International audience

Electric Vehicles (EVs) have become a major element in the global push to combat climate change, given their ability to reduce the transportation sector’s emissions. To support the increasing number of EVs on the road, EV Charging Stations (EVCSs) are being deployed and have become a core element of the transportation infrastructure. EVCSs with individual web portals have been widely studied and proven to be vulnerable to network-based attacks. On the other hand, EVCSs that do not host web portals and cannot be accessed remotely are considered more secure. These EVCSs are generally considered to be more secure and have been overlooked in previous studies. Consequently, in this work, we present the first attack framework that exploits design flaws in this type of EVCS to hijack their operation. Our tests were performed on six actual EVCSs that follow the deployment strategy commonly preferred in North America by most operators and a few operators in Europe. We demonstrate how adversaries can successfully exploit the discussed vulnerabilities to gain unauthorized access to the EVCS configuration and acquire administrator privileges. We then proceed to craft multiple attacks to affect the power grid, steal money, or deteriorate EVCS availability.

Within the rapidly expanding Internet of Vehicles (IoV) landscape, the demand for network services to accommodate data-intensive applications has become increasingly paramount. However, IoV faces significant challenges, including high latency, limited coverage in remote areas, network congestion, and privacy concerns in content popularity prediction. To address these challenges, we introduce the Scalable Optimisation for Networked Aerial-vehicles (SONA) scheme, which reduces latency through advanced caching techniques while enhancing coverage and ensuring required data rates using Unmanned Aerial Vehicles (UAVs). UAVs are deployed to augment coverage in areas lacking RoadSide Unit (RSU) support and to assist in scenarios where RSUs are overwhelmed, ensuring continuous data rate provision. Our scheme introduces a novel mathematical optimisation model and machine learning algorithms: Federated Learning (FL) to collaboratively predict content popularity without exposing user data, Reinforcement Learning (RL) to dynamically optimise UAV placement and energy-efficient deployment, and blockchain to enable secure, decentralized coordination between RSUs and UAVs for real-time decision-making. Simulation results demonstrate the effectiveness of our approach, achieving an average delay of 8 ms, an average cache hit rate of 88.43%, and satisfying desired data rate requirements in 84.85% of scenarios, significantly improving IoV performance.

Fifth Generation (5 G) and beyond networks rely on the HTTP/2 protocol for signaling between core Network Functions (NFs). While HTTP/2 vulnerabilities have been exploited to perform various types of Denial of Service (DoS) attacks in web environments, their impact on telecommunication networks remains under-studied. Though secure by design, the 5 G Service-Based Architecture (SBA) can be vulnerable to misconfigurations and virtualization exploits, particularly with Mobile Network Operators (MNOs) using hyper-scale technologies. This work addresses the lack of practical studies and analyses on the impact of HTTP/2 attacks on 5 G networks, especially given the absence of a 5G-compliant dataset for anomaly detection. Utilizing the open-source free5GC testbed and UERANSIM emulator, we emulate six different HTTP/2 attacks on various NFs within the 5 G SBA. We analyze their impact on the network and demonstrate that many of them cause cascading effects on other NFs involved in related jeopardized 5 G procedures. Our emulations include both malicious and normal network behavior, resulting in the first 5 G anomaly detection dataset that we are aware of. Using CICFlowmeter, we extract flow-based features known for their anomaly detection capabilities and train multiple machine learning models. These models can serve as benchmarks for detecting HTTP/2 attacks in 5 G networks.

The integration of sensing, communication, and power transfer (ISCPT) has emerged as a promising paradigm for energy- and spectrum-efficient 6G networks. Recent studies have revealed that sensing accuracy, achievable rate, and harvested energy inherently exhibit conflicting design requirements and form a nontrivial trade-off region. However, existing integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) schemes typically optimize at most two of these functionalities and lack a unified resource-allocation framework that can flexibly balance all three under stringent power budgets. Motivated by this gap, we consider a reconfigurable intelligent surface (RIS)-aided ISCPT system that employs non-orthogonal multiple access (NOMA) to support multi-user connectivity. In the proposed design, the RIS reshapes the wireless propagation environment in an energy-efficient manner to enhance both sensing and power transfer, while NOMA provides power-domain multiplexing to improve spectral efficiency and user scalability. We formulate a total transmit power minimization problem by jointly optimizing the base-station beamforming, RIS phase shifts, power splitting (PS) ratios, and NOMA decoding order under quality-of-service (QoS), Cramér–Rao-bound-based sensing accuracy, and energy-harvesting constraints. The resulting problem is highly non-convex due to the coupling among the design variables. To solve it efficiently, we develop a block coordinate descent (BCD)-based algorithm that leverages semidefinite relaxation (SDR), successive convex approximation (SCA), and the alternating direction method of multipliers (ADMM). Simulation results verify that the proposed RIS-aided NOMA-ISCPT framework significantly reduces the base-station transmit power while achieving favorable trade-offs among communication reliability, sensing precision, and energy-transfer efficiency.

Device-to-device (D2D)-aided mobile edge computing (MEC) has emerged as a key enabling technology for future sixth-generation (6G) wireless networks. The goal of D2D-MEC is to reduce system latency for edge user equipments (UEs) by enabling access to cloud computing capabilities at the network edge, thereby supporting high transmission rates. To address the vulnerability of communication signals to physical obstructions, we employ relay techniques to enhance system performance and extend coverage. However, relay nodes and base station (BS) are typically equipped with large-scale antenna arrays, which lead to significant implementation costs and limiting practical deployment. To address this issue in a cost-efficient manner without sacrificing system performance, movable antenna (MA) technology is introduced. The key idea of MA technology lies in dynamically optimizing antenna positions to improve system capacity. Therefore, we propose a novel resource allocation framework for an movable relay-aided D2D-MEC system. The proposed scheme jointly optimizes the MA positions at UEs, relays, and the BS, along with the associated beamforming vectors, MEC server resource allocation, and computational task offloading rates. The objective is to minimize the maximum system latency while satisfying both computation and communication rate constraints. Furthermore, considering that current MA control mechanisms primarily rely on mechanical actuation, MA movement delay is incorporated into the latency model to capture the trade-off between antenna mobility and system delay. The resulting optimization problem is non-convex and involves multiple coupled variables. To solve this problem, we develop a parallel and distributed algorithm based on the penalty dual decomposition (PDD) framework, which is further integrated with the successive convex approximation (SCA) method to obtain a suboptimal solution. Simulation results demonstrate that the proposed algorithm significantly reduces system latency and enhances overall efficiency compared to benchmark schemes employing conventional fixed-position antennas (FPAs) at the relays and BS.

The increasing sophistication and complexity of Advanced Persistent Threats (APTs) pose significant challenges to security practitioners. To proactively protect against these threats, security practitioners rely on the generation of testflows, structured sequences of actions designed to verify whether the tactics and behaviors of an APT are present within their organization. However, manually creating such testflows is time-consuming, error-prone, and highly dependent on expert knowledge. Moreover, existing automated approaches suffer from several limitations, including validity, efficiency, and insufficient domain adaptation. To address these challenges, this paper introduces CTI-RAGFlow, to automate the generation of relevant, valid, and effective testflows from unstructured threat reports tailored to specific organizational environments. CTI-RAGFlow introduces three key contributions: (i) a dual-ontology approach, that integrates both a system ontology representing the operational environment and a cybersecurity ontology capturing adversary tactics, techniques, and procedures, improving the precision and accuracy of generated testflows; (ii) a fact-based context retrieval mechanism that combines a hypergraph structured knowledge base with a Retrieval-Augmented Generation pipeline using Large Language Models; and (iii) a fully automated testflow generation process that minimizes manual effort, reduces human error, and facilitates the generation of valid testflow. We evaluate CTI-RAGFlow against three widely used LLM models (e.g., base and fine-tuned models) using publicly available CTI reports for three well-known APTs (e.g., APT41, APT29, APT28). The results show that CTI-RAGFlow outperforms the baselines in terms of semantic relevance, coverage, validity, and effectiveness in verifying multi-stage cyberattack scenarios.

This paper presents an experimental and simulationbased evaluation of two data center network (DCN) topologies: the widely adopted hierarchical Clos architecture and STRAT, a flat, expander-based topology designed around passive optical interconnects. While Clos offers proven scalability and performance, it incurs hardware complexity and suffers from congestion in oversubscribed scenarios. STRAT eliminates aggregation and spine layers entirely—using only Top-of-Rack (ToR) switches interconnected via static optical patch panels—to reduce cost, simplify deployment, and enhance path diversity. Our goal is to assess these topologies based on their inherent architectural properties—namely throughput, congestion resilience, scalability, and cost—without relying on congestion control protocols or centralized traffic engineering. To this end, we adopt simple forwarding schemes based purely on local information: ECMP for Clos, and ECMP with Dynamic Group Multipath (DGM) for STRAT. We evaluate both topologies on a physical testbed built from commercial Ethernet switches and further validate scalability through packet-level simulations of networks with up to 256 switches and 1,024 hosts using OMNeT++. We also introduce DEALER, a lightweight routing algorithm tailored to STRAT’s topology, and evaluate its effectiveness in dynamic conditions. Our results show that STRAT achieves up to 43% higher throughput and requires approximately 40% fewer switches than a comparable Clos topology. These gains are further supported by Load Area Under Curve (LAUC) analysis and congestion hotspot visualizations. Overall, our study highlights STRAT as a compelling and practical alternative to conventional DCN architectures, offering deployable scalability, improved performance under load, and reduced infrastructure cost.

This Special Issue contains a set of remarkable papers covering various recent research advances towards resilient Communication Networks for an hyper-connected World. Papers are organized into five categories: (i) Resilient Architectures for Next-Generation Networks, (ii) Edge, IoT, and Cyber-Physical Systems, (iii) Vehicular, Mobile, and Aerial Networks, (iv) Optical, Hybrid, and Satellite-based Resilient Communications, and (v) Security, Trust, and Resilience in Services and Applications. The editorial begins with an overview of the field and proceeds with a summary of the twenty-two papers included in this Special Issue.

The multi-base-station cooperative (MBC) ISAC mechanism is promising to satisfy the higher-quality sensing requirements of accuracy and coverage in 6G. STAR-RIS can help ISAC BSs provide cooperative sensing and communication (S&C) across full-space radio signal coverage. Short-packet transmission (SPT) is expected to be performed by ISAC BSs in 6G vertical applications to provide enhanced ultra-reliable low-latency S&C services. This paper proposes a STAR-RIS-assisted heterogeneous (SRH) MBC ISAC mechanism within the finite blocklength (FBL) regime. Multiple ISAC BSs and a communication-only (Comm-only) BS collaborate to provide S&C services to users and the target using STAR-RISs and cooperative detection results. A composite SPT (C-SPT) time frame structure is proposed by dynamically designing the S&C block lengths from the perspectives of the sub-frame and the entire transmission block. The systemic sum achievable capacity (SAC) over the entire transmission block is maximized by jointly optimizing the pilot and data block lengths and powers, as well as the active and passive beamforming of ISAC BSs and STAR-RISs under constraints of temporal, spatial, and power resources. The non-convex optimization problem is solved by combining the methods of alternating optimization (AO), quadratic transform (QT), sequential rotation (SR), and particle swarm optimization (PSO). Simulation results demonstrate the superiority of the proposed SRH-MBC ISAC mechanism with the C-SPT frame structure in achieving higher systemic communication performance with the assistance of STAR-RIS.

Sixth-generation (6G) wireless networks have been driving growing demands for the full utilization of spectral efficiency and spatial degrees of freedom (DoFs). This paper investigates a non-orthogonal multiple access (NOMA) empowered integrated sensing and communication (ISAC) system assisted by movable antennas (MAs). We consider a dual functional radar and communication (DFRC) base station (BS) equipped with a two-dimensional (2D) MA array, which simultaneously senses multiple targets and serves users divided into multiple clusters. Successive interference cancellation (SIC) is employed within each cluster to suppress intra-cluster interference. To enhance the total illumination power at the sensing targets while guaranteeing the communication signal-to-interference-plus-noise-ratio (SINR) requirements at the users, we formulate an optimization problem for joint power allocation, beamforming, and antenna position design. To address this highly coupled and non-convex problem, an alternating optimization-based algorithm is proposed. We first determine the SIC decoding order by the equivalent-channel-to-interference-plus-noise-ratios (ECINRs), and derive the close-form solutions of the optimal intra-and-inter cluster power allocation coefficients. The sub-problems of beamforming and antenna position design are solved by semidefinite relaxation (SDR) and successive convex approximation (SCA) based schemes, respectively. Numerical simulation results are provided to verify the effectiveness of the proposed algorithm. The proposed algorithm significantly outperforms baseline schemes, which achieves approximately 2 dB illumination power gain compared to the conventional fixed position antennas (FPA), demonstrating the promising potential of MAs in wireless networks.

In the development of wireless communication technology, multiple-input multiple-output (MIMO) technology has emerged as a key enabler, significantly enhancing the capacity of communication systems. However, traditional MIMO systems, which rely on fixed-position antennas (FPAs) with spacing limitations, cannot fully exploit the channel variations in the continuous spatial domain, thus limiting the system's spatial multiplexing performance and diversity. To address these limitations, movable antennas (MAs) have been introduced, offering a breakthrough in signal processing and spatial multiplexing by overcoming the constraints of FPA-based systems. Furthermore, this paper extends the functionality of MAs by introducing movable rotatable antennas (MRAs), which enhance the system's ability to optimize performance in the spatial domain by adding rotational degrees of freedom. By incorporating a dynamic precoding framework based on both antenna position and rotation angle optimization, and employing the zero-forcing (ZF) precoding method, this paper proposes an efficient optimization approach aimed at improving signal quality, mitigating interference, and solving the non-linear, constrained optimization problem using the sequential quadratic programming (SQP) algorithm. This approach effectively enhances the communication system's performance.

As energy concerns grow with the rise of energy-constrained devices, it becomes imperative to design an energy-efficient and adaptive multiple access (MA) scheme, supported with accurate energy efficiency (EE) evaluation. Non-orthogonal multiple access (NOMA) enhances EE, yet downlink NOMA faces challenges in terms of computational complexity and power demands of successive interference cancellation (SIC), problematic particularly for energy-limited devices. Existing studies overlook the additional SIC power consumption at NOMA receivers, thus overestimating EE, and giving misleading insights for real system design. Besides the need for more accurate EE evaluation, an adaptive MA approach based on this additional power consumption is required. This paper proposes a SIC-power-aware adaptive SDMA/cooperative NOMA system. An optimization problem is formulated by optimizing MA mode decision, BS beamforming, power allocation factors, and strong user relaying power, to maximize the system EE. We decouple the problem into SDMA/NOMA selection and power allocation sub-problems, solved via a modified semi-orthogonal user selection (SUS) algorithm, successive convex approximation (SCA), difference-of-convex (DC) programming, and semidefinite programming (SDP) approaches. Numerical evaluation confirms the efficiency of the proposed scheme, compared to the baseline schemes.

Beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as an innovative and generalized RIS framework that provides greater flexibility in wave manipulation and enhanced coverage. In comparison to conventional RIS, optimization of BD-RIS is more challenging due to the large number of optimization variables associated with it. Typically, optimization of large-scale optimization problems utilizing traditional optimization methods results in high complexity. To tackle this issue, we propose a gradient-based meta learning algorithm which works without pre-training and is able to solve largescale optimization problems. With the objective to maximize the sum rate of the system, to the best of our knowledge, this is the first work considering joint optimization of receiving beamforming vectors at the base station (BS), scattering matrix of BD-RIS and transmission power of users equipment (UEs) in uplink rate-splitting multiple access (RSMA) communication. Numerical results demonstrate that our proposed scheme can outperform the conventional RIS RSMA framework by 22.5 %.

The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By allowing antennas to adjust positions dynamically within a finite area at transceivers, this technology enables more favourable channel conditions, optimizing performance across applications like mobile telecommunications and remote sensing. However, throughout history, the introduction of every new technology has presented opportunities for misuse by malicious individuals. Just as MAs can enhance connectivity, they may also be exploited for disruptive purposes such as jamming. In this paper, we examine the impact of an MA-enhanced jamming system equipped with$M$movable antennas in a downlink multi-user communication scenario, where a base station (BS) with$N$antennas transmits data to$K$single-antenna users. We formulate an optimization problem where the jammer determines both the antenna locations and beamforming vectors to minimize the total system sum rate. Given the non-convex nature of the problem, it is decomposed into two sub-problems, which are solved alternately until convergence. Simulation results show that an adversary equipped with MAs reduce the system sum rate by 30 % more effectively than fixed-position antennas (FPAs). Additionally, MAs increase the outage probability by 25 % over FPAs, leading to a 20 % increase in the number of users experiencing outages. The highlighted risks posed by unauthorized use of this technology, underscore the urgent need for effective regulations and countermeasures to ensure its secure application.

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with$N$elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closedform cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zeroforcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.

This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be able to transmit the signal to the other user providing an extra transmission resource and enhancing performance. Both users are equipped with a receiving MA each and a transmitting MA for the relaying user. In this regard, we formulate an optimization problem with the objective of maximizing the achievable sum rate by jointly determining the beamforming vector at the BS, the transmit power at the device and the positions of the MAs while meeting the quality of service (QoS) constraints. Due to the non-convex structure of the formulated problem and the randomness in the channels we adopt a deep deterministic policy gradient (DDPG) approach, a reinforcement learning (RL) algorithm capable of dealing with continuous state and action spaces. Numerical results demonstrate the superiority of the presented model compared to the other benchmark schemes showing gains reaching 45% compared to the NOMA enabled MA scheme and 60% compared to C-NOMA model with fixed antennas. The solution approach showed 93% accuracy compared to the optimal solution.

This paper investigates a movable antenna (MA)-aided integrated sensing and communication (ISAC) system using non-orthogonal multiple access (NOMA). A base station (BS) configured with a two-dimensional MA array simultaneously serves multiple communication users and sensing multiple targets. To enhance system capacity, superimposed symbols are transmitted to communication users, with successive interference cancellation (SIC) employed for signal decoding. Our objective is to maximize the total illumination power at the targets while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements for communication users. To achieve this goal, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the transmit power allocation, beamforming, sensing covariance matrix, and antenna positions. Numerical results show that the MA system achieves significant improvement in illumination power compared to fixed-position antennas (FPAs), with particularly significant gains under high SINR requirements.

Effective elements selection (ES) is essential to the deployment of reconfigurable intelligent surface (RIS), which, however, receives little attention. In this letter, we propose two novel ES strategies intended for deployment in RIS-assisted multiuser multiple-input-single-output (MISO) wireless networks. The first scheme is designed to maximize the effective gains of channels, while the second ES scheme presents a linear swapping selection (LSS) method that focuses on optimizing the total achievable rate. Numerical results show that the second scheme using the LSS method performs better than the first one, and is able to achieve a near-optimal performance but with significantly reduced computational complexity compared with the optimal exhaustive search scheme.

Multiaccess edge computing (MEC) plays a crucial role in providing low-latency and high-data transmission services to Internet of Things (IoT) devices. However, in remote areas where deploying edge devices is challenging, optimizing delay remains a significant research focus. To address this issue, our research investigates a multidrones-assisted IoT task offloading model. In this model, tasks generated by IoT devices equipped with energy harvesting (EH) capabilities are offloaded to MEC servers with the assistance of multiple drones. In order to monitor and manage the energy consumption of IoT devices and the task backlog of edge servers, the energy consumption and task update queues are established. We formulate a mixed integer nonlinear programming (MINLP) problem, which aims to optimize the allocation of communication and computation resources to minimize the execution latency of IoT devices. To ensure the stability of each queue, we employ the weighted perturbation method within the Lyapunov optimization framework to decompose the original problem. And a low complexity multidrones assisted offloading (MUAO) algorithm is designed. Simulation results show that MUAO consistently exhibits lower latency and energy consumption compared to the baseline scheme and other existing algorithms, while maintaining a low packet loss rate of only 5%.

To combat the growing IoT malware threat, many studies propose ML-based classification solutions, but the lack of comprehensive evaluations limits insights for developing new solutions and selecting models. Given this necessity, this work evaluates IoT malware classification models under three key challenges: temporal, architectural, and spatial inconsistencies between development and deployment datasets, which can be regarded as variables characterizing the dataset, and the challenges arise from the variable values inconsistency between the two stages. To improve the conclusions’ comprehensiveness, effectiveness, and generalizability, the evaluation is organized hierarchically across three levels based on model generation, sample variation, and inconsistency assumptions. Given the complexity of the model development pipeline, our evaluation treats each model individually and aims to conclude impacts across all models. The analysis reveals that temporal and architectural inconsistencies significantly degrade model performance, with architectural inconsistency having a greater impact, despite cross-architecture designs. Temporal inconsistency effects vary with temporal value differences, while spatial inconsistency has minimal impact, even with substantial spatial variation. Furthermore, we use one-way ANOVA to identify features contributing to family distinguishability, temporal stability, and architectural generalizability that benefit future solution design. Meanwhile, we studied a specific example to study the model performance degradation under architectural inconsistency. Finally, we summarize the lessons learned and outline potential research directions to address these challenges.

This paper investigates joint user pairing, power, and time slot duration allocation in the uplink multiple-input single-output (MISO) multi-user cooperative rate-splitting multiple access (C-RSMA) networks in half-duplex (HD) mode. We assume two types of users: cell-center users (CCU) and cell-edge users (CEU); first, we propose a user pairing scheme utilizing a semi-orthogonal user selection (SUS) and a matching-game (MG)-based approach where the SUS algorithm is used to select CCU in each pair. Afterward, the CEU in each pair is selected by considering the highest channel gain between CCU and CEU. After pairing is performed, the communication occurs in two phases: in the first phase, in a given pair, CEUs broadcast their signal, which is received by the base station (BS) and CCUs. In the second phase, in a given pair, the CCU decodes the signal from its paired CEU, superimposes its own signal, and transmits it to the BS. Moreover, utilizing uplink RSMA principle, only the CCUs split their messages into two sub-messages. Meanwhile, the messages of CEUs are kept without splitting. We formulate a joint optimization problem in order to maximize the sum rate subject to the power budget constraints of the user equipment (UE) and minimum data rate requirements at each UE. Since the formulated optimization problem is non-convex, we adopt a bi-level optimization to make the problem tractable. We decompose the original problem into two sub-problems: the user pairing sub-problem and the resource allocation sub-problem, where the user pairing sub-problem is independent of the resource allocation sub-problem, and once pairs are identified, the resource allocation sub-problem is solved for a given pair. The resource allocation sub-problem is solved by invoking a low-complexity pre-training free gradient-based meta-learning (GML) algorithm. Simulation results demonstrate that our proposed C-RSMA scheme can achieve around 100%, 51%, 53%, and 215% improvement over C-NOMA with fixed time slot allocation, RSMA, NOMA, and C-RSMA random pairing, respectively at CEU power budget of 17 dBm.

In the context of multicast Network Function Virtual(NFV)-enabled Internet of Things (IoT), numerous sensor devices must efficiently transmit data to multiple data centers for real-time monitoring and analysis. Certain data centers require aggregated data from multiple sensors for informed decision-making. Outdated information lead to incorrect decisions, resulting in economic losses. Therefore, ensuring the timely and effective delivery of information is of paramount importance. However, the issue of information freshness in multicast networks has received limited attention. The deployment Virtual Network Functions (VNFs), data scheduling, and the multitude of routing possibilities pose significant challenges to studying information freshness. We introduce Mixed-Age of Aggregated Information (MAoAI) to quantify information freshness in multicast networks, integrating Age of Information (AoI) and Age of Aggregated Information (AoAI). To address this, we propose an optimization framework for coordinating multicast service requests. This framework takes into account VNF deployment and sharing, multicast routing, transmission scheduling, and data aggregation, mathematically formulated as a complex Integer Linear Programming (ILP) model. To tackle the scalability issue, we develop a Nash equilibrium-based Multicast and Scheduling Game (MSGame) approach, reducing CPU runtime by an average of 98.02% compared to ILP. Comprehensive simulations show improved solution quality and approximate optimal solutions with fewer iterations.

This study presents a large-scale empirical analysis of real-life Internet-of-Things (IoT) malware by conducting a comprehensive analysis of 160,000 malicious executables detected by specialized IoT honeypots over five years. Our findings contribute to improving the knowledge of IoT malware characteristics and inter-relationships, which in return, contribute towards strengthening cybersecurity measures for IoT threat detection/mitigation. To achieve these goals, we leverage various malware analysis techniques to extract useful information from the executable files. Our analysis demonstrate that in contrast to non-IoT malware, we were able to extract unsolicited IP addresses and command strings from the majority of the analyzed IoT malware binaries using off-the-shelf de-obfuscation techniques/tools. Additionally, by correlating the extracted information and performing consequent similarity analysis using NLP-based features, we were able to reveal closely related samples with shared implementation across the adversarial infrastructure. Thus, contributing to labeling previously unseen/unknown IoT malware samples while uncovering emerging, possibly new variants. Finally, given such findings, we discuss the applications of a real-time IoT honeypot, which enables capturing real-time commands from malware-infected IoT devices while enabling timely and effective IoT-malware detection, analysis, labeling, and mitigation.

Network Slices (NSs) are virtual networks operating over a shared physical infrastructure, each designed to meet specific application requirements while maintaining consistent Quality of Service (QoS). In Fifth Generation (5G) networks, User Equipment (UE) can connect to and seamlessly switch between multiple NSs to access diverse services. However, this flexibility, known as Inter-Slice Switching (ISS), introduces a potential vulnerability that can be exploited to launch Distributed Slice Mobility (DSM) attacks, a form of Distributed Denial of Service (DDoS) attack. To secure 5G networks and their NSs against DSM attacks, we present in this work, PUL-Inter-Slice Defender; an anomaly detection solution that leverages Positive Unlabeled Learning (PUL) and incorporates a combination of Long Short-Term Memory Autoencoders and K-Means clustering. PUL-Inter-Slice Defender leverages the Third Generation Partnership Project (3GPP) key performance indicators and performance measurement counters as features for its machine learning models to detect DSM attack variants while maintaining robustness in the presence of contaminated training data. When evaluated on data collected from our 5G testbed based on the open-source free 5GC and UERANSIM, a UE/ Radio Access Network (RAN) simulator; PUL-Inter-Slice Defender achieved F1-scores exceeding 98.50% on training datasets with 10% to 40% attack contamination, consistently outperforming its counterpart Inter-Slice Defender and other PUL based solutions combining One-Class Support Vector Machine (OCSVM) with Random Forest and XGBoost.

The deployment of electric vehicles (EVs) requires the integration of information and communication technologies, making power grids prone to cyber threats from EV cyber-infrastructure. On this basis, this paper studies the impact of a new family of EV-based load-altering attacks (EV-LAA) against the subsynchronous stability of the power grid. First, the cyber-physical connections between the EV ecosystem and the power grid are discussed to represent a threat model for coordinated electric vehicle switching attacks (EVSAs) that can excite torsional modes of the system. Then, it will be demonstrated that a traditional proportional-integral (PI)-based subsynchronous resonance damping controller (SSRDC) cannot stabilize the power grid. With the help of a customized unknown input observer (UIO), an adaptive control framework is developed based on a model predictive control (MPC). This framework can generate online control signals and add them to the internal control framework of the synchronous generators (SGs). A modified IEEE Second Benchmark (M-IEEE-SBM) is used to demonstrate the EV-LAAs' consequences and evaluate the effectiveness of the developed adaptive technique. The proposed strategy is also studied through real-time simulations under a testbed that integrates a virtual sphere (vSphere) for an EV ecosystem with power grids simulated in a real-time simulator (i.e., OPAL-RT 5650). To demonstrate the feasibility of this switching attack vector in an actual power system and its impact on SSR stability, the Palo Verde Nuclear Generating Station (PVNGS) is also simulated in this real-time simulator, and the effectiveness of the proposed adaptive control framework is validated under the EV-LAAs.

Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments wherein establishing an optical fiber infrastructure is challenging and costly. However, FSO-based QNs require a clear line-of-sight (LoS) between users, which is challenging due to blockages and natural obstacles. In this paper, a reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual LoS between users for entanglement distribution. A novel modeling of the quantum noise and losses experienced by quantum states over FSO channels defined by atmospheric losses, turbulence, and pointing errors is derived. Then, the joint optimization of entanglement distribution and RIS placement problem is formulated, under heterogeneous entanglement rate and fidelity constraints. This problem is solved using a simulated annealing metaheuristic algorithm. Simulation results show that the proposed framework effectively meets the minimum fidelity requirements of all users’ quantum applications. This is in stark contrast to baseline algorithms that lead to a drop of at least 84% in users’ end-to-end fidelities. The proposed framework also achieves a 63% enhancement in the fairness level between users compared to baseline rate maximizing frameworks. Finally, the weather conditions, e.g., rain, are observed to have a more significant effect than pointing errors and turbulence.

Integrated sensing and communication (ISAC) has the potential to facilitate coordination gains from mutual assistance between sensing and communication (S&C), especially sensing-aided communication enhancement (SACE). Reconfigurable intelligent surface (RIS) is another potential technique for achieving resource-efficient communication enhancement. Therefore, this paper proposes an innovative RIS-assisted SACE (R-SACE) mechanism with the goal of improving the systemic communication performance of the ISAC system in practical scenarios where the channel status information (CSI) is imperfectly known. In the proposed R-SACE mechanism, a dual-functional base station (BS) provides downlink communication services to both the communication user and the dynamically changing target that is detected using the communication signals. RIS assists in both sensing and communications of the BS. A typical scenario is investigated in which either or both the direct and RIS-assisted reflected communication links are available depending on sensing results. The average systemic throughput (AST) over the entire timeline of the R-SACE mechanism is maximized by jointly optimizing both temporal and spatial resources under the probabilistic constraint and the sensing performance, transmission power, and communication interference constraints. The non-convex probabilistic mixed optimization problem is transformed and then solved by the proposed fixed-point iterative (FPI) algorithm. Simulation results demonstrate that the proposed FPI algorithm and R-SACE mechanism outperform the baseline algorithms and communication enhancement mechanisms in achieving higher systemic communication performance.

In this paper, we investigate a novel integrated sensing and communication (ISAC) system aided by movable antennas (MAs). A bistatic radar system, in which the base station (BS) is configured with MAs, is integrated into a multi-user multiple-input-single-output (MU-MISO) system. Flexible beamforming is studied by jointly optimizing the antenna coefficients and the antenna positions. Compared to conventional fixed-position antennas (FPAs), MAs provide a new degree of freedom (DoF) in beamforming to reconfigure the field response, and further improve the received signal quality for both wireless communication and sensing. We propose a communication rate and sensing mutual information (MI) maximization problem by flexible beamforming optimization. The complex fractional objective function with logarithms are first transformed with the fractional programming (FP) framework. Then, we propose an efficient algorithm to address the non-convex problem with coupled variables by alternatively solving four sub-problems. We derive the closed-form expression to update the antenna coefficients by Karush-Kuhn-Tucker (KKT) conditions. To improve the direct gradient ascent (DGA) scheme in updating the positions of the antennas, a 3-stage search-based projected GA (SPGA) method is proposed. Simulation results show that MAs significantly enhance the overall performance of the ISAC system, achieving 59.8% performance gain compared to conventional ISAC system enabled by FPAs. Meanwhile, the proposed SPGA-based method has remarkable performance improvement compared the DGA method in antenna position optimization.

The Electric Vehicle (EV) charging infrastructure has been rapidly expanding to keep up with the increased demands of EV consumers. This government-backed infrastructure expansion resulted in the rushed integration of a significant number of insecure EV Charging Stations (EVCS), which are vulnerable to cyber-attacks. Motivated by the uncovered vulnerabilities in different components of the EV charging infrastructure, in this paper, we study the security of the EVCS Cloud Management System (CMS). Specifically, we focus on the (in)security of the Open Charge Point Protocol (OCPP) backend communication with the EVCS. We verified the prevalence of such security weaknesses by discovering 6 zero-day vulnerabilities in each of the 16 representative live EV charging management systems. Our findings highlight the insecurity of the OCPP backend, which is widely deployed on existing EVCSs in the wild. Indeed, we discuss various attack scenarios that lead to man-in-the-middle, denial of service, firmware theft, and data poisoning, to name a few. We also leverage the developed testbed to demonstrate the feasibility of launching switching attacks against the power grid using compromised EVCSs. Finally, we contribute to the security of the EV charging ecosystem by also recommending countermeasures to mitigate/prevent future cyber-attacks.

We explore in this paper a scenario involving UAV-assisted NOMA, where the UAV serves a dual purpose by providing communication and sensing capabilities, thus supporting ISAC technology. To this end, we formulate an optimization problem aimed at minimizing the Cramér-Rao Bound (CRB) for target localization, with the goal of jointly determining the beamforming vectors at both the base station (BS) and the UAV, as well as the UAV’s position, while maintaining the communication quality of service (QoS) for the users. Given the complex interdependencies between variables and the stochastic nature of the environment due to channel variations, we adopt a deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning (RL) approach suited for continuous state and action spaces. Our numerical results demonstrate the system’s advantages over the conventional NOMA approach and underscore the algorithm’s accuracy in achieving near-optimal solutions.

This research introduces an innovative routing protocol designed to operate within the switch data plane, capitalizing on the unique advantages offered by Expander-based next-generation Data Center Networks (DCNs). Expander graphs, the foundational structures of Expander-based data centers, are renowned for their high edge expansion, which translates to shorter average path lengths and heightened connectivity among all network nodes. Traditional routing methods, such as Equal-Cost Multi-Path (ECMP) and Shortest Path (SP), are not adequate to fully harness the abundant path diversity intrinsic to these DC topologies. Hence, there exists a compelling demand for an efficient routing protocol capable of realizing the full potential of this path diversity. Furthermore, the advent of programmable switches in modern network technology underscores the need for routing techniques that can be seamlessly implemented within the data plane to achieve line-rate packet processing speed.

This paper examines the downlink scenario where a transmitting base station (BS) provides communication services to a set of users by utilizing the rate-splitting multiple access (RSMA), while concurrently providing sensing functionalities. Owing to the available transmit power of the cellular users and their capabilities of decoding the RSMA common stream, we propose to leverage the users in the network to assist the sensing process by collectively forming a probing beam towards the target(s). Using this proposed system and to evaluate its potential gains, we formulate an optimization problem to jointly determine the beamforming design at the transmitting BS, the common stream split, and the distributed beamforming design at the users as well as at the receiving BS aiming to maximize the minimum rate of the users. Due to the non-convexity posed by the formulated problem, we perform rigorous mathematical operations and leverage the semi-definite relaxation (SDR) method to solve it using a successive convex approximation (SCA) algorithm. Our numerical results demonstrate the advantage of exploiting users' resources to assist in the sensing process which is reflected in an enhancement in the achieved rate by the users. Moreover, we present the advantage of our model in comparison to Spatial Division Multiple Access (SDMA) scheme.

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, a cell-center user (CCU), and a cell-edge user (CEU) located at the edge of adjacent cells. Through JT-CoMP, all BSs collaborate to simultaneously transmit the data to all users including the CCUs and CEU. To enhance the signal quality for the CEU, CCUs relay the common stream to the CEU by operating in half-duplex (HD) relaying mode. We aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCU s, and the time slot fraction aiming to maximize the minimum achievable data rate. The formulated problem is non-convex and challenging to solve directly. To address this, we employ change-of-variables, first-order Taylor approximations and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show improvements of 12% and 41 % over RSMA and HD C-NOMA, respectively in high channel disparity between the BS and UEs.

The evolution of IoT malware has ignited interest in the creation of malware family classification models. Nonetheless, these models encounter security concerns stemming from issues related to their interpretability and vulnerabilities exposed within the training pipeline. Recent research highlighted the limitations of learning-based malware classifiers, which are susceptible to backdoor attacks due to relying on human-engineered features to simplify the mapping from features to binary perturbations. In contrast, our study aligns with the current trajectory of the malware classification field, where we emphasize the detection of backdoor attacks targeted at models employing features extracted from within the model itself. To thoroughly assess model vulner-abilities, we have devised a dynamic trigger generation method based on sample features, which we refer to as “BENIGN”. This approach is used to contaminate and launch attacks on the model while also implementing a tailored training process to achieve specific attack objectives. Through experiments, we analyze the impact of variables involved in its training procedures on the attack stability and success rates. Last, we evaluate mitigation methods and emphasize the challenges and adaptability needed to defend against these attack strategies.

Electric Vehicles (EV) have experienced a tremendous rise in popularity as they offer a sustainable alternative to conventional vehicles. However, the EV ecosystem is a complex system consisting of many interconnected components such as the EV Charging Station (CS) and the EV Charging Station Management System (CSMS). Given its connection to the smart grid and its direct impact on the transportation sector, securing the EV ecosystem is essential and requires the design of novel monitoring solutions. Previous studies proposed single-component detection mechanisms that cannot detect all potential anomalies across the system. Our work addresses this issue through the combination and correlation of monitoring data collected from the different EV ecosystem components. Our objective is to develop a real-time monitoring platform for attack detection in the public EV charging ecosystem that is based on the extension of the IEC 62351-7:2017 Network and System Management (NSM) standard. By adopting an international security standard, we ensure the monitoring platform is compatible with international power systems. To validate the utility of the approach, we integrate the monitoring framework with a real-time EV charging cosimulation testbed and discuss how it can be used to detect EV-based cyberattacks.

This paper introduces a 2-layer cooperative rate-splitting multiple access (C-RSMA) framework designed for multiple groups of two users. Within each user group, the message is divided into three components: an inter-group common message, an inner-group common message, and a private message. Our framework incorporates a novel user-pairing policy, leveraging a combination of semi-orthogonal user selection (SUS) and a matching-game (MG)-based algorithm to identify user pairs, which allows for selecting the cell-center-users (CCUs) and cell-edge-users (CEUs) for each pair. To enhance signal quality at the CEUs, we employ cooperative communication, where each CCU relays the inner-group common message to its paired CEU. This framework is formulated as an optimization problem by jointly optimizing user pairing, beamforming vectors at the base station (BS), common stream split, time slot duration, and transmit power of CCUs to maximize the network sum rate. The formulated problem is highly non-convex and difficult to solve, and hence, we adopt bi-level optimization which breaks the original problem into outer and inner problems. The outer problem is considered as the user pairing problem and we solve it using the SUS-MG algorithm. Once the users are paired, we solve the inner optimization problem for each pair using a successive convex approximation (SCA) approach. Finally, numerical results demonstrate that our proposed approach can outperform 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.

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.

Data center networks have been gaining a lot of attention in recent years. These networks are scaling up quickly with the explosive nature of current applications. Lately, a lot of efforts have been exerted to improve the performance of these networks compared to the often performance-lagging standard Clos-based topologies. One of the approaches for performance improvement is to use alternative data center network topologies. Consequently, researchers explored topologies based on Expander Graphs (EGs), such as Jellyfish, Xpander, and STRAT, where they exploited the sparse and incremental nature of these new topologies. This paper investigates the STructured Re-Arranged Topology (STRAT) as a potentially robust and efficient design for next-generation data centers. Robustness and throughput metrics are adopted to benchmark the performance of STRAT against the well-known Expander architectures, which show better performance than that of present topologies, (e.g., Fat-Tree, BCube). This paper shows that STRAT has better structural properties than Jellyfish and Xpander, making it more robust to switch and link failures. Specifically, STRAT possesses lower average shortest path length and diameter, higher spectral gap, and higher algebraic connectivity. These exceptional properties allow STRAT to achieve better throughput. Such observations are validated through extensive flow and packet level simulations, demonstrating STRAT’s superior performance in terms of the flow completion time as compared to other Expanders.

This work targets enhancing the quality of charging experience in Electric Vehicle (EV) Public Charging Infrastructure (PCI) networks. The estimation uncertainty of waiting times at charging stations (CSs) hinders the proliferation of such networks and, hence, decelerates EV adoption. Currently, most EV owners prefer to use private chargers; thus, overloading the energy distribution network leaving PCIs under-utilized. Consequently, it becomes important for PCI operators to provide customers with accurate waiting time estimates at various CSs; therefore, allowing them to make more informed CS selections. The per-CS EV waiting times reveal possible CS overloads, which, when frequently repetitive, indicate the need for PCI up-scaling to satisfy increasing demands; hence, ensuring elevated customer QoE. This paper leverages recent real-world data to unveil the statistical properties of EV charging times that, unlike existing studies, are found to be best captured by an Erlang-${k}$distribution. Also, the per-CS charging request arrival processes are characterized under various scheduling policies. It is established hereafter that CSs can be accurately modelled as single-server queuing systems. Finally, extensive simulations are conducted to verify the accuracy of the proposed models and provide further insights into the waiting time performance achieved by each of the adopted scheduling policies.

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective surfaces (IRS) have recently emerged as a technology capable of enhancing the quality of the signals in the radio access network, where passive reflecting elements can be tuned to improve the uplink or downlink signals. Harnessing the IRS’s potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices’ energy consumption considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a suboptimal solution based on alternating optimization where we divide the problem into sub-problems which are then solved separately. Specifically, the offloading decision is solved through a matching game algorithm, and then the IRS phase shifts and resource allocation optimizations are solved in an alternating fashion using the Difference of Convex approach. The obtained results demonstrate the gains both in energy and network resources and highlight the IRS’s influence on the design of the MEC parameters.

This paper investigates the potential of integrating reconfigurable intelligent surface (RIS) and cooperative non-orthogonal multiple access (C-NOMA) in preserving the freshness of information in real-time Internet of Things (IoT) applications. The system model comprises one base stations (BS), one RIS, and two IoT devices (IoTDs), in an uplink setting, where the IoTD with poor channel quality is assisted by the RIS and by the IoTD with the strong quality through a full duplex (FD) device-to-device (D2D) communication. 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 and the RIS phase shift matrix, which is non-convex and is hard to solve directly. In order to resolve this issue, the formulated optimization problem is divided into a power control sub-problem and a RIS configuration sub-problem. Capitalizing on that, a closed-form solution has been derived for the power control sub-problem and the RIS configuration sub-problem is solved by resorting to difference-of-convex (DC) along with successive convex approximation (SCA). The simulation results demonstrate that the proposed RIS-empowered uplink C-NOMA scheme achieves higher AoI-reduction compared to all considered baseline schemes.

In this paper, we consider a wireless network consisting of a base station that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information for time-critical applications. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the reconfigurable intelligent surface phase-shift matrix with the goal of minimizing the long-term average Age of Information. The formulated optimization problem is a mixed integer non-convex optimization problem, which is difficult to solve. To circumvent the high-coupled optimization variables, and with the aid of bi-level optimization, we decompose the original problem into an outer traffic stream scheduling problem and an inner reconfigurable intelligent surface phase-shift matrix problem. For the outer problem, owing to its complexity and stochastic nature of packet arrivals, we resort to deep reinforcement learning solution where the traffic stream scheduling is modeled as a Markov Decision Process, and Proximal Policy Optimization is invoked to solve it. Whereas, the inner problem that determines the reconfigurable intelligent surface configuration is solved through semi-definite relaxation. Finally, we show through extensive simulations that our approach evaluates the combined impact of scheduling policy and reconfigurable intelligent surface configuration on the long term average Age of Information, where we demonstrate its superiority against other baseline schemes.

Electric Vehicle Charging Stations (EVCSs) have been shown to be susceptible to remote exploitation due to manufacturer-induced vulnerabilities, demonstrated by recent attacks on this ecosystem. What is more alarming is that compromising these high-wattage IoT systems can be leveraged to perform coordinated oscillatory load attacks against the power grid which could lead to the instability of this critical infrastructure. In this paper, we investigate a previously sidelined aspect of EVCS security. We analyze the deployment security of EVCSs and highlight operator-induced vulnerabilities rendering the ecosystem exposed to remote intrusions. We create an advanced discovery technique that leverages Web interface artifacts to dynamically discover new charging station vendors. As a result, we uncover 33,320 charging station management systems in the wild. Consequently, we study the deployment security of the charging stations and identify that 28,046 EVCSs were found to be vulnerable to eavesdropping, and around 24% of the studied EVCSs are deployed with default configurations exposing the ecosystem to a Mirai-like attack vector. Aligned with this finding, we discover that the EVCS ecosystem has been targeted by nefarious IoT malware such as Mirai and its variants. This demonstrates that further security measures should be implemented by vendors and operators to ensure the security of this vital ecosystem. Consequently, we provide a comprehensive recommendation for securing the deployment of EVCSs.

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.

This paper examines the integrated sensing and communication technology (ISAC) in the downlink scenario where a base station exploits cooperative non-orthogonal multiple access (CNOMA) to jointly offer communication functions to users and sensing functions to targets. CNOMA allows the user with good channel conditions to assist another user with a weak channel using the decode and forward strategy in full duplex mode while forming a beam-pattern that is capable of sensing the targets. The main objective in this work is to maximize the sum rate of the users by jointly optimizing the communication beamformers and the power allocation of the near user subject to the quality of service requirements for sensing and communication functions. The formulated problem is non-convex and hard to solve using traditional solvers. For that reason, a penalty-based approach is adopted to provide an efficient solution. Numerical results demonstrated the advantage of C-NOMA in ISAC, showing gains reaching up to 38% compared to the traditional NOMA, and 65% compared to the spatial division multiple access (SDMA).

This paper studies the downlink scenario of an unmanned aerial vehicle (UAV)-assisted rate-splitting multiple access (RSMA). The UAV serves as a full duplex (FD) amplify-and-forward relay to assist the base station (BS) in its communication with a set of user equipments (UEs). In this framework, we formulate an optimization problem with the goal of maximizing the minimum achievable rate by jointly optimizing the BS precoding vectors, the common-stream split, UAV transmit power, and the UAV location subject to the power budget constraints of the BS and UAV. Due to the non-convex nature of the problem, we propose an alternating optimization algorithm that decomposes the main problem into a power allocation subproblem and a UAV location subproblem, which are solved in an alternative way. Both subproblems are solved using a successive convex approximation approach. Our numerical results show that the proposed model outperforms traditional RSMA, non-orthogonal multiple access (NOMA), and UAV-assisted NOMA, demonstrating the efficacy of our approach in achieving higher minimum achievable rates.

This paper proposes a reconfigurable intelligent surface (RIS)-assisted cooperative rate-splitting multiple access (C-RSMA) framework with simultaneous wireless information and power transfer (SWIPT). In the proposed framework, the user with good channel gain can act as a full-duplex (FD) relay to forward the common stream to the user with poor channel gain. Moreover, by leveraging SWIPT technology, the user with good channel gain can simultaneously receive information and harvest energy from the base station (BS). This framework is formulated as an optimization problem by jointly optimizing beamforming vectors at the BS, common stream split, power splitting factor, and phase shift configuration at the RIS with the objective of maximizing the sum rate of both users. To tackle this challenging problem, an alternating optimization algorithm based on the successive convex approximation and difference-of-convex approach is proposed. Numerical results demonstrate that our proposed approach can outperform the baseline schemes.

Ransomware, an extortion type of malware, continues to create havoc targeting critical infrastructure and organizations at large, causing an estimated $20 Billion in direct and collateral damages in 2022. While significant efforts from both academia and industry are being pledged to address this debilitating and disrupting phenomena, the ransomware pandemic continues to expand rapidly in frequency, spread and stealthiness. To this end, in this work, we propose RPM, a Ransomware Prevention and Mitigation scheme. RPM is rooted in the proactive analysis of operating systems' API artifacts through the exploitation of a neat observation related to ransomware behavior, namely, activities generated prior to the actual execution of the malicious payloads. RPM employs OS-centric process hooking tactics to develop an offensive approach leveraging such sensing activities. To demonstrate the effectiveness of RPM, we empirically evaluated it using 100 of the most prominent ransomware samples. The results demonstrate very motivating accuracy metrics with low system footprint, asserting the rationale of the proposed scheme. We posture RPM as a strong step towards proactive mitigation, which aims at complimenting ongoing ransomware thwarting efforts.

In line with the global push towards smart cities, the world is increasingly adopting Electric Vehicles (EVs). This increased EV proliferation is putting the Public Charging Infrastructure (PCI) under a large strain. To this end, this work presents a data-driven analysis of the Quality of Service (QoS) on the current EV PCI. This work presents a comprehensive set of metrics that are developed to evaluate the QoS at the current PCI in Quebec, Canada. The analysis is performed on a real dataset covering 5 full years of over 7,000 EV Charging Stations (EVCSs) in Quebec. This data is then used to create a forecast model for predicting future EV charging requests and assessing their impact on the QOS at the current PCI deployment levels. The developed metrics and forecast model are used to recommend new EVCS deployment sites to guarantee acceptable QoS levels in the future based on the current trends in EV adoption.

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.

The adoption rate of EVs has witnessed a significant increase in recent years driven by multiple factors, chief among which is the increased flexibility and ease of access to charging infrastructure. To improve user experience and increase system flexibility, mobile applications have been incorporated into the EV charging ecosystem. EV charging mobile applications allow consumers to remotely trigger actions on charging stations and use functionalities such as start/stop charging sessions, pay for usage, and locate charging stations, to name a few. In this article, we study the security posture of the EV charging ecosystem against a new type of remote that exploits vulnerabilities in the EV charging mobile applications as an attack surface. We leverage a combination of static and dynamic analysis techniques to analyze the security of widely used EV charging mobile applications. Our analysis was performed on 31 of the most widely used mobile applications including their interactions with various components such as cloud management systems. The attack scenarios that exploit these vulnerabilities were verified on a real-time co-simulation test bed. Our discoveries indicate the lack of user/vehicle verification and improper authorization for critical functions, which allow adversaries to remotely hijack charging sessions and launch attacks against the connected critical infrastructure. The attacks were demonstrated using the EVCS mobile applications showing the feasibility and the applicability of our attacks. Indeed, we discuss specific remote attack scenarios and their impact on EV users. More importantly, our analysis results demonstrate the feasibility of leveraging existing vulnerabilities across various EV charging mobile applications to perform wide-scale coordinated remote charging/discharging attacks against the connected critical infrastructure (e.g., power grid), with significant economical and operational implications. Finally, we propose countermeasures to secure the infrastructure and impede adversaries from performing reconnaissance and launching remote attacks using compromised accounts.

This Special Section features research contributions in the area of robust and reliable networks of the future. Modern network infrastructures must support a growing demand for intensive data processing and high-speed communication, that has led, in the last decade, to a constant evolution towards convergence of networking and computing infrastructures. This convergence was made possible by the introduction of network function virtualization and by the emergence of the Software-Defined Networking (SDN) paradigm, and has enabled new forms of cloud and edge computing to cope with the strict requirements of new services and applications, as those in the realm of the Internet of Things (IoT).

Recent years have witnessed the emergence of new and more sophisticated malware targeting the Internet of Things. Moreover, the public release of the source code of popular malware families such as Mirai has spawned diverse variants, making it harder to disambiguate their ownership, lineage, and correct label. Such a rapidly evolving landscape makes it also harder to deploy and generalize effective learning models against retired, updated, and/or new threat campaigns. In this paper, we present EVOLIoT, a novel approach aiming at combating "concept drift" and the limitations of inter-family IoT malware classification by detecting drifting IoT malware families and understanding their diverse evolutionary trajectories. We introduce a robust and effective contrastive method that learns and compares semantically meaningful representations of IoT malware binaries and codes without the need for expensive target labels. We find that the evolution of IoT binaries can be used as an augmentation strategy to learn effective representations to contrast (dis)similar variant pairs. We discuss the impact and findings of our analysis and present several evaluation studies to highlight the tangled relationships of IoT malware, as well as the efficiency of our contrastively learned feature vectors in preserving semantics and reducing out-of-vocabulary size in cross-architecture IoT malware binaries.

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.

Seeing the poor wireless conditions caused by obstacles and deep fading that often face the access network, intelligent reflective surfaces (IRS) have been recently studied for enhancing the quality of the wireless signals using a set of passive reflecting elements. Due to the channel quality issue severely impacting the performance of edge computation offloading, the IRS technology can be applied to enhance the edge offloading performance, especially for devices with strict requirements. In this paper, we study the optimized use of the IRS along with the design of the offloading and resource allocation parameters for maximizing the UEs’ sum of offloaded bits, considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a sub-optimal solution based on the alternating optimization technique. The offloading decision is solved through a customized matching game algorithm, and then the IRS phase shifts and resources allocation are optimized through in alternating fashion using the Difference of Convex approach. Finally, numerical results demonstrate the improvement in the offloading performance provided by the optimized use of the IRSs, and highlights on the IRSs’ influence on the design of the MEC parameters.

Non-orthogonal multiple access (NOMA) is one of the promising access technologies to improve spectral efficiency and serve a higher number of users simultaneously. The latter proves important in time-sensitive services when data has to be collected before a set deadline, otherwise, the data is rendered useless. Therefore, in this paper, we utilize a NOMA-aided unmanned aerial vehicle (UAV) for data collection from time-constrained IoT devices. We optimize the trajectory of the UAV, IoT devices scheduling, and power allocation to maximize the number of served devices while considering the constraints of UAV energy and flight duration, and NOMA clustering. Given the complexity of the problem and the incomplete knowledge about the environment, it is divided into two subproblems. In the first subproblem, the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot are modeled as a Markov Decision Process, and Proximal Policy Optimization is used to solve it. For the second device selection, a heuristic algorithm is used based on prioritizing devices with higher bit rate requirements and strict deadlines. The second subproblem considers power allocation inside the NOMA cluster, where it is formulated as an optimization problem for maximizing the sum rates of the two selected users. Finally, we demonstrate the performance gains of our solution in different scenarios while varying the system parameters as compared with alternative approaches. In particular, our proposed solution achieves a 10% to 30% performance gain compared to the traditional orthogonal multiple access scheme.

In this paper, we consider a wireless network consisting of a base station (BS) that is serving multiple real-time traffic streams forwarding information updates to their destinations in order to sustain the freshness of information. Since the wireless channels may be unreliable due to the impurities of the propagation environments, such as deep fading, blockages, etc., we integrate a reconfigurable intelligent surface (RIS) to the wireless system in order to mitigate the propagation-induced impairments, enhance the quality of the wireless links, and ensure that the required freshness of information is achieved for these real time applications. For this network set-up, we investigate the joint optimization of the traffic streams scheduling and the RIS phase-shift matrix with the goal of minimizing the sum Age of Information (AoI). In order to solve this optimization problem, we propose an efficient algorithm based on a change-of-variables with semi-definite relaxation (SDR). Finally, we perform extensive simulations to verify the effectiveness of our proposed method against other baseline schemes.

Cooperative-Autonomous-Driving (CAD) systems stringently require that vehicle status information (e.g, speed, position, etc) be timely disseminated for safety reasons. CAD systems rely on real-time information to make critical decisions; hence, the paramount criticality of temporally valid information generation and dissemination. However, the timely information update messages’ delivery faces numerous challenges due to the highly alternating wireless signal propagation in vehicular environments as a result of, for instance, shadowing and blockage, which lead to the unavailability of reliable communication links between cooperating vehicles. Under such harsh conditions, Reconfigurable-Intelligent-Surfaces (RISs) have been proven to highly contribute in mitigating the propagation-induced impairments of the wireless environments and, hence, promoting more robust communication links, which, in turn, allow for maintaining the required freshness of information. In the above-context, this paper revolves around the minimization of the Age-of-Information (AoI) perceived by each of a CAD system’s destination node. The problem is formulated as an Integer-Linear-Program (ILP), which turns out to be quite complex. To work around this complexity, it is proposed herein to use decomposition based on the Lagrangian relaxation method, which largely facilitates the problem’s resolution following typical dynamic programming methodologies. Consequently a feasible solution is extracted using a relatively simple heuristic. An analytical framework is established to reveal insights into the proposed solution and gauge its merits through the establishment of a thorough simulation framework involving various scenarios aiming at verifying its correctness, validity and superiority as compared to other solutions derived using the state-of-the-art branch-and-cut method implemented by CPLEX.

Internet of Things (IoT) applications, such as augmented/virtual reality, tactile Internet, immersive gaming, etc., are currently experiencing an unprecedented growth in their demand. IoT devices are constrained by limited computation and power features and might experience excessive computational latency to support resource-intensive tasks. Multiaccess edge computing (MEC) appears to be a promising solution in this regard to expedite the computations of resource-intensive tasks by offloading them to the edge of the network. This article considers a scenario where a base station (BS) serves traffic streams from multiple IoT devices. The packets from each stream arrive at the BS (following a stochastic process) and then forwarded to their respective destinations after they are processed by the MEC node. The scheduling decisions are aimed to keep the information fresh at the destination. The information freshness is captured by Age of Information (AoI) metric. We aim to minimize the expected sum AoI for the MEC-assisted IoT network and provide mathematically traceable expressions for the AoI. First, an optimization problem is formulated to find the optimal scheduling policy in order to minimize the expected sum AoI. The optimization problem is an integer linear programming (LP) problem, which is generally difficult to solve. Hence, we provide a simpler formulation of the problem and derive a more traceable expression for the expected sum AoI. With this approach, the joint impact of stochastic arrivals, scheduling policy, and unreliable channel conditions on the AoI is assessed. We also propose low-complexity algorithms to obtain results for larger networks. Finally, through extensive simulations, we demonstrate the effectiveness of our proposed methods as compared to other existing strategies in terms of achievable AoI.

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

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

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.

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

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

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

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.

The analysis of recent large-scale cyber attacks, which leveraged insecure Internet of Things (IoT) devices to perform malicious activities on the Internet, highlighted the rise of IoT-tailored malware/botnets. These malware propagate by scanning the Internet for vulnerable, exploitable IoT devices that could be utilized for further malicious activities. In this article, we devise a multi-level methodology to investigate Internet-scale reconnaissance activities generated by infected IoT devices. We leverage theShodanIoT search engine and over 6TB of passive network traffic from a large network telescope (darknet) to infer compromised IoT devices and characterize the generated scanning campaigns. The results highlight a distinctive characteristic of IoT malware/botnets, represented by the targeted ports/services over the analysis interval. Furthermore, while these ports/services are mainly associated with well-known IoT malware/botnets (e.g.,MiraiandSatori), we uncovered newly targeted ports, which indicate emerging IoT malware/botnet. Finally, by comparing two instances of analyzed IoT-generated scanning campaigns, we highlight the persistence and evolution of IoT malware/botnets (e.g.,ADB.MinerandFbot), which exploit existing, and in some cases, possibly new vulnerabilities.

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

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

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

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

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

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.

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

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.

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 aimed for future sixth generation (6G) wireless networks. In the LiFi physical layer, the majority of the power allocation problems for mobile users investigated and reported in the literature are non-convex. These problems may be solved using dual decomposition techniques or heuristics that require iterative algorithms, and often, cannot be computed in real time due to the high computational load. In this paper, a proactive power allocation (PPA) approach that can alleviate the aforementioned issues is proposed. The core of the PPA approach is two cascaded neural networks consisting of one convolution neural network (CNN) and one long-short-term-memory (LSTM) network that are jointly capable of predicting posterior positions and orientations of mobile users following random trajectories in indoor environments. Afterwards, the predicted parameters are fed into the expression of the channel coefficients of the mobile users. Finally, the resulting predicted channel coefficients are exploited for deriving near-optimal power allocation schemes prior to the intended service time, which enables near-optimal and real-time service for mobile LiFi users.

This paper proposes a novel framework to emerge the reconfigurable intelligent surface (RIS) in cellular networks wherein enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services coexist. In order to avoid the violation in the URLLC latency requirements, the framework proposes RIS phase shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. The system model consists of a single base station (BS) and a single RIS which deployed to enhance the channel environments of the eMBB and the URLLC users. To allocates the eMBB users, we formulate a time-slot basis eMBB allocation problem which has the goal of 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. Since the formulated problem is a non-convex problem which hard to be solved directly, we adopt the alternating optimization approach to decompose the eMBB allocation problem optimization problem into a power allocation and a RIS phase shift matrix sub-problems. Then, 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 by jointly optimizing the power and frequency allocation. Then, we proposed a heuristic algorithm to allocate the URLLC load. The proposed algorithm has a low time complexity which makes it a efficient method for multiplexing URLLC and eMBB traffics. Finally, simulation results show that 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 rates.

The work presented in this article aims at improving the ground vehicle connectivity in the context of an intermittent vehicle-to-UAV (V2U) communication scenario where vehicles opportunistically establish time-limited connectivity with passing by unmanned aerial vehicles (UAVs) serving as flying base stations responsible for routing incoming vehicle data over backbone networks and/or the Internet. As opposed to existing work in the literature where vehicles are only allowed to establish direct connectivity with in-range UAVs, this work aims at also exploiting the possible formation of vehicular clusters and, hence, the feasibility of intervehicular communications to establish multihop paths connecting source vehicles to destination UAVs. A mathematical model is presented for the purpose of capturing the nodal (i.e., vehicles and UAVs) mobility dynamics and derive an expression for the overall V2U connectivity probability as well as the overall average vehicle connection time. Extensive simulations are conducted in order to adduce the validity and accuracy of the proposed model and provide further insights into the connectivity sensibility to fundamental system parameters.

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.

The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

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.

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.

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

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

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

The emergence of Internet of Things malware, which leverages exploited IoT devices to perform large-scale cyber attacks (e.g., Mirai botnet), is considered as a major threat to the Internet ecosystem. To mitigate such threat, there is an utmost need for effective IoT malware classification and family attribution, which provide essential steps towards initiating attack mitigation/prevention countermeasures. In this paper, motivated by the lack of sophisticated malware obfuscation in the implementation of IoT malware, we utilize features extracted from strings- and image-based representations of the executable binaries to propose a novel multi-dimensional classification approach using Deep Learning (DL) architectures. To this end, we analyze more than 70,000 recently detected IoT malware samples. Our in-depth experiments with four prominent IoT malware families highlight the significant accuracy of the approach (99.78%), which outperforms conventional single-level classifiers. Additionally, we utilize our IoT-tailored approach for labeling newly detected “unknown” malware samples, which were mainly attributed to a few predominant families. Finally, this work contributes to the security of future networks (e.g., 5G) through the implementation of effective tools/techniques for timely IoT malware classification, and attack mitigation.

Telecommunication networks are currently realizing more-huge-than-ever data demands from subscribers all over the world. Due to the ongoing pandemic, nearly all businesses have adapted working models with remote operations. People engaged with major industries, e.g., academia, health and municipalities are utilizing online platforms to carryout their routine tasks. This indeed shifts the attention from one-to-one (unicast) communication to one-to-many (multicast) and many-to-many (multi-source multi-destination) communications. Network operators are facing increased pressure to provide quick responses in order to satisfy the bandwidth hungry and time sensitive user demands. This can only be done by enhancing deployability as well as manageability of the services. Network Function Virtualization (NFV) provides a transformation of traditional proprietary network designs to a more agile and software based environment in order to achieve flexible deployments, reduced setup costs and less-time-to-market for the new services which is very much needed in the current scenarios. Previous studies on NFV-enabled multicast problem either proposed Integer Linear Program (ILP) models, that are pretty unscalable, or heuristic-based techniques that do not guarantee good quality of the solutions obtained. In this article, we propose an NFV multicast resource optimization model exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements. Herein, we propose a novel Dantzig-Wolfe (DW) decomposition model that tackles the complexity of the problem by breaking it down into a master problem and several pricing problems. We compare the DW approach with the ILP and heuristic methods and demonstrate that our approach achieves near to optimal solution (in comparison to heuristic based methods) much faster than ILP. We also study the dynamic admission of NFV-enabled multicast requests by solving the problem in an online manner using the batch processing of requests. We then evaluate the performance of the proposed algorithms through extensive simulations and demonstrate that proposed algorithms are promising and outperform existing solutions.

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.

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.

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles' computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks' weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit's range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig-Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solved optimally with a polynomial-time dynamic programming approach. Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of our approach compared to branch and bound and greedy algorithms.

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.

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 (INR) 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.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in downlink communication systems. Using C-NOMA, users with more favorable channel conditions can assist communication between the base station (BS) and the users with less favorable channel conditions using either full-duplex (FD) or half-duplex (HD) device-to-device (D2D) relaying and successive interference cancellation (SIC). To maximize the benefits of C-NOMA, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme in a downlink cellular system consisting of a BS that communicates with a set of spatially dispersed users. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) which is difficult to solve due to the dependency between power control and user pairing. Thus, we decompose the problem into an inner power control problem and an outer pairing problem. For the inner problem, we derive the optimal closed-form expressions for both HD and FD relaying modes, while the outer problem of user pairing can be solved using the well-known Hungarian method. The 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 negligible computational time.

The precision time protocol (PTP) is considered as one of the most favorable mechanisms for providing unified and precise time at the substation level in the smart grid. Nevertheless, PTP was shown to be vulnerable to cyber-attacks targeting its components and synchronization services. In this article, we capitalize on the theory and outcome of our previous work to contribute a more complete solution that addresses PTP cyber security. We propose to close the PTP loop through an extension that introduces new functionality and messages. This extension covers the PTP attack surface and enables the detection of attacks on PTP time synchronization. We formally model and verify the proposed extension using UPPAAL model checker. In addition, we validate the proposed extension using Omnet++ simulation. The evaluation demonstrates that our approach preserves PTP functionality, while successfully detecting cyber attacks against PTP components in a timely manner.

Network Function Virtualization (NFV) converts network functions executed by costly middleboxes into instances of Virtual Network Functions (VNFs) hosted by industry-standard Physical Machines (PMs). This has proven to be quite an efficient approach when it comes to enabling automated network operations and the elastic provisioning of resources to support heterogeneous services. Today's revolutionary services impose a remarkably elevated reliability together with ultra-low latency requirements. Therefore, in addition to having highly reliable VNFs, these VNFs have to be optimally placed in such a way to rapidly route traffic among them with the least utilization of bandwidth. Hence, the proper selection of PMs to meet the above-mentioned reliability and delay requirements becomes a remarkably challenging problem. None of the existing publications addressing such a problem concurrently adopts VNF decomposition to enhance the flexibility of the VNFs' placement and a hybrid routing scheme to achieve an optimal trade-off between the above-mentioned objectives. In this paper, a VNF-decomposition-based backup strategy is proposed together with a delay-aware hybrid multipath routing scheme for enhancing the reliability of NFV-enabled network services while jointly reducing delays these services experience. The problem is formulated as a Mixed Integer Linear Program (MILP) whose resolution yields an optimal VNF placement and traffic routing policy. Next, the delay-aware hybrid shortest path-based heuristic algorithm is proposed to work around the MILP's complexity. Thorough numerical analysis and simulations are conducted to validate the proposed algorithm and evaluate its performance. Results show that the proposed algorithm outperforms its existing counterparts by 7.53% in terms of computing resource consumption.

We study in this paper the secrecy performance of the multiple-input multiple-output (MIMO) visible light communication (VLC) wiretap channel. The underlying system model comprises three nodes: one transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver and one eavesdropper, each equipped with multiple photo-diodes (PDs). The VLC channel is modeled as a real-valued amplitude-constrained Gaussian channel and the eavesdropper is assumed to be randomly located in the coverage area. We propose a low-complexity precoding scheme that aims at enhancing the secrecy performance of the system. Specifically, assuming discrete input signaling, we derive an average achievable secrecy rate for the underlying system in a closed-form, and the derived expression is a function of the precoding matrix and the input distribution using stochastic geometry. Then, we propose a low-complexity design of the precoding matrix based on the generalized singular value decomposition (GSVD) of the channel matrices of the system. We examine the resulting average achievable secrecy rate using the truncated discrete generalized normal (TDGN) distribution, which is the best-known discrete distribution available in the literature. Finally, we validate the proposed scheme through extensive simulations and we demonstrate its superiority when compared to other schemes reported in the literature.

This paper investigates the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications. Precisely, a V2U communication scenario is considered where vehicles opportunistically establish connectivity with passing by UAVs for a limited period of time during which these vehicles transmit data packets to in-range UAVs serving as flying base stations and, in turn, are responsible for delivering these packets to backbone networks and/or routing them over the Internet. A mathematical framework is established with the objective of modeling the vehicles' OnBoard Units' (OBUs') buffers as single-server queueing systems. The established queueing model will allow for the evaluation of the V2U communication system in terms of the average data packet delivery delay. Extensive simulations are conducted with the objective of asserting the validity and accuracy of the proposed queueing model as well as providing further insights into the delay sensibility to various system parameters.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in cellular downlink systems. The system model consists of a base station (BS) that needs to serve multiple users within a region of service. A subset of the users, especially those located close to the cell edge, undergo severe fading and suffer from poor channel quality and low achievable rates. To overcome this problem, CNOMA is proposed as the system design methodology, in which users that have the capability of full-duplex (FD) communication can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To harness both the multiplexing gain from NOMA and the diversity gain from FD-D2D communications, we formulate and solve a novel optimization problem that jointly determines D2D user pairing and power allocation. The formulated problem is a mixed-integer non-linear program (MINLP) with prohibitively high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. Our simulation results show that with reasonable assumptions, the proposed scheme always outperforms some existing schemes in the literature, and that, under undesirable conditions, e.g., poor D2D channel conditions or imperfect self-interference (SI) cancellation, the proposed scheme is reduced to conventional NOMA.

The Fifth Generation (5G) era is bringing tremendous new network capabilities enabling diverse services belonging to different business verticals (i.e., manufacturing, automotive, etc.) and provided with top-notch Quality of Service (QoS) (i.e., ultra-low latency, ultra-reliability, etc.). Empowered by soft-warization technologies such as Network Function Virtualization (NFV), 5G networks are envisioned to be agile, sustainable and self-organized. NFV promotes the automated provisioning of Network Services (NSs) through processing their traffic by a chain of Virtual Network Functions (VNFs). As VNFs are shared between multiple NSs, a clear approach to map and schedule the carried traffic of these services is required. Hence, in this paper, we solve the Latency-Aware Service Schedule Orchestration problem (LASSO) that jointly addresses the mapping and scheduling of services to VNFs. We formulate the problem as a Mixed Integer Linear Program (MILP) and we present ENCHAIN, a novel game-theoretic approach exploiting a scalable solution for the LASSO problem while providing each NS the freedom to decide on its own mapping and scheduling solution.

In this paper, we study the downlink transmission in a multi-UAV enabled wireless communication system where different types of contents are requested by ground users. Particularly, in the system, we consider a number of UAVs each equipped with a cache that is refreshed during off-peak hour to store some contents to be requested by ground users. To harness their full potential, a novel cooperative communication for the cache-enabled UAVs is presented, and we formulate an optimization problem that jointly decides the UAV placement as well as the transmit beamforming to maximize the number of users admitted to the system. Since the formulated problem is a mixed-integer non-convex program, which is generally known to be NP-hard, we resort to an appealing framework developed from the conventional difference-of-convex (DC) programming. Numerical results reveal the superiority of employing cooperative UAVs over non-cooperative ones and offer some insights into how the flexible deployment of cooperative UAVs benefits the system performance.

By pushing computation to the mobile network edge, Multi-access Edge Computing (MEC) has been an enabler for the stringent latency and energy requirements of the new Internet of Things (IoT) services. On the other hand, ultra-dense heterogeneous networks with wireless backhaul have been proposed as a low-cost solution, allowing Network Operators (NOs) to extend the network capability, by deploying densified close-proximity small-cells and hence supporting a large number of low-latency low-energy IoT devices. In this paper, we study the problem of IoT task offloading in a MEC-enabled heterogeneous network, which to the best of our knowledge, is the first attempt to thoroughly explore the task offloading problem in a heterogeneous network with MEC support and wireless backhaul. We jointly optimize the offloading decision, transmission power, and the allocation of radio and computational resources, with the objective of minimizing the devices energy consumption, while respecting their latency deadline. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method for providing an approximate solution on the original problem. Through numerical analysis, we perform simulations based on multiple scenarios, and find out how NOs can respond to the requested load and help in minimizing the total devices energy consumption.

In recent years, providing connectivity to fast-moving vehicles on highways has been the focus of the wireless research community. In this paper, in the context of V2X, we propose using unmanned aerial vehicles (UAVs) to serve vehicles on a highway, where a UAV is dispatched in disaster situations (such as floods or earthquakes) to serve these vehicles, or to provide better coverage when vehicles are out of reach of road side units. We consider free flow scenario where vehicles moving between two road-side units and where the infrastructure is partially or totally unavailable. Our goal is to guarantee a certain Quality of Service (QoS) for each vehicle on the highway by jointly optimizing the UAV trajectory and the radio resource allocation. We show that during the UAV flight time, the UAV adapts its velocity to the velocities of the vehicles in the served cluster, to maximize the minimum average rate for each vehicle. Our findings are verified through Monte-Carlo simulation where we demonstrate the effectiveness of our proposed design under different UAVs types.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets. Among the emerging markets, Internet of Things (IoT) use cases are standing out with the proliferation of a wide range of sensors that can be configured to continuously monitor and transmit data for intelligent processing and decision making. Devices in such scenarios are normally extremely energy-constrained and often exist in large numbers and can be located in hard-to-reach areas; the fact that necessitates the design and implementation of effective energy-aware data collection mechanisms. To this end, we propose the utilization of unmanned aerial vehicles (UAVs) to collect data in dense wireless sensor networks using projection-based compressive data gathering (CDG) as a novel solution methodology. CDG is utilized to aggregate data en-route from a large set of sensor nodes to selected projection nodes acting as cluster heads (CHs) in order to reduce the number of needed transmissions leading to notable energy savings and extended network lifetime. The UAV transfers the gathered data from the CHs to a remote sink node, e.g., a 5G cellular base station, which avoids the need for long range transmissions or multihop communications among the sensors. Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected CH nodes based on projection-based CDG with minimized UAV trajectory distance. We formulate a joint optimization problem and divide it into four complementary subproblems to generate close-to-optimal results with lower complexity. Moreover, we propose a set of effective algorithms to generate solutions for relatively large-scale network scenarios. We demonstrate the superiority of the proposed approach and the designed algorithms via detailed performance results with analysis, comparisons, and insights.

Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.

Among the challenges of realizing the full potential of light-fidelity (LiFi) cellular networks are user mobility, random device orientation, and blockage. In this paper, we study the impact of those challenges on the performance of LiFi networks in an indoor environment using measurement-based channel models, unlike existing studies that rely on theoretical channel models. In our paper, we adopt spatial modulation (SM) and consider two configurations for the user equipment (TIE). A multidirectional receiver (MDR) structure is proposed, in which the PDs are located on different sides of the TIE, e.g., a smartphone. This configuration is motivated by the fact that conventional structures exhibit poor performance in the presence of random device orientation and blockage. In fact, we show that the MDR outperforms the benchmark structure by over 10 dB at bit-error ratio (BER) of 3.8 × 10-3. Moreover, an adaptive access point (AP) selection scheme for the SM is considered, where the number of APs is 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 entire room is evaluated for sitting and walking activities, for which the orientation-based random waypoint (ORWP) mobility model is invoked. Furthermore, we demonstrate that the proposed adaptive technique with SM outperforms the conventional spatial multiplexing system. We also study the performance of the underlying system on the uplink channel where we apply the same techniques used for the downlink channel. It is shown analytically that the multidirectional transmitter (MDT) with adaptive SM is highly energy efficient.

This paper addresses the problems of data sanitization and cyber situational awareness by analyzing 910 GB of real Internet-scale traffic, which has been passively collected by monitoring close to 16.5 million darknet IP addresses from a /8 and a /13 network telescopes. First, the paper offers a novel probabilistic darknet preprocessing model, which aims at sanitizing darknet data to prepare it for effective use in the task of cyber threat intelligence generation. Such model has been engineered using a distributed multithreaded approach, rendering it operational and highly effective on darknet big data. Second, the paper further contributes by presenting an innovative approach to infer large-scale orchestrated probing campaigns by leveraging darknet data, for Internet cyber situational awareness. The approach uniquely reduces the dimensionality of such big data by utilizing its artifacts, instead of processing the actual raw data. This is accomplished by extracting and analyzing probing time series using formal methods rooted in Fourier transform and Kalman filtering. Thorough empirical evaluations indeed validate the accuracy and the performance of the proposed methods and techniques. We assert that the darknet sanitization model and the probing orchestration inference approach are of significant value, given their postulated highly applicable nature to the field of Internet measurements for cyber security in the era of big data.

Middleboxes have gained popularity due to the significant value-added services these network elements provide to traffic flows, in terms of enhanced performance and security. Policy-aware traffic flows usually need to traverse multiple middleboxes in a predefined order to satisfy their associated policy, also known as Service Function Chaining. Typically, Middleboxes run on specialized hardware, which make them highly inflexible to handle the unpredictable and fluctuating-nature of traffic, and contribute to significant capital and operational expenditures (Cap-ex and Op-ex) to provision, accommodate, and maintain them. Network Function Virtualization is a promising technology with the potential to tackle the aforementioned limitations of hardware middleboxes. Yet, NFV is still in its infancy, and there exists several technical challenges that need to be addressed, among which, the Virtual Network Function assignment problem tops the list. The VNF assignment problem stems from the newly gained flexibility in instantiating VNFs (on-demand) anywhere in the network. Subsequently, network providers must decide on the optimal placement of VNF instances which maximizes the number of admitted policy-aware traffic flows across their network. Existing work consists of Integer Linear Program (ILP) models, which are fairly unscalable, or heuristic-based approaches with no guarantee on the quality of the obtained solutions. This work proposes a novel Logic-Based Benders Decomposition (LBBD) based approach to solve the VNF assignment problem. It consists of decomposing the problem into two subproblems: a master and a subproblem; and at every iteration constructive Benders cuts are introduced to the master to tighten its search space. We compared the LBBD approach against the ILP and a heuristic method, and we show that our approach achieves the optimal solution (as opposed to heuristic-based methods) 700 times faster than the ILP.

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.

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.

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.

Driven by the expeditious evolution of the Internet of Things, the conventional vehicular ad hoc networks will progress toward the Internet of Vehicles (IoV). With the rapid development of computation and communication technologies, IoV promises huge commercial interest and research value, thereby attracting a large number of companies and researchers. In an effort to satisfy the driver's well-being and demand for continuous connectivity in the IoV era, this paper addresses both safety and quality-of-service (QoS) concerns in a green, balanced, connected, and efficient vehicular network. Using the recent advances in training deep neural networks, we exploit the deep reinforcement learning model, namely deep Q-network, which learns a scheduling policy from high-dimensional inputs corresponding to the current characteristics of the underlying model. The realized policy serves to extend the lifetime of the battery-powered vehicular network while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform several scheduling benchmarks in terms of completed request percentage (10-25%), mean request delay (10-15%), and total network lifetime (5-65%).

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices' energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users' tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

Recent attacks have highlighted the insecurity of the Internet of Things (IoT) paradigm by demonstrating the impacts of leveraging Internet-scale compromised IoT devices. In this paper, we address the lack of IoT-specific empirical data by drawing upon more than 5TB of passive measurements. We devise data-driven methodologies to infer compromised IoT devices and those targeted by denial of service attacks. We perform large-scale characterization analysis of their traffic, as well as explore a public threat repository and an in-house malware database, to underlie their malicious activities. The results expose a significant 26 thousand compromised IoT devices "in the wild," with 40% being active in critical infrastructure. More importantly, we uncover new, previously unreported malware variants that specifically target IoT devices. Our empirical results render a first attempt to highlight the large-scale insecurity of the IoT paradigm, while alarming about the rise of new generations of IoT-centric malware-orchestrated botnets.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets spanning a wide range of use cases. Among these, massive IoT is standing out with the proliferation of sensors and wearable devices that continuously monitor and transmit data for further processing. This paper proposes a novel data collection technique using Unmanned Aerial Vehicles (UAVs) in dense wireless sensor networks (WSNs) using projection-based Compressive Data Gathering (CDG) as a solution methodology. CDG is utilized to aggregate data en route from sets of sensor nodes to a set of projection nodes (heads) in order to notably reduce the number of transmissions leading to energy savings and extended WSN lifetime. The UAVs forward the gathered data from heads to a remote sink to enhance efficiency by avoiding long range transmissions from heads to the sink or multi-hop communications among sensors to the sink. We formulate a joint optimization problem that captures clustering, heads selection, routing trees construction, and UAV trajectory planning. In order to overcome the complexity of the joint optimization problem, we decompose the problem into separate parts and propose a heuristic to solve each subproblem for large-scale network scenarios.

This paper proposes a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA) to improve the performance of wireless backhaul two-tier heterogeneous networks. Our work's novelty lies in the formulation to solve for the NOMA decoding order along with the cooperation rule between downlink small cell transmissions. We first employ the cochannel time division duplexing (CoTDD) combined with spectrum partitioning to manage interference. Then, we propose an optimization problem which maximizes the total achievable rate by jointly designing the NOMA decoding order and small cell cooperation rule along with the beamformer at the macro base station and small cells. The formulated problem is a mixed integer non-convex one which is generally NP-hard. To attain the solution, we exploit the structure of difference of convex functions to rewrite the formulated binary variables and then to equivalently transform the optimization problem into more amenable form. Finally, we develop an iterative lowcomplexity algorithm based on successive convex approximation (SCA) principle, which is provable to eventually converge at a sub-optimal solution. Numerical results show that our proposed strategy outperforms the conventional designs in terms of total achievable rate.

We study in this paper the achievable secrecy rate of the Gaussian multiple-input single-output (MISO) visible light communication (VLC) in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) equipped with a single photo-diode (PD) and a group of randomly located eavesdroppers, each equipped with a single PD. The channel is modeled as deterministic, real- valued and subject to an amplitude constraint, through which Alice wants to communicate privately with Bob. We consider the case where the eavesdroppers are colluding together, i.e., they act jointly in eavesdropping on the communication between Alice and Bob. We adopt the truncated generalized normal (TGN) as input signaling and beamforming as transmission strategy. At first, we derive a closed-form expression of the average achievable secrecy rate as a function of the density of eavesdroppers using stochastic geometry. Then, we investigate the optimal beamformer that maximizes the average achievable secrecy rate of the system. Finally, the analysis is verified by Monte Carlo simulations.

We study in this paper the secrecy performance of the multiple-input single-output (MISO) visible light communication (VLC) channel in the presence of randomly located eavesdroppers. We consider a system model comprising a transmitter, equipped with multiple fixtures of LEDs, one legitimate receiver equipped with a single photo-diode (PD) and a group of randomly located (and colluding) eavesdroppers, each equipped with a single PD. We derive a closed-form expression for the average achievable secrecy rate as a function of the beamforming vector, the input distribution, and the location density of the eavesdroppers using stochastic geometry. We then investigate the optimal beamformer that maximizes the average achievable secrecy rate. We corroborate the analytical results through Monte Carlo simulations and we demonstrate substantial improvements provided by the proposed scheme over existing ones.

Given the inability of Mobile Cloud Computing (MCC) to guarantee the requirements of the delay-sensitive applications, Mobile Edge Computing (MEC) has been proposed to drastically reduce that latency. But since edge servers suffer from limited capabilities that offset the latency benefits in periods of high load, a hierarchical edge cloud architecture has been studied as a way to mitigate that problem. However, such model incurs different computational costs that depend on the cloudlet layer. In this paper, we jointly minimize the mobile devices' energy consumption and computational cost in a multilayered MEC, by optimizing their transmission power and the assigned server computation while respecting their latency threshold. We mathematically formulate the mixed integer non-convex program and propose an efficient algorithm based on Successive Convex Approximation (SCA) method to solve and obtain a high-quality solution. Through numerical results, we analyze different scenarios, and show the efficiency of our algorithm in providing an approximate solution that efficiently decreases the total energy consumption and computational cost.

In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macro cell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs' positions.

Network Function Virtualization (NFV) has revolutionized service provisioning in cloud datacenter networks. It enables the complete decoupling of Network Functions (NFs) from the physical hardware middle boxes that network operators deploy for implementing service-specific and strictly ordered NF chains. Precisely, NFV allows for dispatching NFs as instances of plain software called virtual network functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. Nevertheless, NF softwarization introduces processing vulnerability (e.g., failures caused by hardware or software, and so on). Since any failure of VNFs could break down an entire service chain, thus interrupting the service, the functionality of an NFV-enabled network will require a higher reliability compared with traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF chain placement and flow routing optimization. In order to guarantee the required reliability, an incremental approach is proposed to determine the number of required VNF backups. Through illustration, it is shown herein that the formulated single path routing model can be easily extended to support resource sharing between adjacent backup VNF instances. This paper advocates the absolute existence of a share-resource-based VNF assignment strategy that is capable of trading off all of the reliability, bandwidth, and computing resources consumption of a given service chain. A heuristic is proposed to work around the complexity of the presently formulated integer linear programming (ILP). Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of this proposed heuristic reflecting its ability to achieve very close results to those obtained through the resolution of the complex ILP within a negligible amount of time. Above and beyond, the proposed resource-sharing-based VNF placement scheme outperforms existing resource-sharing agnostic schemes by 15. 6% and 14.7% in terms of bandwidth and CPU utilization respectively.

User request (UR) service scheduling is a process that significantly impacts the performance of a cloud data center. This is especially true since essential quality-of-service (QoS) performance metrics such as the UR blocking probability as well as the data center's response time are tightly coupled to such a process. This paper revolves around the proposal of a novel Deadline-Aware UR Scheduling Scheme (DASS) that has the objective of improving the data center's QoS performance in term of the above-mentioned metrics. A minority of existing work in the literature targets the formulation of mathematical models for the purpose of characterizing a cloud data center's performance. As a contribution to covering this gap, this paper presents an analytical model, which is developed for the purpose of capturing the system's dynamics and evaluating its performance when operating under DASS. The model's results' accuracy are verified through simulations. Also, the performance of the data center achieved under DASS is compared to its counterpart achieved under the more generic First-In-First-Out (FIFO) scheme. The reported results indicate that DASS outperforms FIFO by 11 to 58 percent in terms of the blocking probability and by 82 to 89 percent in terms of the system's response time.

Non-orthogonal multiple access (NOMA) has been proposed as an alternative to orthogonal multiple access (OMA) in an effort to enhance the spectral efficiency of 5G cellular systems. However, the NOMA throughput gain relative to that of OMA has been shown to be modest. In this paper, we propose a novel NOMA scheme that exploits the partial overlap (i.e., similarity) among users bit sequences at the base station (BS). Specifically, users bit sequences are divided into blocks of short lengths, i.e., short bit sequences. Then, one user is selected and served during a given transmission time interval (TTI). Users whose bit sequences partially overlap with the bit sequence of the served user are also (partially) served during the same TTI. At the receiving end, the receiver corresponding to the served user recovers its entire bit sequence, whereas the partially served users recover their corresponding overlapping bit blocks and ignore the rest of the sequence. The performance of the proposed scheme is analyzed in terms of the overall throughput. We show that a throughput gain of up to three times that of existing OMA schemes can be achieved. Moreover, we show that the average rate per user decreases slightly as the number of users increases, whereas it linearly decreases with the number of users in existing NOMA schemes. We stress here that the proposed scheme completely differs from existing NOMA schemes as the latter schemes are based on power allocation at the BS where successive interference cancellation is normally used. The implication of this is that the proposed scheme provides substantial throughput gains without causing interference among users and without adopting a specific power allocation at the BS.

Cooperative relaying is often deployed to enhance the communication reliability (i.e., diversity order) and consequently the end-to-end achievable rate. However, this raises several security concerns when the relays are untrusted, since they may have access to the relayed message. In this paper, we study the achievable secrecy diversity order of cooperative networks with untrusted relays. In particular, we consider a network with an N-antenna transmitter (Alice), K single-antenna relays, and a single-antenna destination (Bob). We consider the general scenario, where there is no relation between N and K, and therefore, K can be larger than N. Alice and Bob are assumed to be far away from each other, and all communication is done through the relays, i.e., there is no direct link. Providing secure communication while enhancing the diversity order has been shown to be very challenging. In fact, it has been shown in the literature that the maximum achievable secrecy diversity order for the adopted system model is one (while using artificial noise jamming). In this paper, we adopt a nonlinear interference alignment scheme that we have proposed recently to transmit the signals from Alice to Bob. We analyze the proposed scheme in terms of the achievable secrecy rate and secrecy diversity order. Assuming Gaussian inputs, we derive an explicit expression for the achievable secrecy rate and show analytically that a secrecy diversity order of up to min(N, K) - 1 can be achieved using the proposed technique. We provide several numerical examples to validate the obtained analytical results and demonstrate the superiority of the proposed technique to its counterparts that exist in the literature.

In this paper, we consider a novel model of two-tier wireless backhaul (WB) small cell networks where each small cell access point is equipped with a buffer of finite storage. We employ a time-spectrum transmission accommodation scheme to separate the WB and access transmissions on top of developing a joint optimized transmit beamforming and power allocation algorithm. Unlike previous work, we propose a more advanced buffering protocol to study the small cell performance improvement by solving the offline and online optimization problems that maximize the total small cell access rate. Both kind of problems are generally non-convex and NP-hard. The offline scheme assumes the availability of channel state information (CSI) in current and future time slots and is used as a benchmark for online algorithms. To solve it, we develop a low-complexity algorithm which iteratively solves a sequence of lower bounded convex approximated problems until convergence. The formulated online problems must be solved in a slot-by-slot manner since transmitters only know the CSI of the current time slot. Then, we also develop low-complexity online algorithms to jointly design the transmit beamforming and power allocation. Our theoretical and numerical simulation results show that our proposed model with advanced buffering strategy outperforms the conventional designs in terms of small cell access rate for both offline and online algorithms.

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.

Critical infrastructure, such as the smart grid, is vulnerable to failures and attacks. The complex nature of these systems embeds hidden vulnerabilities that threaten their functionality when exploited. In this paper, we perform a vulnerability analysis of the smart grid based on the power flow dynamics and in the presence of the essential communication network. Our analysis identifies a small number of power lines and communication links that can trigger a cascading failure and result in a blackout when removed. We quantify the failure effect in the form of fractional loss in the served load. Moreover, we formulate a mathematical model to present both components of the smart grid and their interdependency. A scalable algorithm is introduced to analyze the output of the model. We evaluate the proposed model and algorithm on the IEEE 14, 30, 57, and 300 Bus systems and associated communication networks, and report on the collected results.

The evolution in microgrid technologies as well as the integration of electric vehicles (EVs), energy storage systems (ESSs), and renewable energy sources will all play a significant role in balancing the planned generation of electricity and its real-time use. We propose a real-time decentralized demand-side management (RDCDSM) to adjust the real-time residential load to follow a preplanned day-ahead energy generation by the microgrid, based on predicted customers' aggregate load. A deviation from the predicted demand at the time of consumption is assumed to result in additional cost or penalty inflicted on the deviated customers. To develop our system, we formulate a game with mixed strategy which in the first phase (i.e., prediction phase) allows each customer to process the day ahead raw predicted demand to reduce the anticipated electricity cost by generating a flattened curve for its forecasted future demand. Then, in the second stage (i.e., allocation phase), customers play another game with mixed strategy to mitigate the deviation between the instantaneous real-time consumption and the day-ahead predicted one. To achieve this, customers exploit renewable energy and ESSs and decide optimal strategies for their charging/discharging, taking into account their operational constraints. RDCDSM will help the microgrid operator to better deal with uncertainties in the system through better planning its day-ahead electricity generation and purchase, thus increasing the quality of power delivery to the customer. We evaluate the performance of our method against a centralized allocation and an existing decentralized EV charge control noncooperative game method both of which rely on a day ahead demand prediction without any refinement. We run simulations with various microgrid configurations, by varying the load and generated power, and compare the outcomes.

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises K +1 nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max-min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

The notion of non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information at the transmitter (CSIT). Such knowledge is used to judiciously allocate power among users to make their signals separable at their respective receivers while employing successive interference cancellation (SIC). Feeding back the CSI from the users to the BS (transmitter) is obviously bandwidth consuming. Reducing such an overhead is of great importance and has been of interest in recent years. Furthermore, existing NOMA techniques become inapplicable when the CSI is unavailable at the BS. In this case, the BS has only the option of allocating power among users blindly, including equal power splitting, which has been shown to yield poor performance in terms of outage probability and error probability. This motivates us to develop a NOMA scheme that does not require CSI knowledge at the BS. We make use of a nonlinear interference alignment technique that we have proposed recently, namely, interference dissolution, to develop the proposed NOMA scheme, which allows the BS to communicate with two users simultaneously while keeping signals perfectly separable at their respective receivers. We develop the proposed scheme for multiple-input single-output and single-input single-output downlink channels. We analyze the proposed technique analytically in terms of the achievable degrees-of-freedom and achievable rate per user. We show that the proposed NOMA scheme outperforms existing NOMA techniques in terms of the outage probability and error probability.

In this paper, we propose to re-engineer the wireless backhaul two-tier heterogeneous networks architecture by developing a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA). To effectively manage severe interference from the newly introduced backhaul communications, we employ the cochannel time division duplexing combined with spectrum partitioning between two considered tiers. This paper's novelty lies in the formulation to solve for the NOMA decoding order, which affects the rule of the cooperation between small cell transmissions. We propose two optimization problems of jointly designing the NOMA decoding order together with the transmit beamforming at the macro base station and power allocation at the small cells which maximize the total achievable rate and the number of satisfied users, respectively. The first and second formulated problems are both mixed integer non-convex and are generally NP-hard. To solve them, we first employ the difference of convex functions to present the formulated binary variables and then equivalently transform the optimization problems into more tractable forms. Finally, we develop an iterative low-complexity algorithm based on successive convex approximation technique and majorization minimization method, which is provable to eventually converge at a sub-optimal solution. Numerical results are extensively studied to corroborate that our proposed strategy outperforms the conventional designs in terms of total achievable rate and number of satisfied users.

The fifth generation (5G) of cellular networks is emerging as the key enabler of killer real-time applications, such as tactile Internet, augmented and virtual reality, tele-driving, autonomous driving, etc., providing them with the much needed ultra-reliable and ultra-low latency services. Such applications are expected to take full advantages of recent developments in the areas of cloud and edge computing, and exploit emerging industrial initiatives such as Software Defined Networks (SDN) and Network Function Virtualization (NFV). Often, these 5G applications require network functions (e.g., IDSs, load balancers, etc.) to cater for their end-to-end services. This paper focuses on chaining network functions and services for these applications, and in particular considers those delay sensitive ones. Here, we account for services with deadlines and formulate the joint problem of network function mapping, routing and scheduling mathematically and highlight its complexity. Then, we present an efficient method for solving these sub-problems sequentially and validate its performance numerically. We also propose and characterize the performance of a Tabu search-based approach that we design to solve the problem. Our numerical evaluation reveals the efficiency of our sequential method and the scalability of our Tabu-based algorithm.

We investigate in this paper the achievable secrecy sum-rate of the multiple-input single-output (MISO) visible light communication (VLC) broadcast Gaussian channel with confidential messages. We consider a system model comprising (K +1) nodes: a transmitter (Alice) equipped with N fixtures of LEDs and K (K ≤ N) spatially dispersed users, each equipped with a single photo-diode (PD). The channel is modeled as deterministic and real-valued, subject to amplitude and power constraints, through which Alice wants to transmit in one channel use K confidential messages. We propose a new precoding scheme based on the eigenvalues of the pencils of the different K MISO VLC channels in order to maximize the achievable secrecy sum-rate of the overall system. The motivation behind this scheme is based on the fact that the optimal precoding scheme maximizing the achievable secrecy rate of the single user MISO VLC wiretap channel is expressed through the eigenvector associated to the largest eigenvalue of the wiretap channel's pencil. We investigate both cases, namely, when the locations of users are known, i.e., perfect channel state information (CSI) is available to Alice, or unknown, i.e., imperfect CSI. Furthermore, we adopt the truncated generalized normal (TGN) distribution as input signaling. We present several examples which demonstrate the substantial improvements in the secrecy sum-rates achieved by the proposed techniques compared to those achieved by zero-forcing (ZF) precoding.

Power-domain non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information (CSI) at the transmitter. Feeding back the CSI from the users to the transmitter is clearly bandwidth consuming. In addition, when the CSI is unavailable at the transmitter (CSIT), NOMA techniques become inapplicable, and in such scenario, allocating power among users blindly, including equal power splitting, has been shown to yield poor probability of error performance. To this end, we develop a NOMA technique that does not require CSI knowledge at the transmitter, i.e., with unknown CSIT. The proposed technique allows the transmitter to communicate with multiple users simultaneously while keeping signals perfectly separable at their respective receivers. We apply the proposed technique to a two-user multiple-input single-output (MISO) two-user downlink channel with unknown CSI where the available degree of freedom (DoF) is one. We show that it is possible to allocate 1/2 DoF to each user, implying that the received signals can be perfectly separated at the receiver, i.e., without interference. We analyze the performance of the proposed scheme in terms of the achievable DoF per user and the symbol error rate (SER). We present numerical examples to validate the efficacy of the proposed scheme as a NOMA technique for 5G systems, and compare its performance in terms of the SER to that of existing schemes and demonstrate its superiority.

Real interference alignment is efficient in breaking-up a one-dimensional space over time-invariant channels into fractional dimensions. As such, multiple symbols can be simultaneously transmitted with fractional degrees-of-freedom (DoF). Of particular interest is when the one dimensional space is partitioned into two fractional dimensions. In such scenario, the interfering signals are confined to one sub-space and the intended signal is confined to the other sub-space. Existing real interference alignment schemes yield poor achievable rate at finite signal-to-noise ratio (SNR), which is of interest from a practical point of view. In this paper, we propose a radically novel nonlinear interference alignment technique, which we refer to as Interference Dissolution (ID). ID allows to break-up a one dimensional space into two fractional dimensions while achieving near-capacity performance for the entire SNR range. This is achieved by aligning signals by signals, as opposed to aligning signals by the channel. We introduce ID by considering a timeinvariant, point-to-point multiple-input single-output (MISO) channel. This channel has a one-dimensional space and offers one DoF. We show that, by breaking-up the one dimensional space into two sub-spaces, ID achieves a rate of two symbols per channel use while providing \ DoF for each symbol. We also propose a decoder and prove its optimality. We compare numerically the performance of ID in terms of the achievable rate performance to that of existing schemes and demonstrate ID's superiority.

In this work, we study a novel model of two-tier wireless backhaul small cell networks that considers buffering of finite storage size at each small cell access point. By employing a reverse time division duplexing (RTDD) interference management, we develop an online algorithm that jointly optimizes the transmit beamforming and power allocation. Unlike previous works, we propose a more advanced buffering protocol to improve the small cell performance by solving an online constrained optimization problem that maximizes both the total small cell access rate and backhaul rate. To deal with the non-convex property of the formulated problem, we invoke the framework of successive convex approximation to develop the online algorithm to iteratively solve a convex approximated problem and update corresponding parameters until convergence. Numerical results show that our proposed model with advanced buffering strategy outperforms the traditional designs in terms of small cell access rate.

We investigate in this paper the achievable secrecy rate of the multiple-input multiple-output (MIMO) visible light communication (VLC) Gaussian wiretap channel. We consider a system model comprising three nodes: one transmitter (Alice) equipped with multiple fixtures of LEDs, one legitimate receiver (Bob) and one eavesdropper (Eve), each equipped with multiple photo-diodes (PDs). We study at first the problem of optimal signaling scheme that maximizes the achievable secrecy rate of the MIMO VLC wiretap channel. We consider the cases where the location of Eve is known (i.e. perfect channel state information (CSI)) or unknown (i.e. imperfect CSI). Finally, we derive an upper bound on the secrecy capacity that we used to assess the closeness of the achievable secrecy rate to the derived bound.

The proper design of a vehicular network is the key expeditor for establishing an efficient Intelligent Transportation System, which enables diverse applications associated with traffic safety, traffic efficiency, and the entertainment of commuting passengers. In this paper, we address both safety and Quality-of-Service (QoS) concerns in a green Vehicle-to-Infrastructure communication scenario. Using the recent advances in training deep neural networks, we present a deep reinforcement learning model, namely deep Q-network, that learns an energy-efficient scheduling policy from high-dimensional inputs corresponding to the characteristics and requirements of vehicles residing within a RoadSide Unit's (RSU) communication range. The realized policy serves to extend the lifetime of the battery-powered RSU while promoting a safe environment that meets acceptable QoS levels. Our presented deep reinforcement learning model is found to outperform both random and greedy scheduling benchmarks.

Real-time interactive application workloads (e.g., Web search, social networking, and so on) appear in the form of a large number of mini requests and responses flowing over the datacenters' networks. They end up being sewed all together to constitute a user-requested task or computation (e.g., display a complete Facebook timeline). Applications as such strictly impose low latency flow completion, since the service's quality is decreed by quick aggregation of responses to the largest possible fraction of requests and their delivery back to the user. This paper presents a deadline-aware flow scheduling (DAFS). In addition to reducing the average flow completion time (FCT), DAFS aims at decreasing the deadline mismatch and blocking probabilities, hence improving the average application throughput. An analytical queuing model is formulated herein to capture the datacenter's network dynamics and evaluate its performance when operating under DAFS. The model is validated through extensive simulations whose results also show that DAFS outperforms existing multi-queue-based priority mechanisms by 52% in terms of the average FCT and a range of 7%-29% in terms of the average throughput.

The evolution of smart microgrid and its demand-response characteristics not only will change the paradigms of the century-old electric grid, but also will shape the electricity market. In this new market scenario, once always energy consumers, now may act as sellers due to the excess of energy generated from newly deployed renewable energy generators. In this paper, we propose an optimization scheme to minimize the electricity price with a framework for optimal trading of energy between sellers and buyers of the microgrid network (MGN). The proposed scheme is capable of solving the optimal power allocation problem for an MGN in a polynomial time without modifying the actual marginal costs of a generator. Initially, we mathematically formulate the problem as nonlinear nonconvex and later decompose the problem to separate the optimal marginal-cost model from the electricity allocation model. Then, we develop a divide-and-conquer method to minimize the electricity price by jointly solving the optimal marginal-cost model and electricity allocation problems. To evaluate the performance of the solution method, we develop and simulate the model with various cost functions and compare it with a first come first serve electricity allocation method and distributed energy trading for multiple microgrids.

In cloud data centers (DCs), where hosted applications share the underlying network resources, network bandwidth guarantees have shown to improve predictability of application performance and cost. However, recent empirical studies have also shown that often DC devices and links are not all that reliable and that failures may cause service outages, rendering significant revenue loss for the affected tenants, as well as the cloud operator. Accordingly, cloud operators are pressed to offer both reliable and predictable performance for the hosted applications. While much work has been done on solving both problems separately, this paper seeks to develop a joint framework by which cloud operators can offer both performance and availability guarantees for the hosted tenants. In particular, this paper considers a simple model to abstract the bandwidth guarantees requirement for the tenant and presents a protection plan design which consists of backup virtual machines (VMs) placement and bandwidth provisioning to optimize the internal DC traffic. We show through solid motivational examples that finding the optimal protection plan design is highly perplexing, and encompasses several constituent challenges. Owing to its complexity, we decompose it into two subproblems, and solve them separately. First, we invoke a placement subproblem of the minimum number of backup VMs and then we explore the most efficient correspondence between backup and primary VMs (i.e., protection plan) which minimizes the bandwidth redundancy. Further, we study the design of various facets of such a plan by exploiting bandwidth sharing opportunities in multi-tenant cloud networks.

Middleboxes (i.e., firewall, cache, proxy, etc.) are hardware appliances designed to enforce security and performance policies. Being an integral part of today's cloud and enterprise networks, these middleboxes are expensive, hard to manage and to maintain. Network function virtualization has emerged as a promising technology that replaces these hardware appliances by software ones known as virtual network functions (VNFs). Unlike hardware middleboxes, VNFs can be instantiated and deployed on virtual machines running on commodity servers which ensures their flexibility, manageability, cost-efficiency, and reduce their time-to-market. However, efficiently processing services through an ordered chain of VNFs, called service function chaining (SFC), is not trivial. It requires solving three inter-related sub-problems; the network functions (NFs) mapping sub-problem, the traffic routing sub-problem and the service scheduling sub-problem. This paper first highlights the existing interplay between the three sub-problems and then presents a formulation of the SFC scheduling (SFCS) which exploits interactions between NFs mapping onto VNFs, service scheduling and traffic routing. Given the complexity of the SFCS problem, we present a novel primal-dual decomposition using column generation that solves exactly a relaxed version of the problem and can serve as a benchmark approach. We enhance our solution methodology with a diversification technique to help improve the quality of the obtained solutions. We evaluate numerically our method and show that it can attain optimal solutions substantially faster. Finally, we present several engineering insights for improving the network performance.

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.

In this paper, we study the achievable secure degrees-of-freedom (sdof) for the multiple-input singleoutput (MISO) wiretap channel with an unknown eavesdropper. It is assumed that the eavesdropper's (Eve's) channel state information (CSI) is unknown to the transmitter (Alice) and legitimate receiver (Bob). Recent studies have shown that the achievable sdof in the sense of strong secrecy is zero when Eve's number of antennas is equal to or more than Bob's number of antennas, which is the scenario considered in this paper. To this end, we propose a novel precoding technique and a coding strategy that together achieve full sdof in the sense of strong secrecy without knowing Eve's CSI and without using artificial noise. The proposed precoding method uses the CSI of the Alice-Bob channel in a nonlinear fashion, which makes the transmitted symbols undecodable at Eve. The proposed coding scheme is based on the channel resolvability concept and ensures strong secrecy. Achieving full sdof with an unknown Eve's CSI is significant, because it is contrary to what is believed about the achievable sdof for the MISO wiretap channel in the sense of strong secrecy. We also show that the proposed scheme achieves near Alice-Bob's channel capacity in the sense of strong secrecy with a probability approaching one at finite signal-to-noise ratio.

This paper studies the joint design of downlink transmit beamforming and power allocation in two-tier wireless backhaul small cell heterogeneous networks. We consider reverse time division duplexing combined with equal spectrum splitting between two tiers for interference mitigation. We formulate a constrained optimization problem with the objective of maximizing the proposed access energy efficiency, defined by the ratio of the sum achievable rate at the users to the overall consumed power, where the power consumption model includes the adaptive decoding power. The formulated problem is non-convex and generally NP-hard. To solve it, we first apply the high-complexity branch-and-bound algorithm to find the global optimal solution. Then, we develop a lower complexity algorithm which iteratively solves the convex approximated problem until convergence. Compared with the conventional methods, this algorithm converges faster to a solution that is very close to the global optimal solution achieved by the branch-and-bound approach. Finally, we exploit the framework of the alternating direction method of multipliers on the convex approximated problem to develop a distributed algorithm. Numerical results are obtained to show the improvement of our proposed model with much better power conservation compared with the different design of fixed circuit power assignment.

Network Function Visualization (NFV) enables the complete decoupling of Network Functions (NFs) (e.g., firewall, intrusion detection, routing, etc.) from physical middleboxes used to implement service-specific and strictly ordered chains of these NFs. Precisely, NFV allows for dispatching NFs as plain software instances called Virtual Network Functions (VNFs) running on virtual machines hosted by one or more industry standard physical machines. This, however, introduces vulnerabilities (e.g., hard-/soft-ware failures, etc) causing the break down of the entire VNF chain. The functionality of NFV-enabled networks impose higher reliability requirements than traditional networks. This paper encloses an in-depth investigation of a reliability-aware joint VNF placement and flow routing optimization problem. This problem is formulated as a complex Integer Linear Program (ILP). A heuristic is proposed in order to overcome this ILP's complexity. Thorough numerical analysis are conducted to verify and assert the correctness and effectiveness of the proposed heuristic.

This paper considers the problem of maximizing energy efficiency on the downlink of two-tier wireless backhaul small cell heterogeneous networks, where an interference mitigation strategy that combines reverse time division duplexing and equally orthogonal spectrum splitting is proposed. By enabling the small cell access points with the capability of switching ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection that maximizes the proposed weighted access energy efficiency metric. To better convey the total power consumption model, we assume the adaptive decoding power model at each small cell access point. The formulated problem is combinatorial and non-convex, which is NP-hard in general. Hence, to find a more realistic close-to-optimal feasible solution, we iteratively approximate the non-convex constraints in the formulated problem as second order cone ones based on the first order Taylor convex approximation and error-controlled second order cone approximation. The problem arrived at each iteration is a mixed integer second order cone programming, which can be solved optimally and efficiently by available dedicated solver to achieve the final result at convergence. Numerical results are studied to show the improvement of our proposed model compared to previous works.

We consider a model of two-tier heterogeneous cellular networks (HetNets), with wireless backhaul communication (WBC), consisting of a macrocell and a small cell tiers. A joint design of transmit beamforming, power allocation and bandwidth partitioning for both uplink (UL) and downlink (DL) transmissions is considered in this work. By proposing a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors, we formulate a constrained optimization problem with the objective of maximizing the total energy efficiency of the small cells considering both UL and DL. For this non-convex problem, we leverage the sequential parametric convex approximation (SPCA) method to develop an efficient iterative algorithm to find the local optimal solution. Numerical simulations corroborate the convergence of our proposed algorithm and their performance gains compared to the previous work.

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 became 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 network functions create more flexible and dynamic network services to meet a more diversified demand. In this paper, we study the Virtual Network Function (VNF) chaining scheduling problem with limited network resources. We consider VNF transmission and processing delays, and formulate the VNFs chaining scheduling as a new Mixed Integer Linear Programming (MILP) problem. Our objective is to minimize the latency of the overall VNFs' schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, thereby increasing operators' revenues. Owing to the complexity of the problem, we develop a Genetic Algorithm (GA) based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical results.

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each for a group of sensor nodes, to collect measurements at a single sink node. The goal is to construct such trees which gather measurements in the most energy efficient manner and minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus appropriate link scheduling is required to overcome interference. We refer to this problem as Forwarding Tree Construction and Scheduling (FTCS). Each tree may be constructed independently and then its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Further, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of best forwarding trees which, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of our work is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances where no known exact solutions can be found.

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.

This paper proposes a novel framework for managing the resource provisioning of reliable virtual networks (VN) in the cloud. This includes handling the placement of VN requests while providing availability guarantees, as well as reconfiguring/adapting their placement as their request changes over time. This is particularly interesting for services with periodic resource demands. Given the heterogeneous failure rates of physical network components, the placement and reconfiguration must ensure that the selected hosts for each VN meets its availability requirements. The existing work on availability-aware VN placement has overlooked the case of “availability over-provisioning,” as well as the fact that VN requests are subject to change over time. To this extent, we propose a novel framework that consists of two main modules; JENA: a tabu-based availability-aware resource allocation (embedding) module for VNs that achieves “just-enough” availability guarantees, and ARES: a reliable reconfiguration module to adapt the embedding of hosted services as they scale. Further, we introduce the concept of “protection-domains” and “protection-policies” to equip our proposed modules with the ability to augment services with redundant/backup nodes to enhance their reliability. Our numerical results show that our framework enhances network's admissibility (with 33% lower blocking compared to existing work), and in return increases the cloud provider's long term revenue, compared to peer and benchmark algorithms.

This paper deals with the multiple link/node substrate failures that impact a multicast virtual network (MVN) in which link recovery is not feasible and node migration is mandatory. A novel restoration approach is introduced to repair the failed MVNs while maintaining their quality of service requirements (e.g., end-to-end delay and delay variations). This approach relies on reducing the search region and exploiting nodes ranking and filtering (NRF) techniques to speed up the recovery process of finding an alternative node to which to migrate. The performance is extensively evaluated against multiple failures, with and without NRF, compared with complete re-embedding technique, link failure algorithms for single link failure, and previous work for single node failure. Simulation results prove that our recovery technique achieves good restoration ratio in considerably fast execution time, low link mapping cost (gain) with a slight impact on the admission ratio.

In a virtualized infrastructure where multiple virtual networks (or tenants) are running atop the same physical network (e.g., a data center network), a single facility node (e.g., a server) failure can bring down multiple virtual machines, disconnecting their corresponding services and leading to millions of dollars in penalty cost. To overcome losses, tenants or virtual networks can be augmented with a dedicated set of backup nodes and links provisioned with enough backup resources to assume any single facility node failure. This approach is commonly referred to as Survivable Virtual Network (SVN) design. The achievable reliability guarantee of the resultant SVN could come at the expense of lowering the substrate network utilization efficiency, and subsequently its admissibility, since the provisioned backup resources are reserved and remain idle until failures occur. Backup-sharing can replace the dedicated survivability scheme to circumvent the inconvenience of idle resources and reduce the footprints of backup resources. Indeed the problem of SVN design with backup-sharing has recurred multiple times in the literature. In most of the existing work, designing an SVN is bounded to a fixed number of backup nodes; further backup-sharing is only explored and optimized during the embedding phase. This renders the existing redesign techniques agnostic to the backup resource sharing in the substrate network, and highly dependent on the efficiency of the adopted mapping approach. In this paper, we diverge from this dogmatic approach, and introduce ProRed, a novel prognostic redesign technique that promotes the backup resource sharing at the virtual network level, prior to the embedding phase. Our numerical results prove that this redesign technique achieves lower-cost mapping solutions and greatly enhances the achievable backup sharing, boosting the overall network's admissibility.

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded or compressed measurement, and these measurements are collected at the sink for recovering the uncoded transmissions from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the realistic physical interference model. The problem of gathering tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with both minimal latency and fewer transmissions. We show the joint problem is NP-hard and owing to its complexity, we present a decentralized method for solving the tree construction and the link scheduling subproblems. Our link scheduling subproblem relies on defining an interference neighbourhood for each link and co-ordinating transmissions among network links to control the interference. We prove the correctness of our algorithmic method and analyse its performance. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

This paper considers the problem of jointly constructing and scheduling forwarding trees in a wireless sensor network, each to collect measurements from a group of sensor nodes at a single sink node. The goal is to construct such trees that gather measurements in the most energy efficient manner and with minimal gathering latency. We assume transmissions (carrying measurements) on wireless links interfere with one another, and thus, appropriate link scheduling is required to manage interference. We refer to this problem as forwarding tree construction and scheduling (FTCS). Each tree may be constructed independently, and then, its links are scheduled. However, when all trees are combined together, the shortest and energy efficient schedule may not be guaranteed. Furthermore, a large number of possible forwarding trees for each group of sensors may be considered. Both problems of enumerating forwarding trees and scheduling links for those trees are hard combinatorial problems. This is compounded by the fact that the two problems must be solved jointly, to guarantee the selection of the best forwarding trees that, when their links are scheduled, guarantee a shortest energy efficient schedule. After highlighting the complexity of the FTCS problem, we present a novel primal-dual decomposition method using column generation. We also highlight several challenges we faced when solving the decomposed problem and present efficient techniques for mitigating those challenges. One major advantage of this paper is that it can serve as a benchmark for evaluating the performance of any low complexity method for solving the FTCS problem for larger network instances, where no known exact solutions can be found.

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works.

This paper aims at inferring probing campaigns by investigating dark net traffic. The latter probing events refer to a new phenomenon of reconnaissance activities that are distinguished by their orchestration patterns. The objective is to provide a systematic methodology to infer, in a prompt manner, whether or not the perceived probing packets belong to an orchestrated campaign. Additionally, the methodology could be easily leveraged to generate network traffic signatures to facilitate capturing incoming packets as belonging to the same inferred campaign. Indeed, this would be utilized for early cyber attack warning and notification as well as for simplified analysis and tracking of such events. To realize such goals, the proposed approach models such challenging task as a problem of interpolating and predicting time series with missing values. By initially employing trigonometric interpolation and subsequently executing state space modeling in conjunction with a time-varying window algorithm, the proposed approach is able to pinpoint orchestrated probing campaigns by only monitoring few orchestrated flows. We empirically evaluate the effectiveness of the proposed model using 330 GB of real dark net data. By comparing the outcome with a previously validated work, the results indeed demonstrate the promptness and accuracy of the proposed approach.

Network virtualization enables the multi-tenancy concept where multiple tenants's services can cohabit the same substrate network and share its resources. With multi-tenancy, the problem of allocating resources to the various tenants emerges as a challenging problem. This former is commonly known as the virtual network embedding problem (VNE), which has attracted numerous effort from the research industry due to its NP-Hard nature. Yet, most of the existing work overlook the various modes of communication a virtual network (VN) can exhibit, assuming it is always a one-to-one communication between virtual machines (VMs). The recent technological advancements (such as Software Defined Networks (SDNs)) have paved the way for efficient multicast in data center networks, thereby leveraging the support of services and applications which multicast data in large volumes. While much work has been devoted for studying the problem of multicast virtual network (MVN) embedding in the cloud, little attention has been paid to investigating the impact of failure on this service class. In this paper, we study the impact of facility node failure on embedded MVNs, and introduce a novel post-failure restoration scheme to repair failed MVNs while maintaining their requested Quality of Service (QoS). Our numerical results prove that our suggested method achieves encouraging restoration ratio in considerably fast execution time.

We investigate the joint application of compressive sensing and network coding to the problem of energy efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes; each compressed data refers to a weighted aggregation of measurements from sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique based on compressive sensing is shown to reduce significantly the number of transmissions. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns which in turn can be exploited to perform network coding on the compressed data being forwarded on these trees. Such technique will further reduce the number of transmissions required to gather the measurements, and consequently result in a better network-wide energy efficiency. This paper addresses the problem of network coding aware construction of forwarding/aggregation trees and we present a mathematical model to optimally construct such trees. We also develop a decentralized method for solving the problem and we show that our method is both very scalable and accurate. We also show that when both network coding and compressive data gathering are considered jointly, modest gains may be attained.

Heterogeneous and small cell networks (HetSNets) have emerged as a promising mean to significantly improve coverage and performance of next generation cellular networks. However, the high density of base stations in such networks accentuates the harmful impact of interference on network performance. This paper considers a network of multiple small cells where the base stations seek to maximize a common objective by forming clusters and allocating power to their users. We formulate the joint clustering and power allocation problem as a mixed integer optimization problem. We show that such problems can be optimally solved only by performing an exhaustive search over all the possible clustering decisions. Furthermore, it is shown that even if the clustering is established the power allocation problem remains NP-hard. Due to the high computational complexity of the optimal solution, we propose three heuristic algorithms which perform greedy clustering and iterative power allocation. Simulations show that the proposed algorithms, and especially the neighboring links first heuristic, provide a good computational complexity/performance tradeoff.

Vehicular Ad hoc Networks have been receiving significant interest during the past years as they support both safety and non-safety applications for passengers commuting onboard smart vehicles. Vehicles may communicate with each others for the purpose of sharing information. Moreover, they may be privileged by Broadband Internet access as well as other services provisioned by stationary Roadside Units (RSUs) deployed along the roadways. When a vehicle leaves the coverage range of an RSU, it enters a dark area. However, it may still maintain connectivity with the RSU through multi-hop communication with other cooperative vehicles serving as intermediate relays. In this paper, we study the probability of establishing a connectivity path between a far away vehicle residing in a dark area and an RSU deployed along a roadway experiencing free-flow traffic conditions. For this purpose, we establish a stochastic mathematical framework which jointly considers the availability of intermediate relay vehicles as well as their ability to capture the communication channel in a contention-based MAC environment. Extensive simulations were conducted for the purpose of validating the derived expressions and examining the throughput performance of the system.

This paper revolves around the evaluation of the achievable throughput in the context of an IEEE 802.11p-based vehicular subnetwork scenario where a completely isolated source vehicle, S, desires to communicate with a distant stationary Roadside Internet Gateway (RIG), D. Multi-hop inter-vehicular communication is exploited for the purpose of establishing a path between S and D along which downstream cooperative vehicles serve intermediate packet relays. The formation of such a path is governed by the vehicular traffic behaviour as well as the per-hop contention-oriented data forwarding process. Following the formation of a continuous chain of in-range cooperative vehicles between an arbitrary source-destination pair (S,D), a stochastic analytical framework is developed with the objective of determining the probability of successful data transfer from S to D taking into account the per-hop vehicle contentions for channel access. Then, theoretical expressions for the achievable per-hop as well as the end-to-end throughput are presented. Simulations are conducted for purpose of validating the presented analysis and evaluating the considered subnetwork's performance.

Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in large scale sensor networks; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the aggregation and forwarding load across the network. With CDG, multiple forwarding trees are constructed, each for aggregating a coded measurement, and these measurements are collected at the sink for recovering the uncoded measurements from the sensors. This paper studies the problem of constructing forwarding trees for collecting and aggregating sensed data in the network under the physical interference model. The problem of aggregation tree construction and link scheduling is addressed jointly, through a mathematical formulation, and its complexity is underlined. Our objective is to collect data at the sink with minimal delays and fewer transmissions. Owing to the complexity of the joint problem, we present a decentralized method for solving the tree construction and the link scheduling sub-problems. Our link scheduling sub-problem relies on defining an interference neighbourhood for each link and coordinating transmissions among network links to control the interference. Numerical results are presented to compare the performance of the decentralized solution with the joint model as well as prior work from the literature.

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.

Network virtualization is regarded as the pillar of cloud computing, enabling the multi-tenancy concept where multiple Virtual Networks (VNs) can cohabit the same substrate network. With network virtualization, the problem of allocating resources to the various tenants, commonly known as the Virtual Network Embedding problem, emerges as a challenge. Its NP-Hard nature has drawn a lot of attention from the research community, many of which however overlooked the type of communication that a given VN may exhibit, assuming that they all exhibit a one-to-one (unicast) communication only. In this paper, we motivate the importance of characterizing the mode of communication in VN requests, and we focus our attention on the problem of embedding VNs with a one-to-many (multicast) communication mode. Throughout this paper, we highlight the unique properties of multicast VNs and its distinct Quality of Service (QoS) requirements, most notably the end-delay and delay-variation constraints for delay-sensitive multicast services. Further, we showcase the limitations of handling a multicast VN as unicast. To this extent, we formally define the VNE problem for Multicast VNs (MVNs) and prove its NP-Hard nature. We propose two novel approach to solve the Multicast VNE (MVNE) problem with end-delay and delay variation constraints: A 3-Step MVNE technique, and a Tabu-Search algorithm. We motivate the intuition behind our proposed embedding techniques, and provide a competitive analysis of our suggested approaches over multiple metrics and against other embedding heuristics.

Broadband Internet access provisioning in vehicular environments requires establishing on-the-fly connectivity between mobile vehicles and stationary Internet gateways deployed along roadways. The literature encloses various works revolving around vehicle-to-infrastructure (V2I) communication schemes designed to cater for this objective. In this paper, two novel complexity minimal MAC schemes are proposed for drive-thru Internet (DTI) access provisioning systems. The first of these schemes is called the random vehicle selection (RVS) scheme, and the second is called the least residual residence time (LRT) scheme. A mathematical framework is established with the objective of modeling a vehicle's onboard unit's buffer and evaluating its performance under RVS and LRT, in terms of several quality-of-service metrics. Extensive simulations are conducted for the purpose of verifying the proposed models' validity and accuracy.

We investigate the achievable performance gain that network coding (NC) when combined with successive interference cancellation (SIC) brings to a multihop wireless network. While SIC enables concurrent receptions from multiple transmitters, NC reduces the transmission time-slot overhead, and each of these techniques has shown independently great benefits in improving the network performance. We present a cross-layer formulation for the joint routing and scheduling problem in a wireless network with NC (with opportunistic listening) and SIC capabilities. We use the realistic signal-to-interference-plus-noise ratio (SINR) interference model. To solve this combinatorially complex nonlinear problem, we decompose it (using column generation) to two linear subproblems-namely opportunistic NC aware routing and scheduling subproblems. Our scheduling subproblem consists of activating noninterfering NC components, rather than links, which do not interfere with each other and will be used to route the traffic. We further extend our design to consider a multirate multihop wireless network with interference cancellation capabilities. We use numerical evaluation to present the achieved performance gain and compare our work to three other models: a base model with no NC and SIC, a model with only NC, and a model with only SIC capabilities. The numerical results show that our proposed method (both with and without variable transmission rate selection) achieves performance gains that range between moderate and significant for the various considered scenarios. Such improvements are attributed to the joint capabilities of SIC and NC in effectively controlling the interference and improving the spatial reuse.

This article investigates the joint application of compressive sensing (CS) and network coding (NC) to the problem of energy-efficient data gathering in wireless sensor networks. We consider the problem of optimally constructing forwarding trees to carry compressed data to projection nodes. Each compressed dataset refers to a weighted aggregation (or sum) of sensed measurements from network sensors collected at one projection node. Projection nodes then forward their received compressed data to the sink, which subsequently recovers the original measurements. This aggregation technique, based on CS, is shown to reduce significantly the number of transmissions in the network. We observe that the presence of multiple forwarding trees gives rise to many-to-many communication patterns in sensor networks that, in turn, can be exploited to perform NC on the compressed data being forwarded on these trees. Such a technique will further reduce the number of transmissions required to gather the measurements, resulting in a better network-wide energy efficiency. This article addresses the problem of NC--aware construction of forwarding/aggregation trees. We present a mathematical model to optimally construct such forwarding trees, which encourage NC operations on the compressed data. Owing to its complexity, we further develop algorithmic methods (both centralized and distributed) for solving the problem and analyze their complexities. We show that our algorithmic methods are scalable and accurate, with worst-case optimality gap not exceeding 3.96% in the studied scenarios. We also show that, when bothNC and compressive data gathering are considered jointly, performance gains (reduction in number of transmissions) of up to 30% may be attained. Finally, we show that the proposed methods distribute the workload of data gathering throughout the network nodes uniformly, resulting in extended network life times.

Network virtualization enables the multi-tenancy concept and paves the way towards more advancements and innovation in the underlying infrastructure. With network virtualization, allocating resources to Virtual Networks (VNs) that represent tenants' requests emerges as a challenging problem. This problem is commonly known as the Virtual Network Embedding (VNE) problem, and its NP-Hard nature has drawn a lot of attention from the research community. A common feature in the existing work is that the type of communication in the VN requests was never characterized, assuming that they exhibit unicast communication only. In this paper, we motivate the importance of characterizing the type of communication in VN requests. We present a formal definition of the VNE problem for VNs with multicast communication. To the best of our knowledge, the multicast VNE problem has not been addressed in the frame of cloud computing, where the location of all the virtual machines in a given multicast VN is unknown. We propose a novel 3-steps heuristic to solve the multicast VNE problem with end-delay and delay variation constraints. Our numerical results prove the efficiency of our suggested approach over multiple metrics and against numerous embedding heuristics.

This paper presents a new approach to infer malware-infected machines by solely analyzing their generated probing activities. In contrary to other adopted methods, the proposed approach does not rely on symptoms of infection to detect compromised machines. This allows the inference of malware infection at very early stages of contamination. The approach aims at detecting whether the machines are infected or not as well as pinpointing the exact malware type/family, if the machines were found to be compromised. The latter insights allow network security operators of diverse organizations, Internet service providers and backbone networks to promptly detect their clients' compromised machines in addition to effectively providing them with tailored anti-malware/patch solutions. To achieve the intended goals, the proposed approach exploits the darknet Internet space and employs statistical methods to infer large-scale probing activities. Subsequently, such activities are correlated with malware samples by leveraging fuzzy hashing and entropy based techniques. The proposed approach is empirically evaluated using 60 GB of real darknet traffic and 65 thousand real malware samples. The results concur that the rationale of exploiting probing activities for worldwide early malware infection detection is indeed very promising. Further, the results demonstrate that the extracted inferences exhibit noteworthy accuracy and can generate significant cyber security insights that could be used for effective mitigation.

Ethernet Ring Protection (ERP) switching has emerged to provide sub-50ms of restoration times, allowing Ethernet technologies to expand beyond enterprises to Next Generation Metro and Backbone networks, providing much needed services to interconnect dispersed data centers. This paper considers the problem of efficiently designing and planning an Ethernet based metro network with ERP protection method. While previous recent work has addressed such design problem, none has considered the capabilities of exploiting multiple ERP instances, leaving behind some advantages that network providers could tap into to providing their customers with desirable quality of service support. Resource planning in ERP based Ethernet networks is however a complex problem due to the challenges associated with the logical link block selection as well as ring hierarchy selection. ERP instances add however another dimension of combinatorial complexity, making the design problem completely intractable. To address this issue, we resort to large scale optimization tools and present a novel primal-dual decomposition of the original problem using column generation. We show that our method is very scalable and obtain several design insights on various representative network instances.

The performance of wireless networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals, allowing multiple concurrent transmissions. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multirate multihop wireless network. To characterise the network performance, we formulate the joint routing and scheduling problem with rate control as a mixed integer linear program (ILP) with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. We also develop one heuristic based on simulated annealing for solving efficiently the pricing subproblem. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates results in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilitities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reaping the largest benefits.

While many have advocated for the use of Virtual Local Area Networks (VLANs) as a way to provide scalable traffic management, finding the optimal traffic split (mapping) among VLANs to achieve load balancing has turned out to be a very challenging and combinatorially complex problem to solve. This paper considers the traffic engineering problem in data center networks by studying the joint problem of finding spanning trees for VLANs and optimally selecting the most promising spanning trees to map the traffic flows onto. We mathematically model this problem using Integer Linear Program (ILP) techniques and follow a primal-dual decomposition approach, using column generation, to solve exactly a relaxed mapping version of the problem, as well we present approximate solutions to the original problem. We show through numerical evaluations an outstanding scalability of the decomposed version of the problem and we use our results to study the performance of traffic engineering protocols developed in recent literature for data center networks.

This paper presents a consice yet comprehensive description of a DSA-based Vehicle-to-Infrastructure (V2I) communication system operating under spectral scarcity conditions. Existing mathematical models for such a system overlook some but essential ones of its behavioural characteristics. Thus, these models' reported performance results seem to be unrealistically overoptimistic. In this paper, a simulation study is conducted in the context of a real-life scenario. As opposed to the existing studies, the study herein aims at providing more insights into the dynamics of this type of communication systems and assessing its performance in terms of several new metrics.

This paper considers the problem of link scheduling in wireless networks with interference cancellation (IC) capabilities and under the physical SINR interference model. We first present a cross layer formulation and then use duality theory to decompose the joint design problem into congestion control and routing/scheduling subproblems, which interact through congestion prices. Given that the problem of scheduling with IC and under the SINR interference regime has been shown to be NP-complete, this paper develops a decentralized approach which allows links to coordinate their transmissions and therefore efficiently solving the link scheduling problem. We show that our decentralized algorithm achieves very close performance to other centralized methods (e.g., greedy maximal scheduling). We also study the performance gains that IC brings to wireless networks and we show that flows in the network achieve up to twice their rates in most instances, in comparisons with networks without interference cancellation capabilities. These gains are attributed to the capabilities of SIC in better managing the interference in the network and promoting higher spatial reuse among contending links.

Cloud Computing is becoming a mainstream paradigm, as organizations, large and small, begin to harness its benefits. This novel technology brings new challenges, mostly in the protocols that govern its underlying infrastructure. Traffic engineering in cloud data centers is one of these challenges that has attracted attention from the research community, particularly since the legacy protocols employed in data centers offer limited and unscalable traffic management. Many advocated for the use of VLANs as a way to provide scalable traffic management, however, finding the optimal traffic split between VLANs is the well known NP-Complete VLAN assignment problem. The size of the search space of the VLAN assignment problem is huge, even for small size networks. This paper introduce a novel decomposition approach to solve the VLAN mapping problem in cloud data centers through column generation. Column generation is an effective technique that is proven to reach optimality by exploring only a small subset of the search space. We introduce both an exact and a semi-heuristic decomposition with the objective to achieve load balancing by minimizing the maximum link load in the network. Our numerical results have shown that our approach explores less than 1% of the available search space, with an optimality gap of at most 4%. We have also compared and assessed the performance of our decomposition model and state of the art protocols in traffic engineering. This comparative analysis proves that our model attains encouraging gain over its peers.

The performance of wireless multihop networks depends on the achievable channel capacity for each transmission link as well as the level of spectrum spatial reuse in the network. For the latter one, successive interference cancellation (SIC) has emerged as an advanced PHY technique with the ability of decoding two or more overlapping signals and therefore allowing multiple concurrent transmissions. Effectively managing the transmission concurrency over the shared medium ensures good quality of transmission and therefore results in higher achievable transmission data rates. In this paper, we seek to understand the benefits of SIC and its interference management capabilities in a multi-rate multihop wireless network. To characterize the network performance under these characteristics, we follow a cross-layer design approach and formulate the joint routing and scheduling problem with rate control as a mixed integer linear program with the objective to maximize the minimum flow throughput. Given its large scale and combinatorial complexity, we follow a decomposition approach using column generation to solve the problem. However, the complexity of solving exactly the pricing subproblem limits the application of the model to very small size network instances. We develop one efficient greedy method for solving exactly the pricing subproblem as well as a simulated annealing based heuristic approach with very good performance. Our results indicate that SIC benefits strongly depend on the strength of the received signals. We show that transmission links with fixed higher data rates do not necessarily yield higher SIC gains because higher transmission rates result in sparser network topologies and thus less flexible routing. Larger networks with SIC capabilities and bitrate adaptation however are most effective in controlling the interference and improving the spatial reuse and thus reap the largest benefits with gains exceeding 20% over networks only with SIC capabilities or only with rate control.

This paper presents a concise yet comprehensive description of a multichannel vehicle-to-infrastructure communication system. Existing mathematical models for such a system overlook some of its essential behavioral characteristics such as the reneging, force termination, and, ultimately, blocking of service requests (SRs). Thus, the reported performance results obtained from these models seem to be unrealistically overoptimistic. Accordingly, in this paper, a multiserver queueing model is proposed for the purpose of accurately capturing the dynamics of the aforementioned communication system and evaluating its performance. The proposed model is renowned for its complexity and the nonexistence of closed-form analytical expressions that characterize its fundamental performance metrics. Hence, approximations were exploited as a means to enhance this model's mathematical tractability. Simulations are conducted in the context of a realistic scenario with the objective of validating the proposed approximate model, verifying its accuracy, and characterizing the system's performance in terms of several new metrics. The simulations' results indicate a cataclysmic SR blocking probability in the range of 65%-85%.

Probing is often the primary stage of an intrusion attempt that enables an attacker to remotely locate, target, and subsequently exploit vulnerable systems. This paper attempts to investigate whether the perceived traffic refers to probing activities and which exact scanning technique is being employed to perform the probing. Further, this work strives to examine probing traffic dimensions to infer the `machinery' of the scan, whether the probing activity is generated from a software tool or from a worm/bot net and whether the probing is random or follows a certain predefined pattern. Motivated by recent cyber attacks that were facilitated through probing, limited cyber security intelligence related to the mentioned inferences and the lack of accuracy that is provided by scanning detection systems, this paper presents a new approach to fingerprint probing activity. The approach leverages a number of statistical techniques, probabilistic distribution methods and observations in an attempt to understand and analyze probing activities. To prevent evasion, the approach formulates this matter as a change point detection problem that yielded motivating results. Evaluations performed using 55 GB of real dark net traffic shows that the extracted inferences exhibit promising accuracy and can generate significant insights that could be used for mitigation purposes.

In this paper, we study the performance of multi-hop wireless networks when both proper interference control and effective spatial reuse are jointly considered. For the former, we assume nodes endowed with Successive Interference Cancellation (SIC) capabilities at the physical layer and for the latter, we advocate joint link scheduling and power allocation. We study the achievable performance of such networks through a cross-layer design framework. We formulate the joint problem of routing, link scheduling and power allocation as a mixed integer non-linear program (MINLP) and we use column generation (CG) to decompose and linearize it, and hence, solve it. Our numerical results show that when SIC and power control are combined, around 75% performance improvement in dense networks may be achieved in comparison with a network that does not incorporate any advanced interference management techniques.

This paper proposes an approach that is composed of two techniques that respectively tackle the issues of detecting corporate cyber scanning and clustering distributed reconnaissance activity. The first employed technique is based on a non-attribution anomaly detection approach that focuses on what is being scanned rather than who is performing the scanning. The second technique adopts a statistical time series approach that is rendered by observing the correlation status of a traffic signal to perform the identification and clustering. To empirically validate both techniques, we experiment with two real network traffic datasets and implement two proof-of-concept environments. The first dataset comprises of unsolicited one-way telescope/darknet traffic while the second dataset has been captured in our lab through a customized setup. The results show, on one hand, that for a class C network with 250 active hosts and 5 monitored servers, the proposed detection technique's training period required a stabilization time of less than 1 second and a state memory of 80 bytes. Moreover, in comparison with Snort's sfPortscan technique, it was able to detect 4215 unique scans and yielded zero false negative. On the other hand, the proposed clustering technique is able to correctly identify and cluster the scanning machines with high accuracy even in the presence of legitimate traffic.

ABSTRACT The 4G of mobile networks will be a technology‐opportunistic and user‐centric system, combining the economical and technological advantages of various transmission technologies. As a part of its new architecture, LTE networks will implement an evolved packet core. Although this will provide various critical advantages, it will, on the other hand, expose telecom networks to serious IP‐based attacks. One often adopted solution to mitigate such attacks is based on a centralized security architecture. However, this approach requires large processing and memory resources to handle huge amounts of traffic, which, in turn, causes a significant over dimensioning problem in the centralized nodes. Hence, it may cause this approach to fail from achieving its security task. In this paper, we focus on a SPAM flooding attack, namely SMTP SPAM, and demonstrate, through simulations and discussion, its DoS impact on the Long Term Evolution (LTE) network and subsequent effects on the mobile network operator. Our main contribution involves proposing a distributed architecture on the LTE network that is secure and that mitigates attacks efficiently by solving the over dimensioning problem. It is also cost‐effective by utilizing ‘off‐the‐shelf’ low‐cost hardware in the distributed nodes. Through additional simulation and analysis, we demonstrate the feasibility and effectiveness of our approach. Copyright © 2012 John Wiley & Sons, Ltd.

The open literature encloses numerous studies on the efficiency of retransmission mechanisms used in typical data communication networks for the purpose of recovering from packet transmission errors or losses. This paper revolves around the design and analysis of a Delay-Aware Data Delivery (DADD) scheme for Vehicular Intermittently Connected Networks (VICNs). At the heart of DADD is a novel mechanism that allows a source stationary roadside unit (SRU) to carry out necessary bundle retransmissions to high speed vehicles newly entering its communication range. In turn, these vehicles will guarantee delay-minimal delivery of the retransmitted bundles to the destination SRU. A mathematical model is developed to characterize the operation of the source SRU under DADD as well as to evaluate the resulting bundle delivery delay. To verify the validity and the accuracy of the proposed model, extensive simulations are conducted where the performance of DADD is compared to that of two other existing schemes. Results show that DADD outperforms these two schemes by 14.28% to 36.84%.

The increased propagation delay of future long-reach passive optical networks (LR-PONs) may lead to a significantly increased idle time and delay if optical network units (ONUs) use conventional report-grant mechanisms. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed propagation delay in LR-PONs. In this study, we evaluate three dynamic bandwidth allocation (DBA) frameworks in terms of frame (packet) delay; namely, we consider conventional (interleaved) polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We enhance MT-P and RT-P by applying the just-in-time framework. Next, we provide an analytical framework for evaluating the end-to-end frame delay in our enhanced MT-P and RT-P frameworks. We compare their performance with conventional polling and double-phase polling and investigate their shortcomings and advantages in an LR-PON setting. The simulation results closely match the analysis for this framework. Also, our results indicate that RT-P significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling frameworks.

Ethernet Ring Protection (ERP) has recently emerged to provide protection switching for Ethernet ring topologies with sub-50 ms failover capabilities. In addition to Ethernet's cost-effectiveness and simplicity, ERP's promise to also provide protection in mesh packet transport networks positions Ethernet as a prominent competitor to conventional SONET/SDH and the technology of choice for carrier networks. Higher service availability, however, in ERP mesh networks has been challenged by the issue of network partitioning and the contention for protection resources which may be caused by concurrent failures. In this paper, we show that in a mesh network designed to withstand only single failure situations, network services usually suffer from two outage categories subject to concurrent dual-link failures. We address the problem of minimal capacity network design to provide high service availability against concurrent dual-link failures. We cast this combinatorially complex design problem as an optimization one and show that higher service availability can be achieved by proper RPL (Ring Protection Link) placement and ring hierarchy selection. The objective is to maximize the network flow under any dual-link failure scenario. Our design achieves minimal capacity allocation that minimizes the number of service outages (up to 37%) therefore achieving higher service availability. Numerical evaluation and comparative study show that the joint desgin approach of the ILP model provisions 8% less capacity than the sequential two-step approach to achieve similar service availability.

This paper proposes a novel bundle delivery scheme (BDS) aimed at achieving a delay-minimal bundle delivery in the context of an intermittent roadside network. The realization of this objective is challenging whenever network information is completely unavailable. The concept of virtual space (VS) presents itself as an efficient solution that allows the source to perform necessary data bundle retransmissions to a subset of arriving vehicles. In turn, these vehicles will secure earlier delivery of the retransmitted bundles to the destination. A thorough empirical performance evaluation of the BDS shows that this scheme exhibits a delay improvement of 22.6%-40% relative to other existing schemes.

This paper presents an in-depth investigation on the feasibility of dynamic spectrum access (DSA) in vehicular environments. We present a comprehensive description of the DSA-based vehicle-to-infrastructure (V2I) communication as it takes place in the context of a scenario where spectral resources are limited. Founded on top of this description is a queueing model whose primary objectives are to capture and characterize the dynamics of this type of communication system and assess its performance in terms of several classical metrics. Simplicity and tractability distinguish the proposed model herein from existing models in the literature. Extensive simulations and numerical analysis are conducted for the purpose of validating the proposed model, evaluating the performance of DSA-based communication, and highlighting its limitations.

This paper addresses, in the context of wireless network design, the problem of optimally partitioning the spectrum into a set of nonoverlapping channels with nonuniform spectrum widths. While narrower bands split the total available spectrum into more nonoverlapping channels allowing more parallel concurrent transmissions, wider spectrum bands yield links with larger transport capacity. Thus, we model the combinatorially complex problem of joint routing, link scheduling, and variable-width channel allocation in both single and multirate multihop wireless networks as a mixed integer linear program, and present a solution framework using the column generation decomposition approach. Given the nature and complexity of the resulting dual subproblem, we propose heuristic methods for partitioning the spectrum and allocating resources to each active links, and hence obtain solutions for larger network instances. We present several numerical results and engineering insights suggesting both spectrum width and transmission rates as effective tunable knobs for combatting interference and promoting spatial reuse and thus achieving superior performance in multihop settings.

ABSTRACT In this paper, we propose a cross‐layer design framework for the joint problem of coding‐aware routing and scheduling in WiMAX‐based mesh networks with unicast sessions. The model attempts to maximize the system throughput by exploiting opportunistic coding opportunities through appropriate routing and by achieving efficient spectrum reuse through appropriate link scheduling. We assume centralized scheduling at the base station and focus on minimizing the total schedule length to satisfy a certain traffic demand. Minimizing the schedule length is equivalent to maximizing the system throughput. We present a linear programming optimization model for the joint problem, which relies on the enumeration of all possible schedules. Given its complexity, we decompose the problem using a column generation approach. Our numerical results show that significant gains may be achieved when network coding is incorporated into the design. We compare the performance with that of a joint coding‐oblivious model with and without transmission power control. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes a novel Delay Optimal Data Delivery (DODD) scheme that aims at achieving a delay-minimal bundle delivery in the context of an intermittently connected vehicular network. The realization of this objective is challenging since network information is completely unavailable. The concept of Virtual Space presents an efficient solution that allows the source to perform necessary data bundle retransmissions to a subset of arriving vehicles. In turn, these vehicles will secure earlier delivery of the retransmitted bundles to the destination. The mathematical performance evaluation of DODD as well as extensive simulations show that this scheme exhibits a delay improvement of 22:6% to 40% relatively to other existing schemes.

Ethernet Ring Protection (ERP) has recently emerged to provide protection switching for Ethernet ring topologies with sub-50 ms failover capabilities. In addition to Ethernet's cost-effectiveness and simplicity, ERP's promise to also provide protection in mesh packet transport networks positions Ethernet as a prominent competitor to conventional SONET/SDH and the technology of choice for carrier networks. Higher service availability, however, in ERP has been challenged by the issue of network partitioning caused by concurrent failures. In this paper, we show that in a network designed to withstand only single failure situations, the service availability in the presence of dual failures is affected by the design method, i.e., the RPL placement as well as the selection of ring hierarchy. Therefore, we present a study for characterizing service outages and propose a design method which strikes a balance between capacity requirement and service availability (i.e., the number of service outages resulting from concurrent failures). We observe that by properly selecting the RPL positions and the ring hierarchies, remarkable reduction in service outages is obtained (hence higher service availability in the presence of dual failures) at a modest increase in capacity deployment. Numerical results show that our design approach can reduce the number of service outages as much as 39.1% with only 6.9% additional investment in capacity deployment.

The fourth generation of mobile networks is considered a technology-opportunistic and user-centric system. Part of its new architecture, 4G networks will implement an evolved packet core. Although this will provide various critical advantages, it will however expose telecom networks to serious IP-based attacks. One often adopted solution to mitigate such attacks is based on a centralized security architecture. This centralized approach nonetheless, requires large processing resources to handle large amount of traffic, which may result in a significant over dimensioning problem in the centralized nodes causing this approach to fail from achieving its security task. In this paper, we primarily contribute by presenting a first look on the DoS effects of VoIP SPIT flooding on 4G mobile networks. We further contribute by proposing a distributed architecture on the mobile network infrastructure that is secure, efficient and cost-effective.

Designing low latency polling schemes is one of the most important parts for passive optical networks (PONs), particularly for long-reach PONs (LR-PON) which suffer from long propagation delays. Sophisticated and efficient bandwidth allocation mechanisms are required to cope with the imposed transmission delay in LR-PONs. In this work, we evaluate three dynamic bandwidth allocation methods in terms of transmission delay. Namely, we consider conventional or interleaved polling for traditional PON and two recently introduced scheduling paradigms for next generation LR-PON, i.e., multi-thread polling (MT-P) and real-time polling (RT-P). We examine various flavors of each scheduling method and investigate their shortcomings and advantages in a LR-PON setting. Furthermore, we provide an analytical framework for obtaining packet delay in an enhanced version of RT-P method. The simulation results highly match the analysis for this framework. Also, our results indicate that RT-P method significantly reduces frame delay in LR-PONs compared to MT-P and conventional polling methods.

Ethernet Ring Protection (ERP) has recently emerged to provide protection switching for Ethernet ring topologies with sub-50 ms failover capabilities. ERP's promise to also provide protection in multi-ring mesh packet transport networks will position Ethernet as a serious competitor to conventional SONET/SDH and the technology of choice for carrier networks. Operating ERP in multi-ring mesh networks however comes with unique challenges. This paper presents an optimal ERP capacity design formulation by jointly solving the problem of routing, RPL placement, and ring hierarchy selection. When prior work relied on exhaustive enumeration, our current design is formulated as an integer linear program (ILP) which is shown to be both more capacity and computationally efficient.

We investigate the joint problem of relay selection and optimal sharing of relay power in wireless cellular networks with multicast traffic. We use two different performance metrics to maximize the network performance. We first present a mixed Boolean-convex optimization model to maximize the overall network capacity and solve this combinatorial problem optimally using branch and bound technique. We then show that obtaining the optimal solution is computationally not feasible for large network sizes and, unlike the case of unicast traffic, a water filling method does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing which substantially reduces the computation time and achieves near optimal solutions. In addition, we present a mixed integer linear programming model to maximize the capacity of the minimum capacity link and show that the model is very efficient to reach the optimal solutions.

Delay-optimal data delivery in Vehicular Intermittently Connected Networks (VICNs) is challenging since vehicular traffic is affected by numerous recurring and completely random events. Some of these events cause breakdowns and jams while others subserve traffic stability. Researchers observed that mobile vehicles might be wisely exploited to connect two isolated, Stationary Roadside Units (SRUs). In this context, the design of effective delay-minimal data relaying strategies is receiving significant attention. However, many existing such schemes either do not adequately model vehicular traffic behaviours or adapt typical Internet packet-like forwarding protocols to VICNs. In contrast, this manuscript presents a concise, yet comprehensive study of vehicular traffic states based on which a "comme-il-faut" vehicular traffic model is established. This model captures the fundamental traffic characteristics and enables the selection of appropriate distributions for vehicular flow and speeds that parallel the realistic measurements made by traffic theorists. These distributions constitute the basis of a novel Probabilistic Bundle Release Scheme with Bulk Bundle Release (PBRS-BBR) that is proposed with the objective to minimize the average bundle delivery delay. An analytical queueing model is formulated to assess the performance of PBRS-BBR under medium-to-light vehicular traffic. Extensive simulations are conducted to prove the model's validity and accuracy.

The performance of vehicular data networks (VDNs) is highly dependent on vehicular traffic. Existing studies on VDNs consider custom-developed traffic models that mimic real-life vehicular traffic behavior and prepare the ground for accurate VDN performance evaluation. Traffic evolution is affected by numerous random events. Some developed models are microscopic. They independently consider some possible factors (e.g., weather, road geometry, and drivers' skills). These microscopic models are complex, and their implementations may be costly. Other models are macroscopic. They revolve around only the following three major traffic parameters: 1) density; 2) flow; and 3) speed. The majority of such existing models are unrealistic, because they are based on restrictive assumptions tailored to their enclosing study. Comparing the performance of VDN protocols becomes adequate if and only if these protocols are all developed on top of the same traffic model. Unfortunately, the opposite is true. Hence, the design of a generic traffic model that serves as a basis for future studies on VDNs is equally urgent and important. This paper presents a comprehensive and traffic-theory-inspired macroscopic description of vehicular traffic behavior over roadway facilities that operate under free-flow traffic conditions. Accordingly, a simple and tractable macroscopic traffic model is proposed. Extensive simulations are conducted to verify the validity of the proposed model and its high accuracy.

The use of vehicular infrastructure to establish connectivity between isolated stationary Information Relay Stations is an appealing application of Terrestrial Delay-Tolerant Networking. A source opportunistically releases data bundles to vehicles that randomly enter its range. In turn, those vehicles store the received bundles, carry and deliver them to their intended destination. It follows that a contemporaneous source- destination end-to-end path does not exist. Consequently, bundles experience longer queueing periods at the source. Under such circumstances, the end-to-end bundle delivery delays become several orders of magnitude higher than those experienced in traditional wireless networks. In this context, bundle delivery delay minimization emerges as a challenging problem that has not been adequately addressed in the open literature. This paper proposes a simple Probabilistic Bundle Relay Strategy with Bulk Bundle Release (PBRS-BBR) that aims at minimizing the average end-to-end bundle delivery delay while capturing the essence of vehicular delay-tolerant networking in that it revolves around minimal network information knowledge. A queueing model is formulated to represent stationary sources operating under PBRS-BBR. This model is mathematically analyzed and validated through extensive simulations that gauge its merit.

Carrier Ethernet exhibits an enormous potential to be a cost-effective and less complex replacement of SONET/SDH especially after the ratification of IEEE standard 802.1Qay Provider Backbone Bridge-Traffic Engineering (PBB-TE). The recent IEEE standard 802.3az Energy Efficient Ethernet (EEE) presents another opportunity for service providers to select Ethernet as a technology of choice in the backbone while leveraging on its promise for achieving green transport networks. In this paper, we propose two novel architectures of Photonic PBB-TE (PPBB-TE) core and edge switches, which enhance the usability of PBB-TE networks by reducing power consumption in individual switches in conjunction with passive optical bypassing and EEE. We also formulate the problem of energy-aware scheduling as an optimization problem whose objective is to minimize the overall energy consumption for transmitting Ethernet frames while satisfying their delay requirements. This model will be used as a benchmark for evaluating the performance of packet coalescing, a recently proposed performance-enhancing technique, as well as the performance of EEE.

A persisting major challenge in Vehicular Delay- Tolerant Networks (VDTNs) is the delay minimization of data delivery when communicating nodes are stationary, arbitrarily deployed along roadsides and considerably apart that they cannot establish direct communication between each other. A source opportunistically releases bundles of data to cooperating vehicles passing by, hoping that they will successfully deliver them to the intended destination. Several complex strategies that tackle this problem have been proposed in the open literature. Nevertheless, these strategies often implicitly assume complete network knowledge. In this paper, we propose a rather simple Probabilistic Bundle Relay Strategy (PBRS) that relaxes the availability of complete network information. A queuing model is formulated to represent VDTN stationary sources where PBRS is deployed. We introduce the bundle release probability parameter which expresses the likelihood that a bundle is released by the source to a vehicle passing by. The proposed model is studied analytically and theoretical expressions of its characteristic parameters are all derived. In particular, we compute the time it takes to release a head-of-line bundle (referred to as the bundle's service time). Moreover, the model is validated through a simulation study that gauges its merit. The simulation results show that even with partial network knowledge the proposed queueing model can guarantee acceptable bundle delivery delay.

Dynamic bandwidth allocation and grant scheduling are among the major issues in the design of passive optical networks. In this paper, we investigate the problem of optimal scheduling and bandwidth allocation in next generation 10GEPON coexisting with 1G WDM-PONs. We first propose a network architecture for providing this coexistence. Then, we derive an ILP model for offline joint scheduling and bandwidth assignment for 10G-TDM and 1G-WDM ONUs. In order to address the scalability of the ILP model, we introduce a Tabu Search based heuristic for obtaining near optimal solutions while the computation time is remarkably reduced. We further explore the tradeoff which exists in terms of delay, scheduling length, and channel utilization, when separate or the same DBA modules are used for 1G- and 10G-ONUs.

Survivability is a key issue in the design of optical mesh networks. Various survivability schemes have been introduced among which p-cycle has been attracting quite a lot of attention because of its fast and efficient protection capability. The concept of p-cycle was later generalized to pre-cross-connected trails or p-trails, by noting to the fact that providing pre-cross-connected protection paths and obtaining fast restoration does not require a cyclic structure. In this paper, we investigate the capability of p-trails in protecting traffic demands in a mesh-based survivable network. By taking the sharing capability of p-trails into account, we introduce optimization models to verify the remarkable efficiency of p-trails. We derive two ILP models for survivable network design using p-trails. In our first model, the optimal solution is obtained from a candidate set constructed by exhaustive enumeration of all simple trails. We observe that the size of our ILP model, and therefore the computation time, become prohibitively large making the model unpractical for larger network instances. Therefore, to overcome this scalability issue, we develop a better model for this complex optimization problem using a primal-dual decomposition of the original problem based on the column generation (CG) optimization method. Our developed design approach is shown to be very scalable, as opposed to other prior p-trail design methods; further, we show that p-trails are more efficient than p-cycles in terms of resource redundancy in the network.

In multihop, multi-rate wireless networks, simultaneous transmissions can interfere with one another to impair the transmission quality and prevent correct frame reception. Achieving high network performance (e.g. throughout and energy consumption) in such networks therefore requires a balance between the spectrum spatial reuse and the transmission quality. To achieve this trade-off, a decentralised control algorithm is proposed that allows a sender–receiver pair, using the IEEE 802.11 four-way access method, to dynamically adjust the transmit power and rate for their frames according to the level of interference in the network. Specifically, the scheme is based on the interplay between two approaches: physical carrier sensing and virtual carrier sensing approach to achieve this optimal trade-off. The algorithm outlines the rules for performing power and rate assignment so that higher performance is obtained. A realistic analytical model is presented to study the performance of the proposed heuristic; analytical results show that the algorithm proposed by the authors, indeed, finds the balance between spatial reuse and transmission quality through its appropriate search for the suitable transmission parameters. Simulation results for different topologies are used to demonstrate the significant throughput and energy gains that can be obtained by the proposed scheme.

Network survivability is a key design issue for optical transport mesh networks. Various survivability schemes have been introduced among which p-cycle has (and continues) attracted quite a lot of attention because of its fast and efficient protection capabilities. The concept of p-cycle has been generalized to pre-cross-connected trails, or p-trails, by exploiting the fact that providing pre-cross-connected protection paths and obtaining fast restoration do not necessarily require a cyclic structure as in p-cycles. In this paper, we investigate the benefits and sharing capabilities of p-trails and observe that non-simple p-trails and p-cycles can be built from merging simple trails. We derive two ILP models for survivable network design using p-trails. Our first design model is a simple ILP whose optimal solution relies on the exhaustive enumeration of all simple trails in the network. We observe that the size of our ILP model, and therefore the computation time, become prohibitively large making the model unpractical for larger network instances. Therefore, to overcome this scalability issue, we develop an enhanced model for this complex optimization problem using the column generation (CG) decomposition technique. Our developed design approach is shown to be very scalable, as opposed to other prior p-trail design methods; further, we show that p-trails are more efficient than p-cycles in terms of protection resource redundancy in the network.

This paper studies the interplay between network coding and spatial reuse in wireless mesh networks. We present a method that attempts to maximize the system performance by exploiting effectively (and not greedily) coding opportunities through appropriate routing and achieving efficient spectrum reuse through opportunistic link scheduling. We show that judiciously selecting coding structures requires proper transmission power allocation to better manage cumulative interference in the network, and thus yield better spectrum spatial reuse and effective multi-hop system throughput. We present an optimization model for this complex design problem, which relies on the enumeration of all possible schedules and decompose it into subproblems which we can solve more efficiently. Our numerical results indicate that optimal joint coding and scheduling with proper power allocation yields a performance enhancement of more than 10% over that with maximal power transmission and more than 45% enhancement over a coding oblivious design model. Our results also revealed that network coding has only marginal benefits (~6%) in a dense network and that in such networks managing interference through proper power allocation yields very good performance.

We investigate the problem of flexible spectrum access in multihop wireless networks. We assume radios that are capable of transmitting on channels of contiguous frequency bands and which do not require any sophisticated processing. Because these radios can flexibly configure their transmissions anywhere in the available frequency band, the spectrum becomes vulnerable to fragmentation and interference. We consider the joint problem of routing, link scheduling and spectrum allocation where scheduling feasibility is considered under the physical interference (SINR) constraint. We present a primal-dual decomposition for this complex optimization problem based on column generation. We show that obtaining the optimal solution to this problem is computationally not feasible, except for very small networks. We thus adopt a two-fold method to circumvent the complexity while yielding practical solutions. First, we relax the SINR constraint and use a simplified graph-based model for the interference. Second, we use a simulated annealing (SA) approach to solve the dual subproblem. Our SA approach however is augmented with an SINR feasibility check. Our results confirm that the primal-dual decomposition method using SA substantially reduces the computation time and achieves near optimal solutions. The results also reveal that substantial improvement in network performance is obtained with flexible spectrum assignment which results from its capability of better managing the interference in the network.

Bandwidth allocation and transmission grant scheduling are problems of particular interests to multichannel Passive Optical Networks (PONs). While a number of studies have been carried out on each of these subproblems separately, to the best of our knowledge a study on the joint problem has been lacking. In this paper, we investigate the joint problem of bandwidth allocation and transmission grant scheduling in multichannel optical access networks using a scheduling theoretic approach. We formulate the problem as an ILP model and compare it with the sequential model presented in pervious work. Our experiments declare that the joint scheduling and sizing algorithm achieves a reduction of up to 28% in scheduling cycle length when compared to the non-joint models. Since the model has shown to be very hard to solve, except for small sized network instances, we introduce a Tabu search heuristic for the joint problem that provides near optimal solutions in significantly shorter times. We also illustrate that the choice of buffer size of ONUs has a critical rule in the performance of our joint scheduling and bandwidth allocation solutions.

Recent research results have shown that channel width is an important control knob that can be easily adapted through software and can be used for achieving higher system throughput and better energy efficiency. In this paper we address the problem of joint routing and transmission scheduling in a multichannel wireless mesh network with variable-width channel allocation. While narrower bands split the total available spectrum into more non-overlapping channels allowing more parallel concurrent transmissions, wider bands increase the capacity of the communication links. We present a cross-layer problem formulation which incorporates multi-path routing and link layer scheduling. We model this combinatorial complex problem as a mixed integer linear program and present a primal-dual decomposition method for solving it. Our method has always shown to strike a good balance between often conflicting objectives to achieve higher system performance. Numerical results revealed that up to 57% improvement in network performance is obtained when variable-width channel assignment is used against the best fixed-width channel assignment for larger networks; this is due to the capability of the former in achieving a good balance between higher concurrency and better control of interference.

The nature of a time variant channel environment have posed significant challenges on the design of new communication protocols and applications for IEEE 802.11 based multihop networks. The ability to predict the channel status enables these networks to better exploit the network resources and take precautionary measures, when necessary, to improve the channel reuse through tuning protocol parameters (such as transmit power, carrier sensing threshold and transmission data rate). This work suggests the use of Interacting Multiple Model (IMM) estimator, which is build from a (K) number of Kalman Filters to work in parallel, in order to predict the future signal-to-interference in a wireless network with time-varying channel conditions. These predictions are then used by a rate adaptation scheme in discrete event simulation environment to ensure proper and predictable resource usage.

We investigate the problem of scheduling and flexible spectrum access in multi-hop networks with software defined radios. We present a primal-dual decomposition to provide an exact solution for this complex optimization problem. We show that obtaining the optimal solution is computationally not feasible. Thus, we first relax the SINR constraint and use a simplified graph-based interference model. Second, we use a simulated annealing (SA) approach for solving the dual sub-problem. Our SA approach is then augmented with a feasibility check so that only SINR-feasible schedules are passed back to the primal.

In this work a cross-layer optimization in centralized Wireless Mesh Networks equipped with multiple antennas is introduced. A scenario has been considered in which multiple antennas at each node are employed to suppress the interference through beam-forming. By integrating different constraints from MAC and Network layers, a mathematical LP formulation is presented to minimize the system activation time. Since the optimization problem is a complex combinatorial one, the optimal solution is approached by a Column Generation decomposition method. Numerical results indicating the theoretical capacity limit of the network are provided for different node densities, transmission ranges and number of antennas. It is shown that interference suppression increases the spectrum spatial reuse and enhances the throughput of the network by minimizing the operation time duration of the system. Moreover, depending on the density of the interference and traffic sessions, there is a saturation threshold on the number of antenna elements; beyond that, more antennas do not necessarily result in smaller system activation time. Furthermore, increasing the transmission range leads to decrease the system activation time of such networks.

We present a cross-layer optimization framework for wireless mesh networks; we assume nodes are equipped with smart antenna and techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are considered. These techniques provide interference suppression, capability to communicate with several nodes simultaneously and higher transmission data rates, respectively. By integrating different constraints from MAC and network layers, a mathematical formulation of the problem is presented. It is shown that the resulted directive, multiple access and multiplexing gain combined with optimal link scheduling effectively increase both the spectrum spatial reuse and the capacity of the links and thus, enhance the achievable system throughput. Since the optimization problem is a complex combinatorial one, the optimal LP solution is approached by a Column Generation decomposition method. The numerical results for different network topologies with various node densities, transmission ranges, traffics and number of antennas are provided, in which system activation time is reduced up to 86.9% by employing smart antenna techniques.

Security has become a primary concern in order to provide protected communication in mobile networks. Unlike the wired networks, the unique characteristics of mobile networks pose a number of nontrivial challenges to security design, such as open peer-to-peer network architecture, shared wireless medium, stringent resource constraints, highly dynamic network topology and absence of a trusted infrastructure. Ubiquitous roaming impacts on a radio access system by requiring that it supports handover between neighbouring cells and different networks. Also, mobile networks are more exposed to interferences than wired networks. There are several components that contribute to this: adjacent channels, co channels, Doppler shifts, multipath and fading. This SI aims to identify and explore the different issues and challenges related to security aspects in mobile networks. It is dedicated to present the state-of-the-art research results and findings on Security in Mobile and Wireless networks. We accepted 12 papers out of a total of 31 submissions from all over the world. Each paper was reviewed by at least three reviewers and has undergone through two cycles of revisions. The first paper introduces the notion of malnets: networks of adversary-controlled wireless routers targeted to a physical geography. Similar to Internet worms such as Slammer and Code-Red, malnets are created by the recursive compromise of targeted devices. However, unlike their traditionally wired counterparts, malnet worms exploit only other routers that are within their transmission range. The malnet creates a parallel wireless infrastructure that is (a) completely under control of the adversary, and (b) spans a targeted physical area, creating a valuable infrastructure for a variety of virtual and physical attacks. A study of the propagation characteristics of commercial routers and model inter-router connectivity using publicly available war-driving data is given. The resulting characterization is applied to well-known epidemiological models to explore the success rates and speeds of malnet creation across cities such as New York, Atlanta and Los Angles. Finally, a sampling of available exploits is used to demonstrate the construction of multi-vector, multi-platform worms capable of targeting wireless routers. Wireless vehicular communications pose significant challenges for the deployment of next generation roadside services. Some important issues that must be tackled are security, billing and reliability while guarantying a scalable service delivery. The second paper addresses the assignation of secure service session parameters upon the reception of on-demand service requests by an incumbent services district domain, and studies and analyses the performance of the underlying mechanisms. Three types of service request protocols are introduced: single-hop, extended connectivity and multi-hop service requests. A detailed analytical model and cost study for the access protocols are presented. The analysis study covers the estimation of total cost in terms of latency for each access protocol with different mobility characteristics and vehicle densities within the service coverage area and across different serving district domains. The third paper noted that providing secure routing in mobile ad hoc networks (MANETs) is far more difficult than establishing secure routing in wired networks or static wireless networks, because node mobility and the relative scarcity of bandwidth render prior solutions ineffective. Further solutions based on securing link or path information do not work well in MANETs because the dynamic nature of links requires extensive use of flooding to establish effective countermeasures. The authors argue in their paper that secure routing in MANETs must be based on the end-to-end verification of physical-path characteristics aided by the exploitation of path diversity to increase the probability of finding secure paths. They hence applied this approach to the design of the Secure Routing through Diversity and Verification (SRDV) protocol and proved that the countermeasures used in SRDV can defend against a variety of known attacks to routing protocols, including attacks involving collusion, and the fabrication and modification of routing packets. The fourth paper proposes a new architecture based on an efficient trust model and Secure Distributed Clustering Algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of certificates in each cluster. They use a combination of a fully self-organized security for trust models like PGP adapted to adhoc technology and the clustering algorithm with respect to trust and mobility metrics. Furthermore, they present a new approach: the Dynamic Demilitarized Zone (DDMZ) to protect the CA in each cluster. The principal idea of DDMZ consists in selecting the dispensable nodes, also called registration authorities (RA); these nodes must be confident and located at one-hope from the CA. Their roles are to receive, filter and treat the requests from any unknown node to the CA. This approach allows to avoid the single point of failure in each cluster. Moreover, they propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of the DDMZ against different attacks. The performance evaluation of the proposed SDCA shows the robustness and the availability of DDMZ. The effects of direct connectivity and transmission range on the stability and security of the network are analysed. In the fifth paper, authors observe that, in MANETs, there is an increasing interest in providing anonymity for witnesses, i.e. those users who share their knowledge in detecting either malicious or selfish users. As well, it is a challenging problem to prevent the misuse of anonymous sources. They proposed the PlainClothesMan (PCM) protocol to provide anonymity for the witness who helps identify malicious or selfish users. Once there are more than a certain number of claims from distinct users against the same user, she is identified as a malicious or selfish user. Moreover, in PCM, the misuse of the witness anonymity is prevented in such a way that malicious users who broadcast the same invalid claims repeatedly can be identified. Two exemplary scenarios are designed and simulated to model the necessities of witness anonymity in MANETs. Simulation results show that, witness anonymity is very important for ensuring proper and efficient executions of fundamental functionalities of MANETs, e.g. certificate revocation and fairness, and PCM is both effective and efficient in providing such a type of anonymity The sixth paper provides an overview of Internet security to meet the IP Multimedia Subsystem (IMS). The IMS, a platform designed for providing various multimedia services, requires high availability and trustability. However, the IMS specifications do not explicitly introduce the protection mechanisms. By focusing on the security issues of IMS, the seventh paper discusses and surveys the potential security threats on IMS. Those security threats include: (a) routing attacks and DNS attacks that result from vulnerabilities of IP technology, and (b) Denial of Service (DoS) attacks and the privacy control threats that are IMS-specific. (c) Spam threats such as Spam over Internet Telephony (SPIT), Spam over Instant Messaging (SPIM) and presence spam. On the other hand, this paper also reviews the existing work, available countermeasures and tools for preventing these threats from harm. The seventh article addresses schemes to provide key recovery capability for mobile communications. In recent year, wireless communication network has been the most convenient and powerful way for all computer users to access the resources from the Internet. However, the lack of suitable key recovery mechanism that meets the special security and performance requirements for wireless communication networks is still an open issue. By addressing this issue, the eighth paper proposes a novel security mechanism with key recovery capability satisfying the requirement of privacy protection and monitoring while maintaining low computational complexity. The scheme provides a ‘backdoor’ for an authorized agency to monitor suspected communications while protecting legal users from unauthorized disclosure of their data privacy. With the security and performance analysis of the proposed protocol, the author shows that the scheme is suitable for deploying in wireless communication network. The eighth paper introduces the analysis of performance overheads for securing the signalling plane in beyond 3G networks. Although the countermeasures are urgent for mobile network such as IMS, the cost should also be considered. For signalling plane, since no built-in protection mechanism was defined for SIP, 3GPP technical specifications introduce IPSec to protect SIP messages. However, the additional protection can lead to significant computational and spatial overheads both in the core network and in the terminals, which might affect their performance. By focusing on the possible performance overhead from protecting SIP by additional IPSec methods, the ninth paper presents and validates the analysis of potential performance overheads brought by security mechanisms developed to protect signalling messages in beyond-3G core networks and mobile terminals. According to the analysis result, the overheads for both cases can significantly impact the performance of the entire architecture. The ninth article addresses the impact of key assignment on VANET privacy. The privacy issue is also an important topic for VANET. To know the best way of key assignment for maintaining user privacy from (1) the CA or (2) non-CA entities, the tenth paper gives the analysis of the privacy provided by three key assignment approaches for VANETs: (1) all vehicles share (a) a copy of the same key and (b) a copy of multiple same keys, (2) each unique key is held by only one vehicle and (3) each key is held by several vehicles. According to the mathematical and logical analysis result, the authors found that ‘keys should be unique to vehicles and vehicles should be given multiple keys.’ Besides, the author also verified that ‘it is impossible to provide good privacy and fast revocation when keys are shared among vehicles.’ The tenth paper proposes a Dirichlet reputation system in reliable routing of wireless ad hoc network. In peer-based network such as wireless ad hoc networks, the reliable routing is most important for maintaining the normal operation and good throughput of the whole network. However, the misbehaviour of some nodes will impact the routing reliability. To predict and select a reliable path for each nodes of wireless ad hoc network, the 11th paper proposes Dirichlet reputation model based on Bayesian inference theory that evaluates reliability of each node in terms of packet delivery. To adjust the responsiveness of the reputation system, the moving window mechanism is applied to change the speed of updating nodes' reputation: the larger size implies the fast misbehaviour detection and the smaller size slows the speed of redeeming bad reputation. According to the simulation result, the negative impacts of misbehaving nodes can be reduced and the throughput can be improved by integrating the proposed reputation system into the routing protocol of wireless ad hoc network. The 11th paper suggests an efficient reputation based hybrid key management architecture for ad hoc networks. In fact, it is difficult to deploy the common key management approaches into the wireless ad hoc network. To address this issue, the twelfth paper proposes adaptable hybrid public key management architecture for wireless ad hoc networks based on a clustering algorithm—RECA. The reputation system is applied to compute the trust levels information. All trustworthy cluster-heads that managed clusters are elected based on the trust levels. In addition, each cluster-head is a CA for the members of the cluster and the key management among all the cluster-heads is done by applying totally distributed CA. According to the simulation result, the enhancement of scalability and certification services availability can be achieved. The last article offers a new cluster based algorithm for Black Hole Intrusion Detection System (BHIDS). This paper focused on intrusion detection on wireless ad hoc network. Only applying the traditional firewall or cryptography is not enough to protect the wireless ad hoc network from active attack. This paper addresses this issue and proposes a 2-layered cluster-based BHIDS for mobile, ad hoc networks. By splitting the nodes in the network into 2-layered clusters, the processing and communication overhead between the cluster heads at layer 1 and 2 are reduced the cluster heads of each layer can cooperate to detect the potential attacks. The features of monitoring include: mobility of nodes, variation in number of attacking nodes, packet delivery rate and density of the network. The simulation result shows marked improvement on packet dropping compared with the other work. Furthermore, the authors found that the stability of routes in sparse network is more than dense network. We would like to thank the contributors as well the reviewers towards the success of this special issue. We specially thank Professor Hsiao-Hwa Chen, Editor-in-Chief, Wiley's Security and Communication Networks Journal, for his advice and help during the process of putting together this Special Issue. We hope that you will enjoy reading this Special Issue.

The problem of scheduling and routing tree construction in WiMAX/802.16 based mesh networks is not defined in the standard and has thus been the subject to extensive research. We consider the problem of joint routing and scheduling in WiMAX-based mesh networks, with the objective of determining a minimum schedule period that satisfies a given (uplink/downlink) traffic demand. Minimizing the length of a schedule amounts to maximizing the spectrum spatial reuse by activating concurrently as many links. This group of transmission links active concurrently is referred to as the transmission group and refers to the set of wireless links that can simultaneously transmit without violating the signal-to-interference-plus-noise ratio (SINR) requirement. Our model is referred to as maximum spatial reuse (MSR). We assume centralized scheduling at the base station and attempt to maximize the system throughput through appropriate routing tree selection and achieving efficient spectrum reuse through opportunistic link scheduling. We present an ILP optimization model for the joint problem, which relies on the enumeration of all possible link schedules. Given its complexity, we decompose the problem using a column generation (CG) approach. We present two formulations for modeling MSR, namely the link-based (CGLink) and the path-based (CGPath) formulation. These two formulations differ mainly in the number of routing decision variables. Our experimental results indicate that the path-based formulation needs much less computational (CPU) time than the link-based in order to determine the (same) optimal solution with the same spatial reuse gain.

At present there is a strong worldwide push towards bringing fiber closer to individual homes and businesses. Another evolutionary step is the cost-effective all-optical integration of fiber-based access and metro networks. STARGATE [1] is an all- optical access-metro architecture which does not rely on costly active devices and makes use of an overlay island of transparency with optical bypassing capabilities. In this paper we propose ONU architectures, and discuss several technical challenges, which allow STARGATE EPONs (SG-EPONs) to evolve in a pay-as-you grow manner while providing backward compatibility with legacy infrastructure and protecting previous investment. Using a simple bandwidth and wavelength allocation, we study the capacity and delay performance of SG-EPON through simulations.

Inter-Vehicle Communications have recently attracted research from both academia and industry. In such networks, vehicles should be able to communicate among each other (V2V) as well as with roadside Infrastructure units (V2I). These units provide some services such as driver information systems and Internet access. Because of the high speed and high mobility of vehicles, establishing and maintaining a connection to these units is very challenging. We introduce a new protocol which uses the characteristics of vehicle movements to predict the vehicle behavior and select a route with the longest life time to connect to the wired network. It aims at spreading the advertisement messages through multi-hops without flooding the network, do seamless hand-overs and select the most stable routes to these units. We performed some simulations and compared the performance of our work with some well-known protocols.

The problem of scheduling and tree routing in WiMAX/802.16 based mesh networks were not defined in the standard and are thus subject to extensive research. In this paper, we consider the problem of joint routing and scheduling in 802.16-based wireless mesh network, with the objective of determining a minimum length schedule that satisfies a given (uplink/downlink) end-to-end traffic demand. Minimizing the schedule length amounts to maximizing the spectrum spatial reuse by concurrently transmitting on as many links as possible, which we refer to as a transmission configuration (a group of links that can simultaneously transmit without violating the signal-to-interference-plus- noise ratio (SINR) requirement). Our model is referred to as maximum spatial reuse (MSR). Since there is an overwhelming number of possible transmission configurations to be assigned to time slots, we adopt the column generation technique to construct our MSR model. We present two formulations for modeling MSR, namely the link-based column generation (CGLink) formulation and the path-based column generation (CGPath) formulation. These two formulations differ mainly in the number of routing decision variables. Our experimental results indicate that the path-based formulation needs much less computational (CPU) time than the link-based formulation in order to determine the (same) optimized solution with the same spatial reuse gain.

Failure-independent path-protecting (FIPP)p-cycle is an extension of the span-protectingp-cycle, and an alternative approach for providing fully pre-connected protection paths with end-to-end failure-independent path protection (Kodian and Gorver,J.ofLightwaveTechnol.,vol. 23, no. 10, pp. 3241-3259). We study the unavailability of end-to-end traffic in FIPP-based mesh networks, which are designed to protect against single failures, and present an availability-aware network design method. Our design method allocates FIPPp-cycles such that the end-to-end unavailability of the protected demands is bounded by an upper limit which we can control. Our study will also focus on determining whether FIPPp-cycles will maintain their resource efficiency advantages over spanp-cycles when the network design is based on limiting the unavailability. Our results first show that the length of the FIPPp-cycle plays a vital role in determining the availability of the working path(s). Similar to span-protectingp-cycles, higher service working path(s) availability is obtained when the FIPPp-cycle(s) contains fewer hops. Results also indicate the important role of the number of demands protected by the same FIPPp-cycle. We notice that the higher the desired availability is, the less efficient the FIPP method becomes. This relationship is due to the fact that, to achieve higher service availability, the design will limit the number of demands sharing the same FIPP cycle. Accordingly, we affirm that, when the network design limits the service unavailability, FIPP tends to be less efficient, and its redundancy is 8-13% higher than span-protectingp-cycles. Additionally, we observe that, when we do not limit the unavailability, the average availability for span-protectingp-cycles tends to be more than the FIPPp-cycle method. We present our findings.

WiMax/802.16 mesh network is an emerging infrastructure that offers a cost-effective deployment for highspeed wireless broadband access to the back haul network. However, as in most wireless multi-hop networks, WiMax/802.16 mesh suffers from interference that decreases considerably the throughput and spatial reuse of the network. Interference in WiMax/802.16 mesh is a result of several phenomena, namely concurrent transmissions in the neighborhood and data collisions (that need to be avoided) at a receiver from transmitting nodes that are outside the range of each other (hidden terminal nodes). In this paper, we study the problem of minimizing iInterference (MI) in WiMax/802.16 mesh centralized scheduling networks by appropriately routing end connections and assigning slots to them. The proposed model includes the effect of hidden terminal nodes as well as the interferences coming from neighboring nodes. Results show that power-aware routing and adequate frame size selection yield better network performance, a consequence of the improved network spatial reuse.

Wireless LANs (WLANs) suffer degrading performance when operating within domestic areas due to multiple reasons such as: multi-path, fading, path loss and user mobility. To overcome this, transmission rate is usually adjusted to a more error-resistant rate. In this paper, we first present a novel rate adaptation algorithm for IEEE 802.11 that can efficiently identify the threshold frame error rate (FER) at which link adjustment is required, based on a simple throughput analysis at the MAC layer. Then, we extend our rate adaptation algorithm to support IEEE 802.11e quality of service (QoS) requirements. When a real-time stream with QoS requirements is admitted, critical constraints such as delay bound and maximum packet drop count are integrated in the selection of the most convenient transmission rate that best respects the flow requirements. Moreover, we use dynamic bandwidth allocation rather than the default transmission opportunities (TXOPs) in a way that best offers a flow the required time for retransmissions due to packet failure based on the variant loss rate present in the channel. We validate our proposed rate adaptation algorithms via simulation results where the efficiency and effectiveness of the algorithm are noticed for both best effort and QoS flows.

WiMax/802.16 mesh network is an emerging infrastructure that offers a cost-effective deployment for high-speed wireless broadband access to the back haul network. In order to provide the best cost-effective deployment solution, nodes must be powered by batteries which do not require electrical cables and voltage transformers deployment (city power lines voltage is not suited to WiMax/802.16 nodes voltage). Moreover this deployment approach (the only one compatible with a mobile nodes topology) is environment constraint free since not all cities have easy-reachable power lines (e.g. rural cities). However it requires a predictive mechanism to calculate the lifetime of the network, in purpose to provide the adequate maintenance (recharge/change nodes batteries, reconfigure the nodes) at the appropriate time. We study the problem of maximizing the network lifetime (MNL) in order to minimize the maintenance of WiMax/802.16 mesh centralized scheduling networks powered by batteries which causes them to go off line. Moreover our study incorporates an explicit novel modeling of interference and hidden terminal nodes using an appropriate time slot allocation. Results show that power aware routing and a convenient frame size improve the network lifetime.

Mobile devices in the IEEE 802.11 based multihop ad hoc networks support the transmission of data frames at various predefined transmission rates. To achieve high throughput performance, these multiple rates should be exploited and used in an adaptive manner depending on the channel condition. In this paper, we propose a data rate adaptation scheme with the use of Kalman Filter to determine the channel conditions (forthcoming interference). Upon receiving the RTS (request to send) packet, the receiver predicts the future interference and accordingly selects the appropriate transmission rate. Then, the receiver encapsulates the rate value in the CTS packet sent to the transmitter. Furthermore and to ensure correct convergence of the kalman Filter, we propose a fuzzy logic approach to tune the parameters of the Kalman Filter. The performance of the proposed scheme is tested and verified in a discrete event simulation environment.

The efficient routing of multi-hop connections in WiMax/802.16 based mesh networks poses several challenges, such as dealing with the interferences from concurrent transmissions, addressing the collision problem due to hidden terminals and efficiently utilizing the limited node resources. In this paper, we study the problem of maximizing the network lifetime (MNL) in WiMax/802.16 based mesh networks by appropriately routing end connections and assigning slots to them. The proposed model includes the effect of hidden nodes as well as the interferences coming from neighboring nodes. It shows that power-aware routing yields better network lifetime and better network performance, a consequence of the improved network spatial reuse.

Presents the introductory welcome message from the conference proceedings.

WiMax/802.16 mesh network is an emerging infrastructure that offers a cost-effective deployment for high-capacity wireless broadband access to the backhaul network. Recently, mobility in WiMax/802.16 based mesh networks has been discussed through the IEEE 802.16e standard. Hence, mesh nodes need no longer be stationary, and as a result they will be powered from energy limited batteries. In such networks, radio frequency RF-links become vulnerable to breakage due to nodes mobility and nodes are condemned to failure when their battery is depleted. Adopting this deployment strategy requires a mechanism for selecting the most stable routing paths (with the highest RF-links and nodes availability). In this paper, we develop a mathematical model that considers RF-link and node characteristics in such mesh networks and maximizes the network stability.Namely, our model takes into account the interference caused by adjacent RF-links as well as nodes mobility and energy.Results show that selecting most stable paths augments the longevity of the network's time of operation which in turn leads to a higher data delivery and more satisfied clients.

Recently, tuning the physical carrier sensing threshold (CSth) has been proposed as an efficient mechanism to enhance the network throughput in an IEEE 802.11-based multihop ad hoc networks. The physical carrier sensing method reduces the likelihood of collision by preventing nodes in the vicinity of each other from transmitting simultaneously, while allowing nodes that are separated by a safe margin to engage in concurrent transmission. In this paper, we propose a distributed adaptive scheme through which nodes dynamically adjust their CSth to eliminate the likelihood of collisions from hidden terminals and in turn enhances spatial reuse by reducing the number of exposed terminals. Specifically, a node adjusts its CSth based on both its success/ failure history attempts and the information it receives from neighboring nodes through CTS packets. Moreover, to reduce the effect of exposed terminals, the proposed scheme employs the RTS/CTS exchange only for the purpose of informing neighboring nodes of their CSth but not to silence them. The proposed scheme adaptively performs a dynamic switch between the RTS/CTS access scheme and the basic scheme based on a predefined policy in order to avoid the additional overhead caused by the RTS/CTS exchange. Simulations results have demonstrated the significant throughput gains that can be achieved by the proposed scheme, compared with other methods in recent literature.

Telecommunication and grid computing applications demand high bandwidth data channels that offer guarantees with respect to service availability. Such applications include: remote surgery, remote experimentation, video on-demand, teleconferencing and bulk transfers. Furthermore, by forecasting traffic patterns internet service providers attempt to optimise network resources in order to lower operational costs during peak periods of bandwidth consumption. Advance reservation for wavelength division multiplexed networks can address some of these issues by reserving high volume communication channels (i.e. lightpaths) beforehand. The authors develop a mathematical model to solve the problem of scheduling lightpaths in advance. The optimal solution is presented as a mixed integer linear program with the assumption that all traffic is static and the network is centrally controlled. Furthermore, we have developed two novel meta-heuristics based on: 1) a greedy implementation (local search) and 2) simulated annealing. The meta-heuristics have shown to produce good approximate solutions in a reasonable amount of time.

Transmission power control (TPC) in a Mobile Ad hoc network (MANET) environment reduces the total energy consumed in packet delivery and/or enhances network throughput by increasing the channel's spatial reuse. In this paper, a distributed correlative power control scheme using prediction filters (Kalman or extended Kalman) is proposed. The prediction filter is used to estimate the forthcoming interference. Both the transmitter and receiver in MANET environment make use of predicted interference to assign correlative power values to their associated ensued packets to guarantee the success of the IEEE 802.11 four-way handshaking communication (RTS/CTS/DATA/ACK). Simulation results for different topologies are used to demonstrate the significant throughput and energy gains that can be obtained by the proposed power control scheme.

EPONs are designed to deliver services for multiple applications, such as VoIP, standard and high-definition video, interactive video and best effort traffic. We present the first framework that will enable for per-stream/flow QoS protection in EPON networks using a two stage admission control (AC) system. While the first stage enables the ONU to perform flow admission locally according to the bandwidth availability, the second stage allows for global admission control at the OLT. Appropriate bandwidth allocation algorithms are presented as well. An event driven simulation model is implemented to study the effectiveness of the proposed scheme in providing and protecting QoS.

Providing a security solution for mobile ad-hoc networks (MANETs) is not an easy task. This is due to the unique characteristics of MANETs, such as the lack of a pre- existent infrastructure, the dynamic topology of the network, the non-existence of a control authority and the constraints of device resources. In this paper, we introduce the monitoring and cluster manager modules to improve our distributed hierarchical architecture. Moreover, we study the concept of dynamic demilitarized zone (DDMZ) defined in our hierarchical architecture to avoid a single point of failure in MANETs. The DDMZ is formed by the dispensable nodes which belong to the confident community. The confident community is formed by sets of confident nodes which have high trust levels and collaborate with each other to ensure secure services. We propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of DDMZ against different attacks. Furthermore, we estimate the robustness and the availability of DDMZ and we also analyze the effects of direct connectivity and transmission range on the stability and security of the network.

In this work, we study analytically the benefits of transmission power control on throughput and energy consumption in a uniformly distributed power-aware ad hoc networks where nodes are equipped with directional antennas. We construct an interference model for directional antenna based on a honey grid model to calculate the maximum interference. We further derive a directional collision avoidance model and based on the integrated interference/collision model and signal to interference (SIR) requirements, we present the maximum end-to-end throughput under the maximum interference. We further investigate the effect of collision on the energy consumption and propose an energy consumption model that utilize all aspects of energy wastage.

Higher throughput gains and prolonged life time can be achieved for mobile ad hoc networks (MANET) with nodes equipped with directional antennas. The employment of directional antennas can enhance the spatial reuse by allowing concurrent communications to occur within the same vicinity. Another advantage of directional antennas is the higher gain resulted from its directivity, which can be utilized to reduce the transmission power during a directional transmission. In order to maximize the throughput and energy gains from directional antennas, we propose in this paper a transmission power control scheme for directional medium access protocol (MAC) protocols. The proposed scheme can be integrated to any directional MAC protocol that adopts a single channel for transmission and reception of IEEE 802.11 frames. The proposed power control scheme exploits the temporal directional transmission power correlations that exist between the IEEE 802.11 frames (RTS/CTS/DATA/ACK) for successful communication. Simulation results for different topologies are used to demonstrate the significant throughput and energy gains that can be obtained under the investigated scheme.

Abstract This paper describes a new vulnerability for the IEEE 802.11 protocol and studies its impact on degrading the performance of ad hoc networks. A host that exploits this new simple, but practical, vulnerability could cause devastating effects on the proper operation of the network protocols and hence severely degrade the performance. In this work, a misbehaving node fully cooperates by forwarding packets for other nodes and completely adheres to the proper selection of backoff intervals; however, it maliciously forces the forwarding operation to fail in order to either disrupt the route discovery process or to cause damage to existing flows routed though that node. As a result, the medium around the misbehaving node will be less congested and hence the node will obtain an increased unfair access to the channel. We use network simulations to show that such malicious misbehaviors have devastating effect on demoting the network performance and disrupting the protocol functioning. Hence, necessary extensions for existing detection systems are required to mitigate the effects of these new vulnerabilities. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract We consider the problem of localized energy aware routing in mobile ad hoc networks. In localized routing algorithms, each node forwards a message based on the position of itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we propose new localized energy aware routing algorithms called OLEAR. The algorithms have very high packet delivery rate with low packet forwarding and battery power consumption. In addition, they ensure good energy distribution among the nodes. Finally, packets reach the destination using smaller number of hops. All these properties make our algorithm suitable for routing in any energy constrained environment. We compare the performance of our algorithms with other existing energy and non‐energy aware localized algorithms. Simulation experiments show that our algorithms present comparable energy consumption and distribution to other energy aware algorithms and better packet delivery rate. Copyright © 2006 John Wiley & Sons, Ltd.

Ethernet Passive Optical Networks (EPONs) are considered the most promising solutions for upgrading the cur- rent congested access networks to enable the delivery of broad- band integrated services. Although current EPON architectures are economically feasible, they are however bandwidth limited. In this paper, we discuss a simple upgrade architecture from EPON to WDM-PON. We present various Dynamic Wavelength and Bandwidth Allocation algorithms (DWBAs) that exploit both inter-channel and intra-channel statistical multiplexing in order to achieve good performance. We use extensive simulation experiments to validate our reasoning. I. INTRODUCTION

The IEEE 802.16 WirelessMAN standard provides a comprehensive quality-of-service (QoS) control structure to enable flow Isolation and service differentiation over the common wireless network Interface. By specifying a particular set of service parameters, the media access control (MAC) mechanisms defined in the standard are capable of offering service guarantees on the connection basis. However, the design of efficient, flexible and yet bandwidth-saving scheduling algorithms for such QoS provisioning still remains an open topic. This paper proposes a new distributed QoS control scheme that guarantees particular service parameter settings for both uplink and downlink connections. Detailed simulation experiments are presented to study the performance and to validate the effectiveness of the proposed algorithm.

The emerging IEEE 802.11e standard is expected to provide service differentiation and resource allocation for various types of real-time traffic. To support the transmission of voice and multimedia data with performance guarantees, it is crucial to design efficient algorithms for admission control and resource allocation. Several methods have been proposed. However, most of these proposed methods may not be efficient because they assign channel access parameters (CAPs) according to the access category (AC) a flow is mapped into rather than based on the absolute QoS requirements of the flow. Using simulations we highlight the shortcomings of current admission control methods and accordingly we propose a flow-based service differentiation mechanism, which select Channel Access Parameters (CAPs) based on each traffic QoS requirements.

In this paper, we consider the problem of detecting intrusions initiated by cooperative malicious nodes in infrastructure-based networks. We achieve this objective by sampling a subset of the transmitted packets, between each intruder and the victim, over selected links or router interfaces. Here, the total sampling rate on all links must not exceed the sampling budget constraint. We build a game theoretic framework to model distributed network intrusions through multiple malicious nodes and a common victim node. To the best of our knowledge, there has not been any study for the case where the attack is distributed over cooperative intruders using game theory. Non-cooperative game theory is used to formally express the problem, where the two players are: (1) the intruders and (2) the intrusion detection system. Our game theoretic framework will guide the intruders to know their attack strategy and the IDS to have an optimal sampling strategy in order to detect these intrusion packets.

Ethernet passive optical networks (EPONs) are being designed to deliver multiple services and applications, such as voice communications (VoIP), standard and high-definition video (STV and HDTV), video conferencing (interactive video) and data traffic access network. However, most of the current work focuses on inter-ONU dynamic bandwidth allocation (DBA) algorithms. In this paper, we concentrate on the intra-ONU bandwidth allocation for different classes of services. We present a new intra-ONU scheduling scheme based on the Deficient Weighted Round Robin (DWRR) scheduling to achieve adaptive fairness among different classes of services. We validate our reasoning by measuring both the end-to-end delay of different traffic along with the jitter performance of high priority traffic using extensive simulation experiments.

Advances in next-generation SONET/SDH technologies have enabled many new service provisioning paradigms. A key addition is the inverse multiplexing feature which enables the splitting of connection demands across SONET/SDH domains. This paper presents a novel tiered survivability scheme that leverages this feature to support multiple levels of service survivability and achieve higher load carrying capability and service resiliency. Detailed simulation performance analysis results are also presented along with conclusions and directions for future work.

MAC layer misbehavior due to selfish or malicious reasons can significantly degrade the performance of mobile adhoc networks. Currently, detection systems for handling selfish misbehavior has been proposed and studied. In this paper we study a new class of malicious misbehaviors that causes transmission timeout of MAC frames at either the transmitter side or the receiver side. A misbehaving node fully cooperates by forwarding packets for other nodes and completely adheres to the proper selection of backoff intervals; however, it maliciously forces the forwarding operation to fail in order to either disrupt the route discovery process or cause damage to the existing flows routed through itself. We design and implement a new detection system that identifies the malicious nodes through a set of monitoring and reaction procedures. Once a misbehaving node is detected, the system reacts, by adapting simple protocol parameters, to mitigate the negative effects. We describe the detection system and the different reaction procedures for different misbehaviors. We evaluate through network simulation the effectiveness of the system in detecting malicious nodes and improving the network performance.

We consider the problem of localized energy aware routing in mobile ad hoc networks. In localized routing algorithms, each node forwards a message based on the position of itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we propose a new localized energy aware routing algorithm called OLEAR. The algorithm shows a high packet delivery rate with low packet forwarding and battery power consumption. In addition, it ensures a good energy distribution among the nodes and packets reach their destinations using smaller number of hops. All these properties make our algorithm suitable for routing in any energy constrained environment. We compare the performance of OLEAR with existing energy and non energy aware localized algorithms. Simulation experiments show that OLEAR presents comparable energy consumption and distribution to other energy aware algorithms and better packet delivery rates.

We propose a novel ring-based local access Passive Optical Network (PON) architecture that addresses some of the limitations of current tree-based PON. Specifically, we propose a simple ring-based Ethernet PON (EPON) architecture with a fully distributed control plane among the ONUs that supports a truly shared LAN capability among end users as well as upstream access to the central office. This architecture is well suited for an autonomous access environment such as a university campus or a private corporation where several buildings are closely dispersed within a 0.5-1 km diameter area. Unlike a typical ring-based PON topology in which Optical Line Terminal (OLT) and Optical Network Units (ONUs) are interconnected via a long fiber ring, under the proposed architecture, ONUs are interconnected via a short distribution fiber ring in the local loop but share the standard trunk feeder fiber for long reach connectivity to the Central Office (CO).

This paper investigates the problem of survivable traffic grooming (STG) in shared mesh optical networks and proposes different frameworks for improving the survivability of low speed demands against multiple near simultaneous failures. Capacity reprovisioning has recently been considered for improving the overall network restorability in the event of multiple failures by allocating protection resources after a failure to unprotected and vulnerable connections. In this paper we propose two different reprovisioning schemes (lightpath level reprovisioning, LLR, and connection level reprovisioning, CLR). Each of these schemes is suitable for a different survivable grooming policy. While LLR provides collective reprovisioning of connections at the lightpath level, CLR reprovisions spare bandwidth for lower speed connections instead. We study the performance of these schemes under two grooming policies (PAL and PAC), and we show that while CLR reprovisions substantially more connections than LLR, CLR yields a much better network robustness to near simultaneous failures due to its superior flexibility in using network resources.

In this paper we build a game theoretic framework to model network intrusions through multiple packets. Detection is accomplished by sampling a portion of the packets transiting through selected network links (or router interfaces). Given a total sampling budget, our work then aims at developing a network packet sampling strategy to effectively reduce the success chances of an intruder. We consider the scenario where a well informed intruder divides his attack over multiple packets in order to increase his chances of successfully intruding a target domain. Each fragment of the attack is transmitted through a different path using multi-path routing, where each path is selected with a different probability. To the best of our knowledge, there has not been any work done for the case where the attack is split over multiple packets using game theory. We formulate the game theoretic problem, and develop optimal sampling schemes.

We propose a two-step method to enhance the dual-failure restorability in p-cycle protected mesh networks that are optimally designed to withstand only single failures. Our two-step method relies on finding alternate routes to repair the non functional p-cycles upon the first failure and protect the exposed spans. We then compare our proposed method with the cycle reconfiguration method proposed in [9] in which the spare capacity is reconfigured dynamically (either incrementally or completely) after the first failure. We find out the additional spare capacity required for dual failure restorability as compared to single failure restorability for our proposed method as well as the incremental and complete cycle reconfiguration methods. We conclude our findings based on our results and discuss the advantages and disadvantages of each of these methods.

Ethernet Passive Optical Network (EPONs) are currently being designed to deliver multiple services and applications, such as voice communications (VoIP), standard and highdefinition video (STV and HDTV), video conferencing (interactive video) and data traffic access network. The emergence of new bandwidth intensive applications and the continuous demand for more bandwidth in a bandwidth limited EPON require an upgrade from current TDM to WDM-based PON which is currently of huge interest in both the academia and industry. In this paper we propose three new Dynamic Bandwidth Allocation (DBAs) schemes for QoS support in WDM-based PON networks. These schemes can comply with any ONU architecture (tunable lasers or multiple fixed transceivers). However, they vary in their performances (i.e. different jitter, delay, bandwidth utilization etc.). We study the performance of these DBAs using extensive simulation experiments.

One of the benefits of web services is their ability to participate in a web services composition process. Therefore, an end-to-end QoS infrastructure should be established. Work conducted in this domain is mainly focused on functional QoS requirements such as service response time, delay, cost, etc. In this paper, we target QoS from the prespective of data freshness and accuracy. Therefore, we propose the usage of the WS-Notification specification as a base medium capable of sensing and routing any information change at the level of web services using a publish-subscribe mechanism. We then propose an algorithm that is capable of identifying the point of information change within the context of multiple web services composition scenario. This is then followed with an appropriate re-computation of a subset of the pre-established, global service execution plan. Our contributions are three fold: first we highlight the importance of qualifyable QoS aspect related to the issue of web services composition and monitoring, second we describe an algorithm capable of capturing and reflecting the state of web services involved in the integration process, and finally we illustrate the usage of WS-Notification to aid in building such systems.

We present Predictable Random Backoff (PRB) algorithm that is capable of mitigating the impacts of selfish hosts on well-behaved hosts in wireless local area networks (WLAN) and mobile ad hoc networks (MANETs). Here, hosts fail to follow the operation of PRB are easily detected. We present an accurate analytical model to compute the system throughput using a three-dimensional Markov chain and evaluate the performance of PRB under both normal case and selfish case. PRB and BEB (Binary Exponential Backoff) perform similarly in the former case. However, PRB can effectively mitigate the impacts of MAC selfish misbehavior and guarantee a fair share of the wireless channel for well behaved hosts.

Security is a fundamental prerequisite for network survivability and reliability in mobile ad hoc networks (MANET). In the presence of selfish nodes that disobey the standard, the performance of well-behaved nodes will significantly degrade. In this paper, we focus on identifying potential threats in medium access control (MAC) layer introduced by selfish nodes, especially "smart" attack strategies that can defeat the existing detection and reaction systems against MAC layer selfish misbehavior. Furthermore, we propose predictable random backoff (PRB) algorithm that is capable of mitigating the impact of these vulnerabilities. PRB is based on minor modification of IEEE 802.11 binary exponential backoff (BEB) and forces each node to generate "predictable" random backoff intervals. Via computer simulations, we show that PRB is fairly efficient in ensuring reasonable throughput for well-behaved flows in the presence of selfish flows

The emerging IEEE 802.11e standard for wireless local area networks (WLANs) has been proposed to support quality of service (QoS) by assigning different channel access parameters (CAPs) to different access categories (ACs). As an important part of QoS, an admission control scheme is required to maximally utilize the wireless medium resources and to efficiently admit the upcoming real time traffic while not compromising the QoS of existing traffic. In this paper, we propose a novel admission and congestion control scheme which obtains the admission control parameters through existing analytical model and traffic QoS requirements. It then dynamically updates the CAPs based on periodical monitoring of current channel conditions. Through numerical analysis and extensive simulation, results show that such a scheme could provide the guaranteed QoS for admitted real-time traffic in terms of guaranteed throughput achievement, bounded maximum delay and bounded maximum dropping rate while maintaining good channel utilization

We study the unavailability of end-to-end traffic in p-cycle based mesh networks, which are designed to protect against single link failures. It has been shown earlier by Grover and Clouqueur that the p-cycle length as well as its topology play a vital role in determining the availability of span(s) which are protected by the p-cycle. Similarly, we derive the relationship between the unavailability of a span(s) and the topology of the p-cycles) which is allocated for the restoration of the span(s). Based on these insights and on the fact that the end-to-end unavailability of a working path depends not only on the length of the restoration path but also on the number of spans along the working path, we try to design a method for allocating p-cycles such that the end-to-end unavailability is bounded by an upper limit and the upper limit can be varied as desired. As expected, results show that more capacity is required to guarantee a lower end-to-end unavailability. Our results also show that shorter service paths tend to use longer p-cycles than longer service paths, to obtain the same level of availability; this is expected since the path length, apart from the p-cycle length, also plays a role in determining the availability of the service path. We compare this formulation with a formulation which rather limits the hop count of candidate p-cycles to provide a lower end-to-end unavailability. We notice that directly limiting the end-to-end unavailability, as proposed by this paper, gives better results in terms of spare capacity redundancy than limiting the hop count of p-cycles. That is because the former allows shorter working paths to use p-cycles with higher hop count and therefore a better utilization of the allocated spare capacity

Ethernet passive optical networks (EPONs) have emerged as a promising candidate for next-generation broadband access networks. As this technology evolves, the development of efficient dynamic bandwidth allocation (DBA) algorithms has become a key concern. This paper presents the principle and implementation issues of a new DBA scheme. Through detailed analyses some general investigations on the fairness issue and statistical bandwidth multiplexing (SBM) mechanisms existing in EPON are developed. This proposed scheme consistently maintains a robust fairness mechanism in the DBA operation. With the better maintained fairness mechanism, efficient SBM persists at the global level, whereas multiple network performance matrices are improved. Detailed simulation experiments are presented to study the performance and to validate the effectiveness of the proposed algorithm.

A key means for improving service availability in optical networks is the ability to recover from dual near-simultaneous failures. To date, most studies have focused on improving survivability for single failure events either via proactive or reactive mechanisms. Conversely, lightpath re-provisioning has only recently been considered as an alternative for improving overall network restorability in the event of dual failures. Here, post-failure re-provisioning establishes new backup capacities for unprotected connections in advance of a second failure. In this paper we propose a new re-provisioning algorithm to improve the restorability of shared optical networks and compare its performance with a conventional scheme. Namely, the proposed algorithm only requires re-provisioning of a fraction of unprotected connections, since upon re-routing onto shared backup capacity, some connections are only in a "temporarily unprotected" state. Through simulation analysis, we demonstrate the effectiveness of the proposed approach in improving optical network restorability with and without wavelength conversion.

Ethernet passive optical networks (EPONs) have emerged as one of the most promising access network technologies. Propelled by rapid price declines in fiber optics and Ethernet components, these architectures combine the latest in optical and electronic advances and are poised to become the dominant means of delivering gigabit broadband connectivity to homes over a unified single platform. As this technology matures, related quality of service (QoS) issues are becoming a key concern. This paper proposes a novel dynamic scheduling algorithm, termed hybrid granting protocol (HGP), to support different QoS in EPON. Specifically, the proposed dynamic scheduling algorithm minimizes packet delay and jitter for delay and delay-variation sensitive traffic (e.g., voice transmissions) by allocating bandwidth in a grant-before-report (GBR) fashion. This considerably improves their performance without degrading QoS guarantees for other service types. Detailed simulation experiments are presented to validate the effectiveness of the proposed algorithm.

We consider the problem of energy aware localized routing in ad hoc networks. In localized routing algorithms, each node forwards a message based on the position information about itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we extend our previous work on randomized localized routing algorithms that achieve high packet delivery rates and show that they have good overall power consumption. We present two different variants of energy aware randomized routing, namely greedy and compass, and we study their performance using different cost metrics (e.g., forwarding power, remaining node energy, or a combination of both). We study their performance experimentally on different topologies and compare it with other existing algorithms. Our simulation results show that energy aware randomized algorithms achieve superior packet delivery rates and moderate energy consumption.

In a mobile ad hoc network (MANET), adversaries could compromise network functionality by attacking the physical layer, the MAC layer, or the network layer. Significant research efforts have been made towards increasing the survivability of MANET either by developing secure routing algorithms or by improving the robustness of MAC layer protocol in the presence of selfish or compromised nodes. In addition, some recent studies have focused on quantifying the resiliency of MANET against MAC layer misbehaviors; however little work has been done on quantifying the impact of these misbehaviors on the performance of ad hoc routing protocols. In this paper, we review some of MANET link layer vulnerabilities, we study their impacts on network layer performance and compare the performance of two prominent on-demand routing protocols (AODV and DSR). We show that simple attacks on the link layer could propagate to the upper network layer and disrupt the routing mechanism, therefore causing devastating effects on the overall network performance. Although several secure ad hoc routing protocols have been proposed, none of them are resilient to MAC misbehaviors.

Ethernet passive optical networks (EPONs) have emerged as the one of the most promising candidates for next-generation access networks. These new architectures couple low-cost optics with advanced edge electronics to offer vastly improved scalability over competing digital subscriber line and cable modem offerings. This paper proposes several novel architectural enhancements for EPON, which will help increase the viability of optical access over a broader range of subscriber access scenarios. Specifically, this paper proposes a two-stage EPON architecture that allows more end-users to share an optical line terminal link, and enables longer access reach/distances (beyond the usual 25 km distance). In addition, a new dynamic bandwidth allocation (DBA) algorithm is proposed to effectively allocate bandwidths between end users. This DBA algorithm can support differentiated services in a network with heterogeneous traffic. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed architecture and algorithms.

Ethernet passive optical networks (EPONs) have emerged as a well accepted candidate for next-generation access networks. Propelled by rapid price declines in fiber optics and Ethernet components, these new EPON architectures combine the latest in optical and electronic advances, and are poised to become the dominant means of delivering bundled services over a single platform. This paper proposes a novel EPON architecture capable of delivering bandwidth-intensive voice, data video services at distances beyond 25 km in the subscriber access network. Specifically, this paper proposes a two-stage EPON architecture that allows more end-users to share an OLT link, and enables longer access reach/distances (beyond the usual 25 km distance). In addition, a new dynamic bandwidth allocation (DBA) algorithm is proposed to effectively and fairly allocate bandwidths between end users. This DBA algorithm can support differentiated services in a network with heterogeneous traffic. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed architecture and algorithms.

Optical dense wavelength division multiplexing (DWDM) has yielded unprecedented levels of bandwidth scalability. In order to exploit these gains, new converged multiservice transport setups have been evolved, most notably under the multiprotocol label switching (MPLS) and generalized MPLS (GMPLS) frameworks. These paradigms offer very efficient data-optical integration and enable a host of new service capabilities. As operators deploy these new technologies, the provisioning of "subwavelength" demands over wavelengths has become a crucial requirement, i.e., traffic engineering/grooming. This work addresses data-optical grooming in converged GMPLS networks. Here, novel integrated constraint-based routing algorithms are developed to provision subwavelength demands at both packet-switching and lightpath routing levels. Simulations indicate notable performance gains and resource efficiencies with the proposed schemes.

We investigate the problem of grooming dynamic multicast traffic in WDM mesh networks. This problem is equivalent to designing a light-tree based logical topology for multicast streams. It consists of four subproblems, namely routing, wavelength assignment, design of a light-tree based logical topology, and traffic-grooming. We develop different routing schemes to efficiently groom low-speed connections on the light-tree based logical topology. Numerical results demonstrate that the proposed approaches use the network resources more efficiently compared to the nongrooming approach and the approach of serving the multicast requests as separate unicast requests. Moreover, amongst the proposed techniques, the logical-first multihop grooming scheme MC-MHl outperforms all other schemes in terms of blocking probability and performance gain.

Ethernet-based passive optical network (EPON) technology is being considered as a promising solution for next generation broadband access networks due to the convergence of low-cost Ethernet equipment and low-cost fiber infrastructure along with its ability to support successfully IP-based multimedia applications with quality of service requirements. We propose to use the multi-point control protocol (MPCP) defined in the IEEE 802.3ah task force to arbitrate the transmission of different users; dynamic bandwidth allocation (DBA) algorithms are then presented to allocate bandwidths between these users effectively and fairly. These DBA algorithms are then augmented to support differentiated services in a network with heterogeneous traffic. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed protocols.

Efficient path selection combined with fast restoration algorithms is a key requirement for designing shared restorable mesh networks. In this paper we first discuss a distributed path selection algorithm for efficient routing of restorable connections in optical networks. This approach relies on the knowledge of global information, maintained at each node, to determine link sharability and compute optimal shared paths; we compare its performance to another protocol [C. Assi et al., 2002] that only requires the knowledge of local resource usage. Second, we study the network's ability to recover from single element failures in a shared mesh network and we propose a new restoration algorithm for rapid recovery upon a failure. The significant contribution of this algorithm is that the network restoration time is independent of the protection path length (i.e., the effect of propagation delay is eliminated) as well as the accumulation of the switch configuration times. We evaluate the performance of these protocols through simulation experiments.

The efficient use of network capacity strongly depends upon the path selection procedure. In this paper we propose and evaluate efficient path selection algorithms for survivable optical networks with shared protection. Two distributed path selection algorithms are presented. The first approach maintains global information on network resource usage to determine link sharability and compute the optimal shared paths. The second approach, however, 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.

Ethernet-based passive optical network (EPON) technology is being considered as a promising solution for next-generation broadband access networks due to the convergence of low-cost Ethernet equipment and low-cost fiber infrastructures. A major feature for this new architecture is the use of a shared transmission media between all users; hence, medium access control arbitration mechanisms are essential for the successful implementation of EPON: i.e., to ensure a contention-free transmission and provide end users with equal access to the shared media. We propose to use the multipoint control protocol defined by the IEEE 802.3ah task force to arbitrate the transmission of different users, and we present different dynamic bandwidth allocation (DBA) algorithms to allocate bandwidths effectively and fairly between end users. These DBA algorithms are also augmented to support differentiated services, a crucial requirement for a converged broadband access network with heterogeneous traffic. We show that queueing delays under strict bandwidth allocation algorithms result in an unexpected behavior for certain traffic classes, and we suggest the use of DBA with appropriate local queue management to alleviate this inappropriate behavior. We conduct detailed simulation experiments to study the performance and validate the effectiveness of the proposed protocols.

This paper presents a novel hybrid distributed fault-management protocol for combating single-fiber failures in mesh-based DWDM optical networks. The proposed hybrid approach combines Link State Protocol to disseminate and update information only about the physical connectivity of the network and a distributed local information-based signaling algorithm for connection management. The purpose of using a hybrid approach is two advantages: (1) reducing the signaling overhead associated with the global information-based link state protocol by using a distributed approach where only local information is maintained at each node; and (2) eases the implementation of the routing protocol where physical constraints, such as link/node diversity, are imposed. The performance of the proposed hybrid approach is evaluated via comparing the dedicated-path protection and the shared-path protection schemes in terms of blocking probability, restoration time under failure assumption, and data loss incurred during the recovery phase.

This paper investigates and compares the performance of two generalized multiprotocol label switching (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 (routing and wavelength assignment) algorithm and a signaling algorithm. Two triggering mechanisms for LSA (link state advertisement) update procedures are considered; one is a periodically-based update and the other is a threshold-based update. The second protocol is a local-information based fixed alternate link routing approach where the signaling protocol is closely integrated with the RWA protocols.

The purpose of this work is to show the benefits gained by dynamically provisioning low-rate traffic streams at the IP/MPLS layer in future IP-centric WDM-based optical networks. First, several low-rate data flows are statistically multiplexed (groomed) onto one wavelength at the IP/MPLS router. Then, conventional dynamic lightpath provisioning schemes at the physical WDM layer, where the bandwidth of a connection request is assumed to be a full wavelength capacity, are extended to allow the provisioning of "sub-lambda" connection flow requests at the IP/MPLS layer. In this work, provisioning a connection request implies that a flow of data is successfully routed if both an active path and another alternate link and node-disjoint backup path are setup at the same time.

This paper proposes two new distributed signaling protocols for fast automatic setup and tear-down of paths across the emerging interconnection models for IP-over-optical-networks. The first scheme is probe flooding-based routing (PFBR) algorithm with backward reservation while the second scheme is based on an adaptive routing algorithm called multi-path routing (MPR) where k paths are probed simultaneously. Our objective in developing these protocols is twofold: first, to simplify the specific signaling protocols or algorithms in the components of MPLS control plane; and second, to adapt the performance optimization algorithm to the requirements of different user applications by having the flexibility to vary the relative weight assigned to each of three performance metrics [call acceptance rate (CAR), call set-up time (CST), and routing distance (RD)].