CoMP-OMA) techniques. By reaping the benefits of both JT-CoMP and NOMA, we prove that the proposed framework can successfully deal with the inter-cell interference by using CoMP and improve the network’s spectral efficiency through NOMA technique. We also show that, with an appropriate power allocation coefficient setting at the Non-CoMP-UEs, a fairness performance can be achieved between the CoMP-UEs and the Non-CoMP-UEs.

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs’ trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

The concept of smart city strives for greener technology to reduce carbon emission to ameliorate the global warming. Following this footprint, the transportation sector is experiencing a paradigm shift and the transition to electric vehicles (EVs) has prodigious plausibility in reducing carbon emission. However, the anticipated EV penetration is hindered by several challenges, among them are their shorter driving range, slower charging rate and the lack of ubiquitous availability of charging locations, which collectively contribute to range anxieties for EVs' drivers. Meanwhile, the expected immense EV load onto the power distribution network may degrade the voltage stability. To reduce the range anxiety, we present a two-stage solution to provision and dimension a DC fast charging station (CS) network for the anticipated energy demand and that minimizes the deployment cost while ensuring a certain quality of experience for charging e.g., acceptable waiting time and shorter travel distance to charge. This solution also maintains the voltage stability by considering the distribution grid capacity, determining transformers’ rating to support peak demand of EV charging and adding a minimum number of voltage regulators based on the impact over the power distribution network. We propose, evaluate and compare two CS network expansion models to determine a cost-effective and adaptive CSs provisioning solution that can efficiently expand the CS network to accommodate future EV charging and conventional load demands. We also propose two heuristic methods and compare our solution with them. Finally, a custom built Python-based discrete event simulator is developed to test our outcomes.

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design.

The drive for efficient, reliable, green, and connected smart cities has promoted the use of electric vehicles (EVs) as the main future means of transportation. This resulted in a breakthrough in the anticipated number of adopted EVs by the year 2020, and consequently an urge for an available and trustworthy EV charging infrastructure. The diversity of the involved players, the used technologies, the bulk data exchange, and the widespread nature of the charging network give rise to security concerns in the form of message tampering, spoofing, or delaying among others to disconcert the charging service along with the underlying power layer. Furthermore, confidentiality and privacy of user information (i.e. identity, location, payment information, etc.) is another major concern associated with the deployment and use of the charging infrastructure. Thus, there is a need to identify and classify such concerns, and devise suitable solutions for a secure charging infrastructure. In this paper, we present a security assessment of the EV charging infrastructure. We highlight and categorize cyber threats targeting different players in a charging system, along with the security solutions presented in the literature. Finally, we present a gap analysis and insights into future research directions for EV charging system security.

In this paper, we investigate an outdoor and indoor wireless communication network with the assistance of a novel relay-aided double-sided reconfigurable intelligent surface (RIS). A scheduling problem is considered at the outdoor access point (AP) to minimize the sum of age of information (AoI). To serve the indoor users and further enhance the wireless link quality, a novel double-sided RIS with relay is utilized. Since the formulated problem is non-convex with highly-coupled variables, a successive convex approximation (SCA) based alternating optimization (AO) algorithm is proposed to solve it in an iterative manner. Finally, simulation results show the effectiveness and significant performance improvement in terms of AoI of the proposed algorithm compared with other benchmarks.

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.

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

Next-generation cellular systems are anticipated to support 100 times higher data rates (ultra-high rate) compared with the fourth generation (4G) of cellular systems. It is, therefore, necessary to develop novel spectrally efficient uplink/downlink techniques. Multiple techniques have been proposed, including the so-called non-orthogonal multiple access (NOMA) technique. However, the spectral efficiency gains achieved by NOMA over OMA techniques have been shown to be modest. Recently, we proposed a spectrally efficient technique for the downlink channel, which involves exploiting similarities among users’ short bit blocks, where we showed that spectral efficiency gains of up to three times that of OMA schemes can be achieved. However, the technique cannot be extended to the uplink scenario because users are not aware of each other’s bit block. To this end, we propose in this paper a spectrally efficient scheme for the uplink channel, where we exploit the similarity between the short bit blocks of the uplink and downlink sequences corresponding to one user. The downlink bit sequences are those received by a user from the base station (BS). It is assumed that the BS keeps track of the bit sequences transmitted on the downlink channel to different users. The uplink and downlink bit sequences, which are assumed to be uncorrelated, are divided into bit blocks of short lengths, and then, the similarity between those blocks is extracted. Once each user determines its similarity index (i.e., the number of similar bit blocks) between its own bit sequence and its respective downlink bit sequence, this information is communicated with the BS, which will, in turn, select the user with the largest similarity index to transmit during that resource block. The same process repeats every resource block where the user with the maximum similarity index is always selected. We propose a simple overhead exchange algorithm that facilitates the exchange of the information on the similarity indexes between the users and the BS, where we assume that this exchange of information is done through a control channel. The performance of the proposed scheme and the overhead exchange algorithm is investigated analytically and by Monte Carlo simulations. Among the parameters that we incorporate into the analysis are the user density, the length of bit blocks used to check the similarity index, and the channel correlation. We show that spectral efficiency gains of approximately two times that of OMA schemes can be achieved.

Vehicular networks’ connectivity and data delivery delay performance is highly affected by the vehicular traffic's spatio-temporal dynamics whose variations are subject to a multitude of random factors. Under the stringent and inevitable limitations imposed by free-flow vehicular traffic conditions (i.e., low-to-medium vehicular densities, elevated degree of mobility, high speeds, etc), these networks suffer from considerably rapid topology variations leading to severe connectivity intermittence and, hence, delayed data delivery. This motivates the study presented in this paper, which aims at investigating the capability of external elements that are independent of the vehicular traffic flow and its inherent limitations (e.g., airborne unmanned aerial vehicles (UAVs), a.k.a., drones) to serve as possible adjuvant relays; thus, contributing to strengthening/healing weak/broken communication links among ground-bound vehicular entities (i.e., RoadSide Units (RSUs) and vehicles) and uplifting the vehicular connectivity and delay performance. Particularly, in the context of a vehicular sub-networking scenario, a UAV mobility model is proposed as a first step in analytically capturing macroscopic dynamics for UAVs exhibiting waypoint mobility patterns and plying over a considered roadway segment. Then, a stochastic analytical model is formulated for the purpose of mathematically characterizing the path availability and achieved data delivery delays in the presence of these UAVs. A simulation framework is established to verify the validity and accuracy of the proposed models and gauge the merit of UAV assistance in improving the vehicular connectivity and data delivery delay performance.

In order to support the development of 5G technologies, researchers are actively engaged in addressing the challenges accompanying the emerging 5G applications. Unquestionably, an eminent technology gaining significant research attention is edge computing. Vehicular edge computing brings data storage and computing capabilities as well as hosting support applications that comprise emerging vehicular services and applications which demand low-delay processing, to the edge closer to the vehicles, reducing response time and increasing reliability, therefore achieving the holistic vision of the tactile Internet. In this context, this paper considers a vehicular network with edge computing capabilities deployed at road side units, and addresses the problem of workload offloading as well as scheduling of computation tasks on the computing resources available at the edge. The challenge here is the high mobility of the vehicles and hence their short residence time within the coverage range of the road side units hosting the edge computing resources. A joint problem considering the communication and computation resources, as well as the latency requirements of the workload is formulated and the scheduling is shown to be NP-Hard. Subsequently, efficient solutions based on Lagrangian relaxation are derived and presented. We evaluate numerically the proposed methods and show their closeness to the optimal solutions.

Multi-access edge computing (MEC) has formed a major improvement in the existing mobile cloud computing paradigm, due to its ability in addressing the rising number of latency-sensitive services. However, bearing in mind the limited capacity that edge servers possess which offsets their benefits in the periods of high load, a hierarchical arrangement of the edge cloudlets has been studied and has shown to be successful in expanding their capabilities. Yet, considering the emerging business models in 5G networks, the cost disparity between the edge tiers has been until now ignored, leading to cost-inefficient solutions with respect to the network operators (NOs). In this paper, we consider an NO that is leasing resources of a high-tier central cloudlet for task offload, where we jointly minimize the NO’s computational cost and devices’ energy consumption in a multi-tier MEC system, by optimizing the offloading decision, the allocated transmission power and radio resources on the uplink channel, and the assigned servers’ computation, while respecting the devices’ latency requirement. We mathematically formulate our mixed-integer non-convex program and propose a Branch-and-Bound (BnB) algorithm for obtaining the optimal solution. Due to the BnB complexity, we propose a low-complexity algorithm based on the successive convex approximation method to solve and obtain a high-quality solution and also present an inflation-based algorithm for obtaining a polynomial-time and efficient solution. The numerical results show the performance and scalability of the algorithms, demonstrate their efficiency, and uncover insights for helping NOs to better manage their resources following various configurations.

Hardware middleboxes are critical elements in today’s networks. Despite their important roles, they are accompanied by several problems, namely, their lack of flexibility, high capital and operational expenditures, and power consumption. Owing to the recent advances in virtualization, network function virtualization promises to address these problems, through replacing hardware middleboxes by software-based entities which can run on commodity hardware. These virtual network functions (VNFs) promise to alleviate the numerous disadvantages brought by their hardware counterparts. One of these most serious issues is the steadily increasing power consumption. In order to further optimize the power consumption, an efficient framework capable of mapping and scheduling traffic on these VNFs is needed. Such a framework allows to optimally assign and schedule the flows to be serviced, and place the unused servers in energy saving modes. In this paper, we assume VNFs are already placed on physical machines and consider traffic flows with deadlines. We focus on the problem of assigning and scheduling flows to VNFs in the most energy efficient manner. We formulate this problem mathematically and, owing to its complexity, present an efficient algorithmic method for solving it. We compare our heuristic with two other approaches, one of which aims to minimize the makespan and the other to minimize number of servers used. We show that our heuristic combines the advantages of both approaches and generates better results by consuming up to 31.3% and 46.1% less energy than other two approaches.

Intelligent Transportation Systems are thriving thanks to a wide range of technological advances, namely 5G communications, Internet of Things, artificial intelligence and edge computing. Central to this is the wide deployment of smart sensing devices and accordingly the large amount of harvested information to be processed for timely decision making. Robust network access is, hence, essential for offloading the collected data before a set deadline, beyond which the data loses its value. In environments where direct communication can be impaired by, for instance, blockages such as in urban cities, unmanned aerial vehicles (UAVs) can be considered as an alternative for providing and enhancing connectivity, particularly when IoT devices (IoTD) are constrained with their resources. Also, to conserve energy, IoTDs are assumed to alternate between their active and passive modes. This paper, therefore, considers a time-constrained data gathering problem from a network of sensing devices and with assistance from a UAV. A Reconfigurable Intelligent Surface (RIS) is deployed to further improve both the connectivity and energy efficiency of the UAV, particularly when multiple devices are served concurrently and experience different channel impairments. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoTDs transmissions as well as the trajectory of the UAV. First, the problem is formulated with the objective of maximizing the total number of served devices each during its activation period. Owing to its complexity and the incomplete knowledge about the environment, we leverage deep reinforcement learning in our solution; the UAV trajectory planning is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Next, the RIS configuration is then handled via Block Coordinate Descent.

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks., Comment: International Journal of Electrical Power & Energy Systems, Available online 21 November 2021, 107784, Full citation details will be added when the final version becomes available

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, \textit{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.

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.

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.

Due to the dramatic increase in high data rate services and in order to meet the demands of the fifth-generation (5G) networks, researchers from both academia and industry are exploring advanced transmission techniques, new network architectures and new frequency spectrum such as the visible light and the millimeter wave (mmWave) spectra. Visible light communication (VLC) particularly is an emerging technology that has been introduced as a promising solution for 5G and beyond, owing to the large unexploited spectrum, which translates to significantly high data rates. Although VLC systems are more immune against interference and less susceptible to security vulnerabilities since light does not penetrate through walls, security issues arise naturally in VLC channels due to their open and broadcasting nature, compared to fiber-optic systems. In addition, since VLC is considered to be an enabling technology for 5G, and security is one of the 5G fundamental requirements, security issues should be carefully addressed and resolved in the VLC context. On the other hand, due to the success of physical layer security (PLS) in improving the security of radio-frequency (RF) wireless networks, extending such PLS techniques to VLC systems has been of great interest. Only two survey papers on security in VLC have been published in the literature. However, a comparative and unified survey on PLS for VLC from information theoretic and signal processing point of views is still missing. This paper covers almost all aspects of PLS for VLC, including different channel models, input distributions, network configurations, precoding/signaling strategies, and secrecy capacity and information rates. Furthermore, we propose a number of timely and open research directions for PLS-VLC systems, including the application of measurement-based indoor and outdoor channel models, incorporating user mobility and device orientation into the channel model, and combining VLC and RF systems to realize the potential of such technologies.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in a cellular downlink system. The system model consists of a base station (BS) serving multiple users, where users with good channel quality can assist the transmissions between the BS and users with poor channel quality through either half-duplex (HD) or full-duplex (FD) device-to-device (D2D) communications. We formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme, where the objective is maximizing the achievable sum rate of the whole system while guaranteeing a certain quality of service (QoS) for all users. The formulated problem is a mixed-integer non-linear program (MINLP) which is generally NPhard. To overcome this issue, we reconstruct the original problem into a bi-level optimization problem that can be decomposed into two sub-problems to be solved independently. The outer problem is a linear assignment problem which can be efficiently handled by the well-known Hungarian method. The inner problem is still a non-convex optimization problem for which finding the optimal solution is challenging. However, we derive the optimal power control policies for both the HD and the FD schemes in closedform expressions, which makes the computational complexity of the inner problems polynomial for every possible pairing configurations. These findings solve ultimately the original MILNP in a timely manner that makes it suitable for real-time and low latency applications. Our simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in a negligible computational time.

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations.

The demand for Electric Vehicles (EVs) has been exponentially increasing, and to achieve sustainable growth, the industry dictated rapid development of the supporting infrastructure. This requires building a reliable EV charging ecosystem that serves customer demands while ensuring the security of the Internet-enabled systems and the connected critical infrastructure against possible cyber attacks. To this end, we devise a system lookup and collection approach to obtain a representative sample of widely deployed EV Charging Station Management Systems (EVCSMS). Furthermore, we leverage reverse engineering and penetration testing techniques to perform a first-of-a-kind comprehensive security and vulnerability analysis of the identified EVCSMS and their software/firmware implementations. Indeed, our systematic analysis unveils an array of vulnerabilities, which demonstrate the insecurity of the EVCSMS against remote cyber attacks. Considering the feasibility of such attacks, we discuss attack implications against the EV charging stations (EVCS) and their users. More importantly, we simulate the impact of practical cyber attack scenarios against the power grid, which result in possible service disruption and failure in the grid. Finally, while we recommend mitigation measures, our discoveries raise concerns about the lack of adequate security considerations in the design of the deployed EVCS, which will motivate vendors to take immediate action to patch their developed systems. Indeed, our communication with the concerned parties resulted in positive responses from some vendors such as Schneider Electric, who acknowledged our findings by reserving 12 CVEs, respectively.

Relay-assisted FSO systems were proposed as a means for remedying the effects of the various atmospheric impairments on the quality of the FSO signal. Conventional relay-assisted FSO systems, however, are designed around two basic assumptions: relays are buffer-free, and relays are stationary. This article proposes to improve the performance of the existing relay-assisted FSO systems by relaxing both of these highly restrictive assumptions through the integration of UAVs as buffer-aided moving relays into the conventional relay-assisted FSO systems. Specifically, two possible simple integration scenarios are proposed and analyzed through simulation. The obtained simulation results demonstrate the great potential associated with the proposed highly promising, innovative, hybrid FSO architecture. Given that high performance gains are observed under small buffer sizes, it becomes conceivable to employ buffer-aided moving relaying UAVs to serve a variety of other purposes. This includes, for instance, having these UAVs oversee the operation of amateur drones for potential misbehavior or wrongdoing within the area of their deployment.

Traditionally, service-specific network functions (NFs) (e.g., Firewall, intrusion detection system, etc.) are executed by installation-and maintenance-costly hardware middleboxes that are deployed within a datacenter network following a strictly ordered chain. NF virtualization (NFV) virtualizes these NFs and transforms them into instances of plain software referred to as virtual NFs (VNFs) and executed by virtual machines, which, in turn, are hosted over one or multiple industry-standard physical machines. The failure (e.g., hardware or software) of any one of a service chain's VNFs leads to breaking down the entire chain and causing significant data losses, delays, and resource wastage. This paper establishes a reliability-aware and delay-constrained (READ) routing optimization framework for NFV-enabled datacenter networks. READ encloses the formulation of a complex mixed integer linear program (MILP) whose resolution yields an optimal network service VNF placement and traffic routing policy that jointly maximizes the achieved respective reliabilities of supported network services and minimizes these services' respective end-to-end delays. A heuristic algorithm dubbed Greedy-k-shortest paths (GSP) is proposed for the purpose of overcoming the MILP's complexity and develop an efficient routing scheme whose results are comparable to those of READ's optimal counterparts. Thorough numerical analyses are conducted to evaluate the network's performance under GSP, and hence, gauge its merit; particularly, when compared to existing schemes, GSP exhibits an improvement of 18.5% in terms of the average end-to-end delay as well as 7.4% to 14.8% in terms of reliability. 1 2004-2012 IEEE. Scopus

In typical wireless networks, multihop communication is a method used to establish connectivity between distant nodes. Adapting this technique to vehicular networks requires bypassing several challenging constraints imposed by the nature of vehicular environments (e.g., high mobility and speeds and repetitive link disruptions). This paper revolves around establishing a multihop connectivity path between an isolated source vehicle $S$ and a faraway gateway roadside unit (RSU) $D$ through cooperative vehicles serving as intermediate relays. A stochastic model is formulated for the purpose of deriving an expression for the probability of the existence of a connectivity path between $S$ and $D$ . Then, the dynamic changes in the network topology are carefully examined to present a tight upper bound for the average end-to-end packet delivery delay. Finally, taking into account the inherent contention-based nature of the employed IEEE 802.11 p medium access control (MAC) protocol, together with several other limiting factors such as relay unavailability and hidden terminals, the per-hop and the end-to-end throughput expressions are presented. Extensive simulations are conducted for the purpose of validating the proposed model and examining the system's performance.

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond 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 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 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.

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.

Cyberspace continues to host highly sophisticated malicious entities that have demonstrated their ability to launch debilitating, intimidating, and disrupting cyber attacks. Recently, such entities have been adopting orchestrated, often botmaster- coordinated, stealthy attack strategies aimed at maximizing their targets’ coverage while minimizing redundancy and overlap. The latter entities, which are typically dubbed as bots within botnets, are ominously being leveraged to cause drastic Internet-wide and enterprise impacts by means of severe misdemeanors. While a plethora of literature approaches have devised operational cyber security techniques for the detection of such botnets, very few have tackled the problem of how to promptly and effectively takedown such botnets. In the past three years, we have received 12 GB of daily malicious real darknet data (i.e., Internet traffic destined to half a million routable but unallocated IP addresses or sensors) from more than 12 countries. This article exploits such data to propose a novel Internet-scale cyber security capability that fuses big data behavioral analytics in conjunction with formal graph theoretical concepts to infer and attribute Internet-scale infected bots in a prompt manner and identify the niche of the botnet for effective takedowns. We validate the accuracy of the proposed approach by employing 100 GB of the Carna botnet, which is a very recent real malicious Internet-scale botnet. Since performance is also an imperative metric when dealing with big data for network security, this article further provides a comparison between two trending big data processing architectures: the almost standard Apache Hadoop system, and a more traditional and simplistic multi-threaded programming approach, by employing 1 TB of real darknet data. Several recommendations and possible future research work derived from the previous experiments conclude this article.

To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for virtual network functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with NFV infrastructure, among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays and formulate the joint problem of VNF scheduling and traffic steering as a mixed integer linear program. Our objective is to minimize the makespan/latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a genetic algorithm-based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%-20% in the simulated scenarios. 2016 IEEE. Scopus

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation and blockage. We study the impact of those challenges on the performance of LiFi in an indoor environment using measurement-based channel models. We adopt spatial modulation (SM), which has been shown to be energy efficient in many applications, including LiFi. We consider two configurations for placing the photodiodes (PDs) on the user equipment (UE). The first one is referred to as the screen receiver (SR) whereby all the PDs are located on one face of the UE, whereas the other one is a multi-directional receiver (MDR), in which the PDs are located on different sides of the UE. The latter configuration was motivated by the fact that SR exhibited poor performance in the presence of random device orientation and blockage. We show that MDR outperforms SR by over $10$ dB at BER of $3.8\times10^{-3}$. Moreover, an adaptive access point (AP) selection scheme for SM is considered where the number of APs are chosen adaptively in an effort to achieve the lowest energy requirement for a target BER and spectral efficiency. The user performance with random orientation and blockage in the whole room is evaluated for sitting and walking activities. For the latter, we invoke the orientation-based random waypoint (ORWP) mobility model. We also study the performance of the underlying system on the uplink channel where the same techniques are used for the downlink channel. Specifically, as the transmitted uplink power is constrained, the energy efficiency of SM is evaluated analytically. It is shown that the multi-directional transmitter (MDT) with adaptive SM is highly energy efficient. As a benchmark, we compare the performance of the proposed framework to that of the conventional spatial multiplexing system, and demonstrate the superiority of the proposed one., 30 pages, 14 figures, Journal paper

The analysis of large-scale cyber attacks, which utilized millions of exploited Internet of Things (IoT) devices to perform malicious activities, highlights the significant role of compromised IoT devices in enabling evasive and effective attacks at scale. Motivated by the shortage of empirical data related to the deployment of IoT devices, and the lack of understanding about compromised devices and their unsolicited activities, in this paper, we leverage a big data analytics framework ( Apache Spark ) to design and develop a scalable system for automated detection of compromised IoT devices and characterization of their unsolicited activities. The system utilizes IoT device information and passive network measurements obtained from a large network telescope, while implementing an array of data-driven methodologies rooted in data mining and machine learning techniques, to provide a macroscopic view of IoT-generated malicious activities. We evaluate the system with more than 4TB of passive network measurements and demonstrate its effectiveness in the network forensic investigation of compromised devices and their activities, in near real-time. In addition, we empirically analyze and elaborate on the capabilities of the developed system as a scalable infrastructure, which can support a number of applications that enable IoT-centric forensics.

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resources over substrate networks. However, many cloud providers are very concerned about improving service reliability under catastrophic disaster conditions yielding multiple system failures. To address this challenge, this work presents a novel failure region-disjoint VN mapping scheme to improve VN mapping survivability. The problem is first formulated as a mixed integer linear programming problem and then two heuristic solutions are proposed to compute a pair of failure region-disjoint VN mappings. The solution also takes into account mapping costs and load balancing concerns to help improve resource efficiencies. The schemes are then analyzed in detail for a variety of networks and their overall performances compared to some existing survivable VN mapping schemes. 1968-2012 IEEE. Scopus