Search

This work presents an investigation and assessment framework, which, supported by realistic data, aims at provisioning operators with in-depth insights into the consumer-perceived Quality-of-Experience (QoE) at public Electric Vehicle (EV) charging infrastructures. Motivated by the unprecedented EV market growth, it is suspected that the existing charging infrastructure will soon be no longer capable of sustaining the rapidly growing charging demands; let alone that the currently adopted ad hoc infrastructure expansion strategies seem to be far from contributing any quality service sustainability solutions that tangibly reduce (ultimately mitigate) the severity of this problem. Without suitable QoE metrics, operators, today, face remarkable difficulty in assessing the performance of EV Charging Stations (EVCSs) in this regard. This paper aims at filling this gap through the formulation of novel and original critical QoE performance metrics that provide operators with visibility into the per-EVCS operational dynamics and allow for the optimization of these stations’ respective utilization. Such metrics shall then be used as inputs to a Machine Learning model finely tailored and trained using recent real-world data sets for the purpose of forecasting future long-term EVCS loads. This will, in turn, allow for making informed optimal EV charging infrastructure expansions that will be capable of reliably coping with the rising EV charging demands and maintaining acceptable QoE levels. The model’s accuracy has been tested and extensive simulations are conducted to evaluate the achieved performance in terms of the above-listed metrics and show the suitability of the recommended infrastructure expansions.

Millimeter wave (mmWave) full-duplex (FD) is a promising technique for improving capacity by maximizing the utilization of both time and the rich mmWave frequency resources. Still, it has restrictions due to FD self-interference (SI) and mmWave’s limited coverage. Therefore, this study dives into FD mmWave MIMO with the assistance of reconfigurable intelligent surfaces (RIS) for capacity improvement. First, we demonstrate the angular-domain reciprocity of FD antenna arrays under the far-field planar wavefront assumption. Accordingly, a strategy for joint downlink-uplink (DL-UL) channel estimation is presented. For estimating the SI channel, the direct channel, and the cascaded channel, the Khatri-Rao product-based compressive sensing (KR-CS), distributed CS (D-CS), and two-stage multiple measurement vector-based D-CS (M-D-CS) frameworks are proposed, respectively. Additionally, we propose a passive beamforming optimization solution based on the angular-domain cascaded channel. With hybrid beamforming architectures, a novel hybrid weighted minimum mean squared error method for SI cancellation (H-WMMSE-SIC) is proposed. Simulations have revealed that joint DL-UL processing significantly improves estimation performance in comparison to separate DL/UL channel estimation. Particularly, when the interference-to-noise ratio is less than 35 dB, our proposed H-WMMSE-SIC offers spectral efficiency performance comparable to fully-digital WMMSE-SIC. Finally, the computational complexity is analyzed for our proposed methods.

We consider a two-user uplink cooperative rate-splitting multiple access (C-RSMA) and seek to maximize the minimum signal-to-interference-plus-noise ratio (SINR) by jointly optimizing the beamforming at the base station (BS) and device transmit power. Due to the high coupling among the variables, the problem is non-convex and difficult to solve directly. To handle this challenge, we utilize successive convex approximation (SCA) and difference of convex (DC) based approach to approximate the non-convex problem to a convex one. The simulation results demonstrate that our proposed scheme can outperform cooperative non-orthogonal multiple access (C-NOMA) and other baseline schemes.

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often cause a high processing delay at the physical layer. Hence, the obtained solutions are rendered sub-optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and inference complexity, as well as its application in a practical LiFi optimization problem.

Due to the low inertia of inverter-based islanded microgrids (IMGs), these systems require a delicate and accurate load frequency control (LFC) scheme. The deployment of such a control scheme, which preserves the balance between the load and generation, needs a cyber layer on top of the physical system that makes IMGs an appealing target for a variety of cyber-physical attacks (CPAs). Among these CPAs, there is a family of malicious CPAs whose aim is to compromise the LFC scheme by changing the topology of IMG and its parameters. On this basis, an online system identification method is developed to estimate the parameters of IMG using the recursive least square forgetting factor (RLS-FF) approach. Then, based on the estimated parameters, an anomaly-based intrusion detection system (IDS) is developed to identify CPAs and distinguish them from the uncertainties in the normal operation of IMG. Following anomaly detection, a mitigation scheme is proposed to regulate the IMG’s frequency using an adaptive interval type-2 fuzzy logic controller (IT2FLC). The proposed IT2FLC uses different types of distributed energy resources (DERs)—i.e., tidal power plants and solar panels which are, respectively, equipped with inertia emulation and droop-based controllers—to improve the frequency excursion resulting from CPAs. The simulation results verify the performance of the developed detection and mitigation schemes, particularly when the RLS-FF parameters, i.e., forgetting factor, covariance matrix, and reset parameter, are obtained through the grey wolf optimization (GWO) algorithm. Furthermore, the designed mitigation scheme is corroborated by comparing its performance with several well-known attack-resilient control frameworks in LFC studies, e.g., linear quadratic regulator (LQR) and $H_{\infty }$ , using real-time simulations.

Faced with the threat of climate change, the world is rapidly adopting electric vehicles (EVs); however, the EV ecosystem is vulnerable to cyberattacks. In this article, we present a security-oriented real-time cosimulation testbed for the EV ecosystem and the power grid.

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 a 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 (AO) 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 jointly optimizes 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 (DC) 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.

Intelligent transportation systems are thriving thanks to a wide range of technological advances, namely 5G communications, the Internet of Things (IoT), artificial intelligence, and edge computing. Meanwhile, 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. In this article, we consider 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 help in improving the connectivity and energy efficiency of the UAV. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoT devices' 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, 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. Finally, extensive simulations are conducted to demonstrate the efficiency of our solution approach that, in many cases, outperforms other methods by more than 50%. We also show that integrating an RIS with a UAV in IoT networks can improve the UAV energy efficiency.

In this letter, we investigate the power allocation and decoding order at the base station (BS) of two-user uplink (UL) cooperative non-orthogonal multiple access (C-NOMA)-based cellular networks. We formulate a joint optimization problem aiming at maximizing the minimum user achievable rate. Given that the problem at hand is non-convex, as an alternative, we derive low-complexity and near optimal analytical expressions for the power allocation coefficients. We demonstrate through numerical results that the proposed scheme provides superior performance in comparison with the traditional UL NOMA. We also demonstrate that, in UL C-NOMA, decoding the far NOMA user first at the BS provides the best performance.

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

Fifth generation (5G) networks are designed to bring enhanced network operational efficiency to serve a wide range of emerging services. Toward this purpose, 5G adopts a service-based architecture (SBA) that features web-based technologies such as Hypertext Transfer Protocol version 2 (HTTP/2) used for signaling and application programming interfaces (APIs) for service delivery. Several works in the literature have reported that the shift toward the aforementioned technologies brings potential cybersecurity challenges to the 5G network. In this article, we discuss different security features introduced by 5G SBA and explore these security challenges and their solutions in this new architecture. We carefully examine HTTP/2 features, standards, and custom headers, and discuss their security implications in 5G SBA. We comment on the applicability of some known HTTP/2 attacks in 5G SBA in light of the standardized APIs, and discuss the security opportunities and research directions brought by this protocol and its related technologies.

Reconfigurable intelligent surface (RIS) emerged as a promising technology for programming the wireless propagation environment, ushering wireless networks into a new era of high connectivity, and better spectrum and energy efficiency. Research has gained momentum over the past few years to study the gains of RIS and the optimal configuration of its elements. This paper is concerned with the optimal configuration of RIS elements; we consider a single base station (BS) serving multiple single-antenna users and formulate the problem of minimizing the total transmit power while jointly optimizing the allocated power to users and the RIS phase shifts. In line with the existing literature, the problem is decomposed into power control and RIS phase shift sub-problems. The power control sub-problem is the conventional power problem which is solved optimally through closed-form expression. Then, we tackle the problem of RIS passive beamforming optimization and propose two solutions leveraging a linear transformation and element-wise Karush-Kuhn-Tucker (KKT), respectively. The main idea of the linear transformation approach is to reduce the number of optimization variables to the number of users associated with the RIS from the number of its elements which is, in general, very large. On the other hand, the main idea of the element-wise KKT-based approach is to obtain a closed-form expression for the RIS phase shifts for a multi-user scenario, similar to the single-user case. Simulation results show that the proposed schemes have performance in total transmitted power close to the numerical solution and have much lower computational complexity than the baseline solutions. Moreover, simulation results show that the proposed solutions have a competitive performance in terms of optimality and complexity for large-scale RIS. Hence, our approach represents a general framework for configuring RIS elements in real scenarios.

In this letter, we investigate an outdoor and indoor wireless communication network with the assistance of a 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 double-sided RIS with relay is utilized. Since the formulated problem is non-convex with highly-coupled variables, a successive convex approximation (SCA) and penalty based alternating optimization (AO) algorithm with difference of convex (DC) functions 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 baselines.

Next-generation communication systems, including the sixth generation (6G) cellular systems, are expected to support a wide range of new ultra-reliable low-latency communications (URLLC). These emerging applications strictly demand high data rates and/or massive connectivity besides their strict reliability and latency requirements. To enable such URLLC applications alongside the enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC), it is imperative to develop spectrally efficient methods, and non-conventional technologies and networking architectures. To this end, reconfigurable intelligent surfaces (RISs) have recently emerged as a key promising technology to enhance the capabilities of 6G wireless networks. The RIS technology offers a wide range of advantages, including offering a low-cost and energy-efficient solution for controlling the propagation environments. We study the potential of deploying RIS in 6G networks with an emphasis on accommodating the coexistence of URLLC and eMBB traffic. Specifically, we demonstrate through several case studies the potential of integrating RIS into 6G networks with the objective of meeting the requirements of URLLC without compromising the performance of eMBB users. Motivated by this and the fact that the RIS technology has not been fully investigated, we propose a number of relevant research directions and highlight what kind of challenges such directions may entail. We also elaborate on how such challenges, once tackled, may lead to realizing the full potential of integrating RIS into future wireless networks.

An adequate charging infrastructure advocates to ameliorate the range anxiety to propel the disparaged electric vehicle (EV) market. But, the high initial installation cost, requirement of suitable places and the anticipated immense load on the grid during peak times hinder to elongate the charging station network, especially in urban areas. Fortunately, the bidirectional energy transferring capability between vehicles (i.e., V2V) may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a stationary charging infrastructure. In this work, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. A provider intends to maximize the served number of EVs using its limited number of charging trucks, when an EV should be considered as served only if it would be fully charged during its declared charging window. All charging requests are assumed to be received by an agent which provisions a route and schedule for each charging truck and all trucks should return to the depot after serving EVs. We formulate this combinatorially hard problem as an integer linear program (ILP) to maximize the number of served EVs by determining the optimal trajectory of each truck. Owing to its complexity, we present a solution methodology by decomposing the problem using Dantzig-Wolfe decomposition approach; we divide the problem into one master problem and a set of pricing problems (one for each EV) and achieve the solution iteratively. Though the solution achieved from the decomposition might not be optimal, it is faster to be applicable in practice. We also compare the performance with two heuristic algorithms and report on the collected results.

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.

Service restoration (SR) through network configuration in power systems is a highly investigated problem. In SR, utilities reconfigure the transmission and distribution networks to supply the consumer load demands. However, the advancements in distributed energy storage (DES) systems at the consumer side define a new dimension for SR. In this paper, we approach the network reconfiguration for SR in the presence of DES through mathematical modeling. We present a mathematical model that captures the properties of the power system, and reconfigures the network to supply consumer demand over available lines. This model considers power supply from DES, and proposes the least cost SR plan. We evaluate the proposed approach on the IEEE 14-, 30-, and 57-Bus systems, and report on the collected results. The collected results demonstrate the importance of the available DES in power SR.

This paper uniquely leverages unsolicited real darknet data to propose a novel system, CSC-Detector, that aims at identifying Cyber Scanning Campaigns. The latter define a new phenomenon of probing events that are distinguished by their orchestration (i.e., coordination) patterns. To achieve its aim, CSC-Detector adopts three engines. Its fingerprinting engine exploits a unique observation to extract probing activities from darknet traffic. The system's inference engine employs a set of behavioral analytics to generate numerous significant insights related to the machinery of the probing sources while its analysis engine exploits the previously obtained inferences to automatically infer the campaigns. CSC-Detector is empirically evaluated and validated using 240 GB of real darknet data. The outcome discloses 3 recent, previously unreported large-scale probing campaigns targeting diverse Internet services. Further, one of those inferred campaigns revealed that the sipscan campaign that was initially analyzed by CAIDA is arguably still active, yet operating in a stealthy, very low rate mode. We envision that the proposed system that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing campaigns. This would be utilized for early cyber attack warning and notification as well as for simplified analysis and tracking of such events.

Vehicular networks have recently been witnessing an upsurge of interest in energy consumption control. Precisely, in the majority of vehicular networking scenarios, roadside units are deployed along roadways in rural areas as well as on the sides of long highways where a direct connection to the electric grid is merely available. In such situations, these roadside units will be equipped with rechargeable batteries with maintenance requiring costly human intervention. Thus far, the literature offers several proposals of efficient operation schemes for roadside units aiming at optimizing their energy consumption, hence, elongating their duration of availability and participation in the network. Energy harvesting presents itself as an appealing alternative to power/recharge nodal batteries in wireless networks. This article starts by presenting a concise general overview of energy harvesting sources, techniques, and applications. Second, it investigates the feasibility of energy harvesting in vehicular networks, specifically the different challenges confronting its applicability in vehicular environments. Finally, pending related open research problems and directions are presented.

Advanced wireless technologies are, nowadays, being exploited as means for intelligent transportation management and on-the-road driving assistance. However, the recent synergic efforts in both industry and academia are symptomatic of a paradigm shift in Intelligent Transportation Systems. Today's vehicles, being equipped with computerized modules and wireless devices, characterize themselves by a revolutionary smart personality. In particular they can carry and distribute information, they inter-communicate and are capable of communicating with other stationary units deployed along roadways. Most importantly, they can sense. Ultimately, the transportation infrastructure embraces versatile wireless communication systems with the objective of provisioning Wireless Access in Vehicular Environments (WAVE). In order to catalyze the realization of this objective, the U.S. Federal Communications Commission (FCC) has allocated 75MHzof spectrum for Dedicated Short Range Communications (DSRC). The Institute of Electrical and Electronics Engineers (IEEE) has specifically tailored the 802.11pstandard to regulate access to this spectrum. The IEEE 1609.4 protocol allows for multi-channel access and is currently being standardized. Alternatively, researchers have engaged in the design of scheduling policies for the purpose of increasing the spectrum access efficiency and optimizing the performance of vehicular networks in terms of several classical metrics. The recently emerging spectrum scarcity problem has led numerous investigations on the potential exploitation of cognitive radios as well as the feasibility of Vehicular Dynamic Spectrum Access (VDSA) schemes with the objective of: a) enhancing spectrum utilization and b) improving the network's throughput and response time. This paper sheds the light over the latest advancements in each of the above-mentioned research sectors and highlights pending open issues in each of them.

In this paper, we consider the indoor localization problem, i.e., identifying the Cartesian coordinates of an object or a person under the roof. To solve this problem we consider an RF-based localization method called profiling, a two-step process, where a radio map of the monitored area is first constructed by collecting signal strength from known locations. An unknown location is then predicted using this radio map as a reference. In this paper, we first propose a K nearest neighbor (KNN) profiling-based localization method dubbed LEMON (location estimation by mining oversampled neighborhoods). It is based on a low-cost, low-power wireless devices and ensures good accuracy compared to the state-of-the-art. We then propose a variant of LEMON called combinatorial localization which exhaustively searches for the best possible set of nearest neighbors. We further define a Bayesian network model for the same localization problem. The performance of these methods is evaluated through extensive experiments in various indoor areas. We found an interesting outcome that the simple KNN-based approach can offer better localization accuracy compared to other complex localization methods. Thus we further enhance the performance of the KNN-based approach using multiple RF channels.

Given the advent of Intelligent Transportation Systems (ITSs), drivers and passengers could now spend more of their time enjoying entertainment applications, e.g., watching TV or streaming movies. However, such services can drastically increase the traffic load on the existing network infrastructure (i.e. Roadside Units (RSU) and Cellular Base Stations (CBS)). Recently, Unmanned Aerial Vehicles (UAVs) have been playing a remarkable role in offloading terrestrial networks and providing cellular services thanks to their agility and flexibility. Hence, this paper explores a cooperative approach for content caching and delivery in the context of internet of connected vehicles, where a RSU, having access to a library of contents but with limited communication coverage, collaborates with a UAV to deliver contents to vehicles on a road segment. In this context, the connected RSU is responsible for delivering contents to the UAV cache unit by leveraging passing by vehicles. The RSU loads the contents on these vehicles that in turn upload them to the UAV cache unit. We model this cooperation problem mathematically as a mixed integer non-linear programming (MINLP) problem with the objective to maximize the number of served vehicles. Owing to the complexity of solving this problem, it is alternatively cast as an MDP whose solution is obtained through a Dual-Task Reinforcement Learning method (DTDRL). Simulation results show the superiority of our proposed collaborative solution over non-collaborative methods.

IEEE 802.11 CSMA/CA has been widely deployed as the primary MAC protocol for ad hoc networks and wireless LANs. It was designed with the assumption that nodes would follow proper operation of the protocol. Nodes, however, may choose to deviate in order to either obtain an unfair share of the available bandwidth or disrupt the services of the network. Accordingly, a misbehavior rooted at the MAC layer can be classified as selfish or malicious behavior. This article surveys the research activities related to MAC layer misbehavior; based on the operating principles and the objective of misbehaving nodes, we classify MAC layer misbehavior and present descriptions for each solution. We conclude with a brief summary of the key ideas and a general direction for future work.

The efficient use of network capacity in shared restoration schemes strongly depends upon the path selection procedure. In this paper we propose and evaluate path selection algorithms for sharable and restorable connections in optical networks. Namely, two distributed path selection algorithms are proposed. The first approach maintains global information on network resource usage to determine link sharability and compute optimal shared paths. The second approach only relies upon local information maintained at each node. Subsequently, we present an analytical model to evaluate the performance of these path selection algorithms and show its accuracy through numerical examples. Results indicate that path selection algorithms that maximally exploit the use of reserved sharable channels do not necessarily result in fast restoration; rather these two optimality criteria can conflict most of the time. Additionally, algorithms that maximally exploit the sharability condition typically result in lower scalability and higher complexity.

This paper investigates and compares the performance of two GMPLS-based distributed control and management protocols for dynamic lightpath provisioning in future IP networks. The first protocol is a global information-based link state approach that consists of both an integrated RWA algorithm and a signaling algorithm. Two triggering mechanisms for the LSAs update procedures are considered; one is periodic-based and the other is threshold-based. The second protocol is a local-information based fixed alternate link routing approach where the signaling protocol is closely integrated with the RWA protocols.

This work focuses on developing and implementing comprehensive unified constraint-based routing algorithms within the generalized multi-protocol label switching framework (GMPLS) to provision “sub-wavelength” circuits (low-rate traffic streams). Constraint-based routing is further augmented in this work by dynamically routing both an active and another alternate link/node-disjoint backup path at the same time in order to provision a given connection request. This new integrated approach combines both IP routing and optical resource allocation to setup end-to-end connections.

Vehicular network is the enabler of various services for transportation means including safety and infotainment applications. It can better deal with the highly dynamic nature of roads and highways. Meanwhile, the existing infrastructure of the cellular networks is unable to cope with the heavy traffic imposed mainly by videos. Thus, the cellular operators may leverage Roadside Untis (RSU) to offload video traffic and provide better quality of experience for vehicle customers. In this paper, we propose a pricing model that motivates RSU owners to cooperate with the cellular operators in order to offer better quality of experience (QoE) and offload substantial part of videos traffic from the cellular networks. The pricing model also motivates the vehicle owners to cooperate with the RSU to deliver contents to each other and aims to maximize RSU revenue and provide a wide range of QoS levels to the content providers in order to give them a degree of freedom to choose the suitable level of quality for their customers and based on their allocated budgets. We prove this problem is hard to solve, then we propose a lightweight greedy methods as alternative solutions. The conducted results show the efficiency of the proposed solutions and their ability to obtain results similar or so close to the optimal solutions.