A spread spectrum-based (SS) technique is utilized to provide physical layer security in elastic optical networks. Specifically, an integer linear program (ILP) formulation is developed that solves the routing and slot allocation problem in conjunction with the spread spectrum technique. Performance results indicate that the proposed technique, in conjunction with signal overlapping that allows the combination of resources used by different connections on the same slots, provides security to the requested connections while at the same time significantly minimizes the additional requirement in network resources.

In the beyond 5G era, AI/ML empowered real-world digital twins (DTs) will enable diverse network operators to collaboratively optimize their networks, ultimately improving end-user experience. Although centralized AI-based learning techniques have been shown to achieve significant network traffic accuracy, resulting in efficient network operations, they require sharing of sensitive data among operators, leading to privacy and security concerns. Distributed learning, and specifically federated learning (FL), that keeps data isolated at local clients, has emerged as an effective and promising solution for mitigating such concerns. Federated learning poses, however, new challenges in ensuring fairness both in terms of collaborative training contributions from heterogeneous data and in mitigating bias in model predictions with respect to sensitive attributes. To address these challenges, a fair FL framework is proposed for collaborative network traffic prediction and resource allocation. To demonstrate the effectiveness of the proposed approach, noniid and imbalanced federated datasets based on real-word traffic traces are utilized for an elastic optical network. The assumption is that different optical nodes may be managed by different operators. Fairness is evaluated according to the coefficient of variations measure in terms of accuracy across the operators and in terms of quality-of-service across the connections (i.e., reflecting end-user experience). It is shown that fair traffic prediction across the operators result in fairer resource allocations across the connections.

Rapid urbanization trends have resulted in a significant increase in global urban population over the past few decades. This growth is projected to continue, further exacerbating traffic congestion in road networks. Urban air mobility presents a promising solution to alleviate this congestion, offering substantial environmental, economic, and societal benefits. However, to fully realize these benefits, autonomous flight and automation are crucial for safely managing the high density of aircraft expected in urban airspaces.To address this challenge, this work proposes an innovative model predictive control (MPC) framework for generating ondemand, safe 3D trajectories for fleets of unmanned aerial vehicles (UAVs) operating in dense urban environments. Specifically, it introduces a near real-time MPC method designed to enhance the airspace’s capacity to accommodate an increasing number of flights within a confined area, while consistently adhering to stringent safety standards. An extensive simulation study is conducted to demonstrate the effectiveness of the proposed framework in planning safe, on-demand trajectories in complex urban settings.

Unmanned aerial vehicles (UAVs) are increasingly relied upon in a variety of critical applications, including infrastructure inspection, search-and-rescue, and traffic monitoring. While modern UAVs are typically equipped with global positioning system (GPS), inertial measurement unit (IMU) modules, and often include safeguards against adverse environmental conditions, they remain susceptible to sensor malfunctions and signal disruptions. These challenges have led to the need for robust, GPS-free solutions capable of maintaining accurate trajectory prediction and state identification. This work proposes a real-time multi-task learning framework for UAVs that employs Transformer-based neural networks to perform simultaneous trajectory prediction and state identification. The proposed system enables GPS-free UAV operations with the employment of a feature-based visual odometry algorithm that is implemented and fused with telemetry data to achieve accurate localization. The proposed system is implemented (hardware and software modules) in a functional prototype and validated through extensive outdoor experiments using a custom-built dataset, demonstrating strong performance and improved prediction accuracy in GPS-denied environments. The results demonstrate the framework’s ability to enhance the autonomy and reliability of UAV systems in challenging operational scenarios.

In recent years, emergency response systems have been employed extensively in critical operations, particularly for disaster management scenarios. During these operations, robust and efficient communication is crucial to ensure that first responders can obtain critical information for coordination and adaptation to the dynamic conditions of an emergency. The employment of unmanned aerial vehicles (UAVs) in conjunction with Internet of Things (IoT) devices can provide support and enable the transmission and reception of relevant information in emergency situations. A number of different communication methods have been explored to address communication performance and robustness issues that arise during emergencies. This work tackles these issues by developing and evaluating the performance of an integrated LoRa (long-range communication)-based system for UAV-to-X communications. For evaluation purposes, a LoRa-based prototype system is designed and implemented to achieve robust, real-time, and efficient communication for both static and mobile nodes in indoor and outdoor environments. The prototype of the proposed communication architecture is subsequently tested in a real-world environment, demonstrating the feasibility and effectiveness of the proposed solution in terms of communication range, transmission latency and security for the applications investigated.

In this work, the network coding (NC) technique is used in combination with routing and spectrum allocation (RSA) to establish confidential connections in elastic optical networks and protect these connections against eavesdropping attacks. Utilizing NC, the signals of the confidential connections are encrypted using other signals at different nodes in their path while transmitted through the network, preventing an eavesdropper tapping a link to gain access to confidential information. A novel mixed integer linear program (MILP) is proposed to enable encrypted transmission of confidential connections, in combination with a mapping function that maximizes the security level provided for the largest possible set of confidential connections. Further, a heuristic approach is presented for the combined routing, spectrum, and network coding assignment (RSNCA) problem, along with a metaheuristic, for larger-sized networks. The proposed approaches are examined in terms of confidentiality, average number of encryption operations (EOs), spectrum utilization, and running times. Performance results demonstrate the applicability of the MILP formulations in providing optimal lightpath establishments that maximize physical layer security, with the proposed MILP-RSNCA approach achieving the best results in terms of the level of security and the average number of EOs provided, albeit utilizing more spectrum slots. Further, the metaheuristic exhibits results comparable to the MILP in terms of providing security, and can be also applied to large-size networks. In some scenarios, it can even provide the highest available level of security [i.e., a minimum of 7 EOs per link per demand] for more than 50% of the confidential demands.

Current research on robust trajectory planning for autonomous agents aims to mitigate uncertainties arising from disturbances and modeling errors while ensuring guaranteed safety. Existing methods primarily utilize stochastic optimal control techniques with chance constraints to maintain a minimum distance among agents with a guaranteed probability. However, these approaches face challenges, such as the use of simplifying assumptions that result in linear system models or Gaussian disturbances, which limit their practicality in complex realistic scenarios. To address these limitations, this work introduces a novel probabilistically robust distributed controller enabling autonomous agents to plan safe trajectories, even under non-Gaussian uncertainty and nonlinear systems. Leveraging exact uncertainty propagation techniques based on mixed-trigonometric-polynomial moment propagation, this method transforms non-Gaussian chance constraints into deterministic ones, seamlessly integrating them into a distributed model predictive control framework solvable with standard optimization tools. Simulation results demonstrate the effectiveness of this technique, highlighting its ability to consistently handle various types of uncertainty, ensuring robust and accurate path planning in complex scenarios.

This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. While current interception technologies focus mainly on the jamming/spoofing tasks, the challenges of effectively locating and tracking rogue drones have not received adequate attention. Solving this problem and integrating with recently proposed interception techniques will enable a holistic system that can reliably detect, track, and neutralize rogue drones. Specifically, this work considers a team of pursuer UAVs that can search, detect, and track multiple rogue drones over a sensitive facility. The joint search and track problem is addressed through a novel multiagent reinforcement learning scheme to optimize the agent mobility control actions that maximize the number of rogue drones detected and tracked. The performance of the proposed system is investigated under realistic settings through extensive simulation experiments with varying number of agents demonstrating both its performance and scalability.

Charging electric vehicles (EVs) within parking infrastructures with a small number of charging spots is a problem that needs addressing in order to facilitate the wide adoption of EVs. Fairness and efficiency during the charging process are two important parameter that are examined in this work for this specific use case. The α-fair and fair-enough approaches are utilized, demonstrating that for the EV charging scenario considered the fair-enough approach is better applicable for practical applications, as it does not have the computational complexity of the α-fair scheme by achieving the fairest energy resource allocation possible prior to the system efficiency starts degrading. Specifically, it is demonstrated that the fair-enough scheme approximates the α-fair allocation, while also reducing processing time by up to 94%.

The wide adoption and use of unmanned aerial vehicles (UAVs) has created not only opportunities but also threats to the security of sensitive areas. Thus, effective and efficient counter-drone systems are required to protect these areas. This work tackles this issue by developing cooperative multi-agent jamming techniques using reinforcement learning (RL) to counter the operation of one or multiple rogue drones flying over a sensitive area. The aim of the proposed RL approach is to optimize the joint mobility and power control actions of the pursuer UAVs in order to maximize the received jamming power at the rogue drones aiming at disrupting communication links and sensing circuitry, while at the same time keeping the interference to surrounding pursuer agents below a predefined threshold. The effectiveness of the proposed approach in terms of scalability, learning speed, and agents' final joint performance is demonstrated through extensive simulation experiments for various agent and target configurations.

Traditionally, the bandwidth allocation problem is solved by maximizing network efficiency, which may however leave some connections unserved. This clearly leads to an unfair solution from the user’s point of view, rendering fair bandwidth allocation algorithms of paramount importance, especially in the presence of congested network links. Specifically, fair bandwidth allocation algorithms are necessary to effectively control the achievable quality-of-service (QoS) of end-users, or equivalently to effectively control the fairness of the bandwidth allocation decisions. As the fair bandwidth allocation problem has long concerned network operators, various measures of fairness have been proposed. Amongst the most widely applied measures is the$\alpha $-fair scheme that also captures proportional and max-min fairness by appropriately tuning the inequality aversion parameter$\alpha $. Even though this scheme allows a network operator to control the arising fairness-efficiency trade-off, the extensive processing time required for iterating over several$\alpha $-fair solutions often hinders its applicability. Committing, instead, to a single measure of fairness (e.g., proportional or max-min fairness) allows to fast approximate a fair bandwidth allocation but this may lead to either conservative QoS fairness levels or to a heavily degraded system efficiency. To alleviate limitations of known fairness measures, this work proposes a multi-objective optimization function that simultaneously optimizes both QoS fairness and network efficiency, aiming to derive an allocation that is as fair as possible to the extent that network utilization is not degraded for the sake of fairness. To evaluate the performance of the proposed function an optical network environment is considered where connections contend for the spectrum resources, demonstrating that the proposed optimization function significantly outperforms the$\alpha $-fairness scheme in terms of processing time by up to 95% and its special cases in terms of fairness and system efficiency, thus alleviating also the limitations of committing to a single measure of fairness.

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform’s user interface the location traces and associated images collected during a real field exercise.

Connected and Autonomous Vehicles (CAV) depend on satellite systems, such as the Global Positioning System (GPS), for location awareness. Location data are streamed in real-time to the CAV’s perception engine from its onboard GPS receiver for autonomous driving and navigation. However, these receivers are vulnerable to location spoofing attacks that can be easily launched using Commercial-Off-The-Self (COTS) equipment and open-source software. Existing data-driven attack detection solutions typically require data associated with ‘normal’ and ‘attack’ labels. The latter are hard to collect in operational conditions or even in controlled experiments. To this end, we formulate the GPS location spoofing attack detection as an outlier detection problem. The proposed solution based on Machine Learning (ML) relies solely on normal location data for training during attack-free operation. Our solution demonstrates more than 98% detection accuracy according to standard metrics on realistic data produced with the CARLA driving simulator and outperforms by 15% another (non ML-based) state-of-the-art solution.

This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles.

Electric vehicle routing problems are challenging variations of the traditional vehicle routing problem which incorporate the possibility of electric vehicle (EV) recharging at any station, while satisfying the delivery demands of customers. This work addresses the recently formulated capacitated vehicle routing problem (E-CVRP) with variable energy consumption rate. In particular, the cargo weight, which is one of the main factors affecting the energy consumption rate of EVs, is considered (i.e., the heavier the EV the higher the rate). As a solution method, an ant colony optimization algorithm with a local search heuristic is developed. Experiments are conducted on a recently generated benchmark set of E-CVRP instances demonstrating that the performance of the proposed technique improves on the best known so far solutions.

With the advent of Connected and Autonomous Vehicles (CAVs) comes the very real risk that these vehicles will be exposed to cyber-attacks by exploiting various vulnerabilities. This paper gives a technical overview of the H2020 CARAMEL project (currently in the intermediate stage) in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal. Most of the possible scenarios are considered, by which an adversary can generate attacks on CAVs, such as attacks on camera sensors, GPS location, Vehicle to Everything (V2X) message transmission, the vehicle's On-Board Unit (OBU), etc. The counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMEL project achieved by implementing the designed security algorithms.

In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon. This enables the triggering of timely repair actions with low quality-of-transmission (QoT) margins before a costly hard-failure occurs, ultimately reducing the frequency of repair actions and associated operational expenses. Specifically, it is shown that the proposed scheme is capable of triggering a repair action several days prior to the expected day of a hard-failure, contrary to soft-failure detection schemes utilizing rule-based fixed QoT margins, that may lead either to premature repair actions (i.e., several months before the event of a hard-failure) or to repair actions that are taken too late (i.e., after the hard failure has occurred). Both frameworks are evaluated and compared for a lightpath established in an elastic optical network, where soft-failure evolution can be modeled by analyzing bit-error-rate information monitored at the coherent receivers.

As the number of attacks increases in optical networks, security at the management layer (ML) of these networks is of paramount importance to ensure that critical network management information is securely transmitted between ML node(s) and the rest of the network nodes. This work considers the problem of developing a quantum key distribution (QKD) framework for the management layer of an optical network, so that a set of management nodes can securely communicate and exchange keys with the network nodes. An integer linear program (ILP) is formulated for the design of a management layer - quantum key distribution (ML-QKD) network, aiming to maximize the transmission rates provided to network and management nodes, by optimally selecting the placement of the management node(s) and QKD systems. Performance results for several scenarios with a different number of available management nodes and QKD systems, demonstrate that the proposed formulation can design an optimal ML-QKD network.

With the rise of the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs), versatile counter-drone systems are becoming a necessity. In this work, a counter-drone system is developed, where a pursuer drone employs algorithms for detecting and tracking a rogue drone, in conjunction with wireless interception capabilities to jam the rogue drone, while also jointly achieving self positioning for the pursuer drone. In the proposed system, a software-defined-radio (SDR) is used for switching between jamming transmissions and spectrum sweeping functionalities to achieve the desired GPS disruption and self-localization, respectively. Extensive field experiments demonstrate the effectiveness of the proposed solution in a real-world environment under various parameter settings.

Distribution system operators (DSOs) are currently deploying telecommunications infrastructures so as to enable the efficient operation of smart grids. A basic part of a DSO's telecommunications network covers the interconnection of electrical substations with the central management system. This work describes a geographic information system (GIS)-based tool for the design of a backbone optical network, deployed specifically to support the DSO's telecommunication requirements. The QGIS open-source software was chosen as a highly suitable platform for the development of all necessary geospatial tools, along with the use of efficient heuristic algorithms, for selecting cost-efficient routes that allow all electrical substations to be reachable from the central management system. This topological design of the DSO's telecommunications network is essential for the deployment of intelligent power distribution networks with increased functionalities currently being implemented by the DSOs.

Intersection crossing represents a bottleneck for transportation systems and Connected Autonomous Vehicles (CAVs) may be the groundbreaking solution to the problem. This work proposes a novel framework, i.e, AVOID-PERIOD, where an Intersection Manager (IM) controls CAVs approaching an intersection in order to maximize intersection capacity while minimizing the CAVs’ gas consumption. Contrary to most of the works in the literature, the CAVs’ location uncertainty is accounted for and CAVs controls are re-optimized periodically, allowing for the optimization of system performance while creating safe trajectories. To improve scalability for high-traffic intersections, an event-triggering approach is also developed (AVOID-EVENT), which reaches a better trade-off among performance and computational and communication complexity. In a realistic simulation scenario, AVOID-EVENT reduces the number of re-optimizations required by 92.2%, while retaining most of the benefits introduced by AVOID-PERIOD.

In this work, the travel path of a set of drones is scheduled across a graph, where the nodes need to be visited multiple times at pre-defined points in time. The nodes can either be demand nodes requesting monitoring, or supply nodes that are used as take-off/landing locations for the drones and for battery replacement to cope with the limited flying range of the drones. This is an extension of the well-known multiple traveling salesman problem and the proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, the monitoring of remote locations to assist search and rescue missions, or the monitoring of critical infrastructure facilities for security and surveillance purposes. Aiming to find the optimal schedule, the problem is initially formulated as an Integer Linear Program (ILP). However, given that the problem is highly combinatorial, the optimal solution scales only for small-size problems. Thus, a greedy algorithm is also proposed that uses a one-step look-ahead heuristic search mechanism, as well as an algorithm that is based on ant colony optimization (ACO). In a detailed evaluation, it is observed that both algorithms achieve near-optimal performance for small settings, while also scaling to larger settings, with the ACO being more suitable for medium-size settings and the Greedy for larger ones. A field experiment is additionally performed to demonstrate the practical implementation of the proposed system under real-world conditions.

To date, a large body of the literature has looked into the problem of autonomous intersection crossings facilitated by Connected Autonomous Vehicles (CAVs). Nevertheless, existing approaches assume that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized intersection management, which considers vehicle location uncertainties for linear-Gaussian systems. Building upon the proposed framework, a family of$0-1$integer linear programming optimizations are presented that can set, sequentially or simultaneously, the acceleration profiles of all vehicles in the intersection. Extensive simulation results are presented, proving that the proposed framework represents a real-time near-optimal approach that maximizes intersection throughput with probabilistic collision avoidance guarantees.

Drones, including remotely piloted aircraft or unmanned aerial vehicles, have become extremely appealing over the recent years, with a multitude of applications and usages. However, they can potentially present major threats for security and public safety, especially when they fly across critical infrastructures and public spaces. This work investigates a novel counter-drone solution by proposing a multi-agent framework in which a team of pursuer drones cooperate in order to track and jam a rogue drone. Within the proposed framework, a joint mobility and power control solution is developed to optimize the respective decisions of each cooperating agent in order to best track and intercept the moving rogue drone. Both centralized and distributed variants of the joint optimization problem are developed and extensive simulations are conducted to evaluate the performance of the problem variants and to demonstrate the effectiveness of the proposed solution.

This work proposes a reward-based framework for fair resource allocation in reconfigurable elastic optical networks (EONs) under modeled traffic demand fluctuations. The routing and spectrum allocation (RSA) problem is formulated based on the maximization of the reward-based constant elasticity social welfare function, with rewards received by connections when allocated spectrum slots, given their a-priori modeled traffic distributions. Connections are essentially contending for link resources, with the connections’ preferences depending on the reward function used for scoring these resources. Two reward functions are utilized, considering different attributes of the optimization problem, according to fairness- and efficiency-related measures. The proposed framework can be used by a network operator for approximating in advance the provisioning of the resources for each connection that best meets predefined targets. Performance results demonstrate that both reward functions result in spectrum allocations where the quality-of-service (QoS) is as similar as possible for all connections, significantly improving connection over- and under-provisioning. Additionally, the reward function that also accounts for the modeled traffic further improves fairness and connection over-provisioning, while incurring only a small penalty in terms of the aggregate unserved traffic for all destinations.

This work proposes a novel distributed control framework in which a team of pursuer agents equipped with a radio jamming device cooperate in order to track and radio-jam a rogue target in 3D space, with the ultimate purpose of disrupting its communication and navigation circuitry. The target evolves in 3D space according to a stochastic dynamical model and it can appear and disappear from the surveillance area at random times. The pursuer agents cooperate in order to estimate the probability of target existence and its spatial density from a set of noisy measurements in the presence of clutter. Additionally, the proposed control framework allows a team of pursuer agents to optimally choose their radio transmission levels and their mobility control actions in order to ensure uninterrupted radio jamming to the target, as well as to avoid the jamming interference among the team of pursuer agents. Extensive simulation analysis of the system’s performance validates the applicability of the proposed approach.

This work examines deep quantile regression for quality-of-transmission (QoT) estimation and accurate decision making in optical networks. Quantile regression is applied to approximate QoT models capable of inferring QoT bounds for any future lightpath, according to a predefined level of certainty, for confident decision making, without the need to consider traditional margins at decision time. It is shown, that quantile regression automatically accounts for such margins, in a discriminative fashion, leading to a significant margin reduction and subsequently to more accurate inference of the QoT of unestablished lightpaths, when compared to the traditional margin-based decision approaches. Specifically, deep quantile regression for QoT estimation ensures that lightpaths with insufficient QoT will be accurately identified and rejected, while also identifying correctly lightpaths with sufficient QoT, making it a confident decision making tool for the planning of optical networks.

Attacks on the GPS receiver of Connected and Autonomous Vehicles (CAV) and specifically GPS location spoofing is of great concern for the automotive industry as the attacker can compromise the security of CAVs leading to serious repercussions for the drivers and pedestrians. Attack detection solutions based on specialized hardware (e.g., antenna arrays) and satellite signal processing techniques are accurate, yet bulky and expensive to mount on CAVs. Thus, lightweight and cost-effective solutions for detecting location spoofing attacks are highly desirable. This work presents an in-vehicle attack detection solution that fuses multi-source data readily available from the CAV's onboard sensors. It can be implemented in software running on cheap embedded computing platforms integrated into the CAV. The proposed solution is validated using the real-time CARLA simulator, while extensive experimental results demonstrate its effectiveness under different attack scenarios.

Connected and Autonomous Vehicles (CAVs) are expected to become a reality on roads in the near future bringing significant social, economic, and environmental benefits. Cooperation and coordination among CAVs will be enabled through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communications. Each vehicle knows its current location and in many cases will have to communicate this information to its associated Roadside Unit (RSU). With the proliferation of CAVs, the RSU is expected to receive and process a large amount of feedback information from its assigned CAVs. Thus, an event-triggered communication scheme is proposed instead of conventional approaches where communication takes place in a periodic manner. Further, the effect of age of information on the resulting accuracy of the vehicle tracking error is taken into account by considering the message queue wait time at the RSU. A family of optimization problems is proposed, used to determine the optimal accuracy threshold for the event-triggered algorithm, so as to minimize the effect of the tracking error caused by the queue wait time.

Trajectory prediction is crucial in assisting both human-driven and autonomous vehicles. Most of the existing approaches, however, focus on straight stretches of road and do not address trajectory prediction at intersections. This work aims to fill this gap by proposing a solution that copes with the higher complexity exhibited for the intersection scenario, leveraging the 5G-MEC capabilities. In particular, the reduced latency and edge computational power are exploited to centrally collect and process measurements from both vehicles (e.g., odometry) and road infrastructure (e.g., traffic light phases). Based on such a holistic system view, we develop a Long Short Term Memory (LSTM) recurrent neural network which, as shown through simulations using a real-world dataset, provides high-accuracy trajectory predictions. The encountered challenges and advantages of the presented approach are analyzed in detail, paving the way for a new vehicle trajectory prediction methodology.

The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by GNSS and thus a combination of positioning and navigation approaches need to be explored. This work investigates how reliable relative positioning can be achieved in GNSS-challenged application scenarios using a combination of signals of opportunity (SOPs), as well as inertial and vision data. The proposed cooperative relative positioning system (CRPS) exploits this data for real-time positioning, and employs a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time without the use of any GNSS information. Experiments conducted in an outdoor setting demonstrate the applicability of the proposed CRPS and its performance against standalone positioning approaches including GNSS.

Severa1 logistic companies started utilizing electric vehicles (EVs) in their daily operations to reduce greenhouse gas pollution. However, the limited driving range of EVs may require visits to recharging stations during their operation. These potential visits have to be addressed, avoiding unnecessary long detours. We formulate the electric capacitated vehicle routing problem (E-CVRP), which incorporates the possibility of EVs visiting a recharging station while satisfying the delivery demands of customers. The energy consumption of the EVs is proportional to their cargo load which is an important constraint in real-world logistics applications. A new set of benchmark instances is proposed for the E-CVRP. As solution methods to these new benchmarks, we apply the ant colony optimization metaheuristic method and an exact method. Experimental results on the ECVRP demonstrate the high complexity of the problem and the efficiency of the applied metaheuristic solution method.

We propose an α-fair routing and spectrum allocation (RSA) framework for reconIlgurable elastic optical networks under modeled tidal trafIlc, that is based on the maximization of the social welfare function parameterized by a scalar α (the inequality aversion parameter). The objective is to approximate an egalitarian spectrum allocation (SA) that maximizes the minimum possible SA over all connections contending for the network resources, shifting from the widely used utilitarian SA that merely maximizes the network efIlciency. A set of existing metrics are examined (i.e., connection blocking, resource utilization, coefIlcient of variation (CV) of utilities), and a set of new measures are also introduced (i.e., improvement on connection over(COP) and under-provisioning (CUP), CV of unserved trafIlc), allowing a network operator to derive and evaluate in advance a set of α-fair RSA solutions and select the one that best Ilts the performance requirements of both the individual connections and the overall network. We show that an egalitarian SA better utilizes the network resources by signiIlcantly improving both COP (up to 20%) and CUP (up to 80%), compared to the utilitarian allocation, while attaining zero blocking. Importantly, the CVs of utilities and unserved trafIlc indicate that a SA that is fairest with respect to the amount of utilities allocated to the connections does not imply that the SA is also fairest with respect to the achievable QoS of the connections, while an egalitarian SA better approximates a fairest QoS-based SA.

Over the last few years there has been an unprecedented interest in unmanned aerial vehicles (UAVs). However, drones potentially pose great threats to security and public safety, especially when their malicious use involves critical infrastructures and public spaces. This work proposes a multiagent counter-drone system where a team of pursuer drones cooperate in order to track and jam multiple rogue drones. Specifically, a cooperative multi-agent approach is proposed in which the best joint mobility and power control actions of each agent are chosen so that the rogue drones are optimally tracked and jammed over time. Two variants of the joint optimization problem are developed and extensive simulations are conducted so as to evaluate the performance of the proposed approach.

Intersections are among the most challenging sections of our road infrastructure and a clear bottleneck for traffic flows. Key aspects of the 5G cellular network, e.g., the Multi Access Edge Computational (MEC) platform and the reduced network latency, act as enablers for the utilization of Connected Autonomous Vehicles (CAVs) that can ultimately bring about drastic changes in the management of intersection crossings and transportation networks in general. To date, there exist extensive research on the problem of autonomous intersection crossings facilitated by CAVs, but the majority of these works assumes that CAVs know their exact location and system state. This work presents a novel framework that allows for an optimized Intersection Manager (IM) that also considers vehicle location uncertainties. Building upon the proposed optimization framework, AVOID is presented, a real-time, near-optimal algorithm that maximizes intersection throughput with probabilistic collision avoidance guarantees. Extensive simulations assess the performance of AVOID, in terms of safe distance between vehicles and intersection throughput, and the effects of the update communication frequency (from the vehicles to the IM) on the gains of the proposed framework.

Wi-Fi technology has been thoroughly studied for indoor localization. This is mainly due to the existing infrastructure inside buildings for wireless connectivity and the uptake of mobile devices where Wi-Fi location-dependent measurements, e.g., timing and signal strength readings, are readily available to determine the user location. To enhance the accuracy of Wi-Fi solutions, a two-way ranging approach was recently introduced into the IEEE 802.11 standard for the provision of Fine Timing Measurements (FTM). Such measurements enable a more reliable estimation of the distance between FTM-capable Wi-Fi access points and user-carried devices; thus, promising to deliver meter-level location accuracy. In this work, we propose two novel solutions that leverage FTM and follow different approaches, which have not been investigated in the literature. The first solution is based on an Unscented Kalman Filter (UKF) algorithm to process FTM ranging measurements, while the second solution relies on an FTM fingerprinting method. Experimental results using real-life data collected in a typical office environment demonstrate the effectiveness of both solutions, while the FTM fingerprinting approach demonstrated 1.12m and 2.13m localization errors for the 67-th and 95-th percentiles, respectively. This is a two to three times improvement over the traditional Wi-Fi signal strength fingerprinting approach and the UKF ranging algorithm.

For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically all modern positioning systems cannot satisfy this requirement for heighten autonomy levels and positioning is becoming a decisive factor for their proliferation. This work investigates how relative positioning can be achieved using signals that are already accessible in the environment, and derives an online procedure for the exploitation of these signals for localization in GNSS-challenged areas. The proposed relative positioning system (RPS) explores the signal properties over a large spectrum of frequency bands, and derives a vehicle tracking algorithm to accurately estimate the vehicle's trajectory in space and time using an arbitrary set of unknown reference positions. Experimental results demonstrate the applicability of RPS and investigate its performance over the different parameter values.

Ant colony optimization (ACO) algorithms have proved to be suitable for solving dynamic optimization problems (DOPs). The integration of local search operators with ACO has also proved to significantly improve the output of ACO algorithms. However, almost all previous works of ACO in DOPs do not utilize local search operators. In this work, the MAX-MIN Ant System (MMAS), one of the best ACO variations, is integrated with advanced and effective local search operators, i.e., the Lin-Kernighan and the Unstringing and Stringing heuristics, resulting in powerful memetic algorithms. The best solution constructed by ACO is passed to the operator for local search improvements. The proposed memetic algorithms aim to combine the adaptation capabilities of ACO for DOPs and the superior performance of the local search operators. The travelling salesperson problem is used as the base problem to generate both symmetric and asymmetric dynamic test cases. Experimental results show that the MMAS is able to provide good initial solutions to the local search operators especially in the asymmetric dynamic test cases.

In this work we consider the scheduling problem for charging a fleet of electric vehicles (EVs) within a station such that the total tardiness of the problem is minimized. The generation of a feasible and efficient schedule is a difficult task due to the physical and power constraints of the charging station, i.e., the maximum contracted power and the maximum power imbalance between the lines of the electric feeder. The ant colony optimization (ACO) metaheuristic is applied to coordinate the charging process of the EVs within the charging station by generating efficient schedules. The behaviour and performance of ACO is analyzed and compared against state-of-the-art approaches on a benchmark set inspired by real-world scenarios. The experimental results show that the application of ACO is highly effective and outperforms other approaches.

Accurate arrival-time predictions in public transportation systems can improve the perceived quality-of-service offered and increase usage of these systems. To date, predictions were primarily based on periodic updates of mobility information that however exhibit a tradeoff between deterministic performance and system efficiency. To further improve on this tradeoff, event-triggering is emerging as a promising operations paradigm. This work describes an innovative design of a public transportation tracking system within which arrival-time predictions are made utilizing an event-triggering framework. As a first step towards this direction, behavior models are derived through extensive analysis of real mobility data. Thereafter, an event-triggering algorithm is developed to detect changes in the model in an online fashion. The efficiency and applicability of the proposed data-driven event-triggering paradigm is demonstrated through a real-world transit scenario that compares event triggering updating techniques.

Secure logging is essential for the integrity and accountability of cyber-physical systems (CPS). To prevent modification of log files the integrity of data must be ensured. In this work, we propose a solution for secure event in cyber-physical systems logging based on the blockchain technology, by encapsulating event data in blocks. The proposed solution considers the real-time application constraints that are inherent in CPS monitoring and control functions by optimizing the heterogeneous resources governing blockchain computations. In doing so, the proposed blockchain mechanism manages to deliver events in hard-to-tamper ledger blocks that can be accessed and utilized by the various functions and components of the system. Performance analysis of the proposed solution is conducted through extensive simulation, demonstrating the effectiveness of the proposed approach in delivering blocks of events on time using the minimum computational resources.

Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G’s diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine the ML-based Quality-of-Transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. We examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their accuracy and training time. We show that the distributed QoT models outperform the centralized QoT model, especially as the number of diverse QoT requirements increases.

In this work, eavesdropping-aware routing and spectrum allocation (RSA) techniques are proposed for elastic optical networks (EONs) using orthogonal frequency division multiplexing (OFDM). To introduce physical (optical) layer security and protect these networks against eavesdropping attacks, spread spectrum (SS) with signal overlapping techniques are used to encode each requested confidential connection. In order to attain access to the signal and compromise a confidential connection, an eavesdropper will now have to lock on the correct frequency, determine the correct code and symbol sequence amongst co-propagated overlapped signals, and decode the signal. Different routing strategies and a novel spectrum allocation technique are proposed in an attempt to add an extra layer of security for confidential connections, while also considering spectrum utilization. Performance results demonstrate that while each confidential connection now requires more spectrum as a result of spreading in the bandwidth domain, the overall network spectrum usage is not increased proportionally to the spreading factor due to the utilization of spectrum overlapping techniques.

In this work, efficient routing, spectrum, and core allocation (RSCA) techniques are proposed for spatial division multiplexed (SDM) elastic optical networks (EONs) utilizing multicore fibers (MCFs). These techniques provide efficient resource utilization with minimal computational complexity, while also taking into consideration physical layer effects. Initially, a crosstalk- aware approach is presented that accounts for the crosstalk effects in the network that can have detrimental impact on the connections established in the network. This approach is subsequently enhanced with a feedback-based procedure that takes into account all physical layer impairments, aiming to minimize the number of connections that cannot be established in the network due to quality-of-transmission (QoT) considerations.

This work addresses the problem of dedicated protection of multicast sessions in mixed‐graph optical networks, where only a fraction of the nodes have optical splitting capabilities. A novel multicast routing algorithm for sparse splitting optical networks (the Modified Steiner Tree Heuristic [MSTH]) is initially presented and is subsequently utilized (together with two existing heuristics [MUS and MSH]) by an effective scheme for the calculation of a pair of disjoint trees. The key idea of this New Arc‐Disjoint Trees (NADT) protection technique is to gradually construct the primary tree, verifying that after the addition of each one of the destinations of the multicast session, a secondary (arc‐disjoint) tree can still be obtained. Performance results demonstrate that the proposed NADT protection technique clearly outperforms the conventional Arc‐Disjoint Trees (ADT) approach in terms of blocking ratio, while incurring only a negligible increase of the average cost of the derived pair of ADT. Furthermore, it is shown that the newly proposed algorithm, MSTH‐NADT, is the one having the best performance in terms of cost and blocking, with MSH‐NADT having similar, albeit slightly worse, performance. However, as MSH‐NADT requires much less CPU time compared to MSTH‐NADT, MSH‐NADT can be considered the best compromise technique. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(4), 360–372 2017

This work presents algorithms for the design phase of flexible grid optical networks considering the impact of in-band channel crosstalk. Due to crosstalk-induced interactions among different connections, malicious high-power signals can potentially spread widely in the network. Moreover, crosstalk interactions also affect the signal quality of transmission. To this end, it is necessary to plan an optical network in a way that the crosstalk effect is minimized. In this work, a novel Routing and Spectrum Allocation (RSA) optimization algorithm based on Integer Linear Programming (ILP) is proposed in order to minimize the impact of the in-band crosstalk effect in flexible grid optical networks through proper utilization of the resources. Additionally, a heuristic algorithm is proposed to handle larger network instances. Performance results indicate that the proposed algorithms perform close to the First Fit spectrum allocation algorithm in terms of total spectrum utilization of the network, while at the same time minimizing the total number of in-band lightpath interactions.

Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

In this work, a node architecture based on wavelength selective switches with feedback loop attenuators that can be used as power equalizers, as well as an optimization algorithm are utilized in order to minimize the impact of physical layer jamming attacks in optical networks, while also minimizing the capital expenditure (Capex) of the network. Due to crosstalk-induced interactions among different connections, malicious high-power jamming signals can potentially spread widely in the network. Therefore, it is necessary to design an optical network in a way that the spread of an attack is minimized. This is achieved in this work by the design of appropriate power equalizer placement and attack-aware Routing and Wavelength Assignment (RWA) algorithms in the form of Integer Linear Program (ILP) formulations. Performance results indicate that the proposed algorithms minimize the number of required equalizers for zero lightpath interactions without the need of additional wavelength resources.

Vehicular connectivity is considered as one of the most highly anticipated emerging technologies since it promises to transform the automotive sector and have a significant impact on all related markets. Data gathered (and information generated) within and around vehicles will be used to improve road safety, travelling efficiency, and passenger comfort and convenience. However, delivering such data to the infrastructure (to process information and generate intelligence) is a challenging task, mainly due to the very large volume of data traffic produced. A promising approach to support these communication needs is to deliver data traffic opportunistically through the available WiFi APs. Evidently, the intermittent connectivity of these hotspots and the inherent mobility of the vehicles severely limit the volume of traffic sent at any one instance in time. The latter limitation is studied in this paper, where decision policies are derived for vehicle-to-vehicle-assisted offloading to maximize the transmission opportunities and thus expedite data traffic delivery. As illustrated in this paper, these policies are easy to implement in practice and offer significant improvement in vehicular data traffic offloading as compared with opportunistic offloading and basic relaying practices.

In the realm of cooperative Intelligent Transportation Systems, vehicles are able to communicate with each other and with the available telecommunications infrastructure to support various safety-related services, traffic-efficiency solutions, and a wide variety of infotainment applications. As the number of mobile devices and information they need to exchange steadily increases, this will further strain mobile networks. It is for that reason that communication over wireless local area networks (WLANs) is becoming the primary means of transporting data to/from vehicles. However, due to their limit range WLANs only offer intermittent connectivity, and due to node mobility the established connections are only available for a short period of time. The present paper investigates how inter-vehicle communication can help minimize the upload/download delivery times in the context of delay tolerant traffic and intermittent communication. To do so, each vehicular station distributes part of its message to neighboring nodes prior to accessing the infrastructure. In turn, vehicles transmit their buffered messages when passing by the infrastructure terminals to complete the file transfer. This paper presents a thorough study of the aforementioned strategy providing a mathematical framework and optimized forwarding techniques. Performance results validate the applicability of the proposed solution.

This paper investigates the problem of multicast traffic grooming in transparent optical networks utilizing a novel grooming approach that is based on routing/grooming of multicast calls on hybrid graphs. The proposed approach exhibits improved performance when compared to existing grooming schemes especially when the physical layer impairments are taken into account.

This paper proposes and devises a novel, simple, and cost effective PON-based 4G mobile backhaul RAN architecture that enables redistributing some of the intelligence currently centralized in the Mobile Packet Core (MPC) platform out into the access nodes of the RAN. Specifically, this work proposes and devises a fully distributed ring-based EPON architecture that enables the support of a converged PON-4G mobile WiMAX/LTE access networking transport infrastructure to seamlessly backhaul both mobile and wireline multimedia traffic and services.

Carrier Ethernet is becoming the dominating backhaul network due to its flexibility, scalability, interoperability and low-cost. Carrier Ethernet networks (CENs) are inherently high bandwidth-delay product (BDP) networks, hence Traditional TCP, and similar variants, make undesirable choices of transport protocol mainly because of the aggressive multiplicative-decrease algorithm. The Ethernet-Services Transport Protocol (ESTP) has proven relieve the effects of the decrease algorithm by dynamically adjusting the transmission rate according to the level of congestion estimated in the network. It should be emphasized that most protocols detect congestion, but do not estimate the level of congestion. Also ESTP incorporates QoS information to further improve performance. In this work, a steady-state throughput analytical model of ESTP is derived, an experimental testbed is built and simulation results are obtained. All validation methods show good agreement.

The rapid adoption of the Internet and the ubiquitous coverage of cellular/wireless technologies, all concur to the fusion of networks' physical boundaries. At the same time, the proliferation of mobile and wireless devices changes our expectations as users, forcing providers to rethink their network and device management (NDM) approaches. Our work contributes to the convergence of NDM, with an early prototype of a resource-oriented cloud-based management service, using YANG data modeling language and JSON notation to model and encode policies. A proof-of-concept prototype implementation on a Linux-based smartphone demonstrates how policies achieve data offloading between wireless and mobile networks, for the benefit of users and operators.

This paper proposes and devises a simple and cost-effective native Ethernet-based self-healing WDM-PON access ring architecture for next-generation broadband access. Specifically, this paper proposes and devises simple, efficient, and cost-effective fully distributed fault detection and recovery schemes for the proposed WDM-PON ring architecture. The main characteristics of the proposed architecture is that it supports a fully distributed control plane among the ONUs, which enables each and every ONU to independently detect, manage, and recover most of the networking failure scenarios.

This paper addresses the important issue of how to optimize the performance of the current mobile backhaul radio access network (RAN) infrastructure to cope with the growing and dynamic nature of the emerging data-centric mobile multimedia traffic and services. Specifically, this work proposes and devises a simple and cost-effective EPON-based dynamic multiservice RAN architecture that efficiently transports and supports a wide range of existing and emerging data-centric mobile multimedia traffic and services along with the diverse quality of service (QoS) and rate requirements set by these services.

The metro Ethernet network (MEN) expands the advantages of Ethernet to cover areas wider than LAN. Ethernet Services were created to provide multiple services to this carrier-grade network that is rapidly growing popularity in North America [21], Europe [22], and Asia [23]. Techniques to police the network layer for MENs are very mature; but the transport layer relies on different flavors of TCP that do not get the most of Ethernet services. We propose a transport layer protocol that can replace previous versions of TCP to better suit metro Ethernet service policies, such as those established for specified service level agreements (SLAs). Ethernet service's traffic and performance parameters include committed information rate (CIR), excess information rate (EIR), and guaranteed packet loss rate. The purpose of this transport protocol is to integrate these parameters, which are applied at the networking layer, into the proposed transport protocol to make a smarter controller.

This work proposes a simple and cost effective local access WDM-PON architecture that combines the salient features of both traditional static WDM-PON (i.e., dedicated connectivity to all subscribers with bit rate and protocol transparencies, guaranteed QoS, and increased security) and dynamic WDM-PON (i.e., efficiently utilizing network resources via dynamic wavelength allocation/sharing among end-users). Specifically, this paper proposes and devises a novel ring-based local access WDM-PON architecture that efficiently supports dynamic allocation of wavelengths/timeslots and sharing traffic as well as a truly shared LAN capability among PON end-users.

This work proposes a survivable ring-based local access PON architecture that addresses some of the limitations of current tree-based PON architectures including supporting private networking capability as well as providing a fully distributed cost-effective APS scheme.

This paper aims to enhance the transport services of Ethernet MANs by detecting congestion more accurately and responding to packet losses more adaptively. Traditional TCP was designed for best-effort networks, and cannot utilize available bandwidth efficiently in a broadband link. Most of the previous research addressed this problem by modifying the additive increase multiplicative decrease (AIMD) algorithm of TCP. In this paper, a novel resilient transport control scheme is proposed for Ethernet services that utilize a less aggressive congestion window reduction algorithm when a non-congested environment is detected. A stochastic model for packet loss is proposed, in which packet losses are expressed as a random function of congestion and random loss present in the network Transport layer congestion control is stated as a hypothesis test process; making decisions based on multiple ACK feedbacks. The new method can keep false-alarm probability under control and therefore it increases throughput significantly.

There is an emerging wide interest to transition from legacy WAN transport technologies to Ethernet-based technology. The current round of carrier Ethernet standards will successfully equip service providers (SPs) with the required tools to provide carrier-grade scalability and to provision and engineer connection-oriented point-to-point (P2P) packet trunks across a native Ethernet infrastructure. Building on these standards, this paper demonstrates how to support and implement full traffic engineering in a global-scale, two-tiered native Ethernet-over-WDM optical networking architecture. To achieve these objectives, several networking innovations are presented and developed including: 1) a GMPLS-based unified control plane that offers a tighter integration between layer-1 (optical transport layer) and layer-2 (Ethernet layer), 2) a fully distributed integrated routing and signaling framework for dynamically provisioning Ethernet switched paths (ESPs) at any bandwidth granularity including both full wavelength and finer granularity (sub-lambda) ESPs in an integrated Ethernet-optical networking environment, and 3) a novel notion of an integrated link-state advertisements strategy that is consistent with a fully integrated routing and signaling protocol

This paper devises and examines via computer simulation an innovative fully distributed global information- based integrated signaling framework for dynamically provisioning EVCs at any bandwidth granularity including both full wavelength and finer granularity (sub-lambda) EVCs at the optical layer.

The problem of protecting purely multicast connections in WDM mesh network has recently started to receive some attention in the literature. In fact, WDM traffic is heterogeneous in nature and only part of the traffic is multicast and the rest is unicast, therefore we expect the presence of both unicast and multicast traffic in future optical networks. This paper studies the problem of dynamic provisioning of survivable heterogeneous unicast and multicast traffic in WDM networks. Specifically, we propose new protection schemes to provision and protect unicast and multicast connection requests against singlelink failures in WDM-mesh networks. The simulation results of the proposed protection schemes are compared with each others and with those found in the literature. The results showed that our proposed scheme TP-OSPT outperforms all other schemes for moderate and large multicast group size. On the other hand, our proposed scheme OCR performs the best for small multicast group size.

The problem of traffic grooming is a crucial constituent in designing WDM networks and has been extensively studied in the literature. Although it is expected that a part of the future traffic of WDM networks will be multicast, most studies of traffic grooming have assumed only unicast traffic. The paper investigates the problem of grooming multicast traffic in WDM networks. More specifically, different sequential single-hop and multi-hop grooming approaches are studied and compared to traditional non-grooming approaches. We also propose a hybrid approach that utilizes the combined resources at the logical and optical layers.

This work proposes a novel fully distributed Ethernet over star coupler-based PON architecture. The architecture uses a collision-free DBA scheme in which the OLT is excluded from the implementation of the time slot assignment. To implement a distributed control plane, direct connectivity (communicability) between the ONUs should be in place without imposing any constraint on the PON topology. In addition to the added flexibility and reliability associated with a distributed architecture, the performance of the proposed decentralized Ethernet PON scheme and the associated bandwidth allocation algorithms are shown to be as efficient as their centralized counterparts.

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.

The paper proposes a novel IP-over-optical network interconnection model that takes the best features from both the overlay and peer models while avoiding their limitations. Specifically, the proposed model utilizes an optical layer-based unified control plane that manages both routers and optical switches (analogous to the peer model), while still retaining complete separation between the optical and IP layers of the overlay model. This is achieved by shifting the control plane functionalities previously associated with the IP layer to the IP/MPLS-aware, non-traffic bearing OXC controller modules located within the optical domain. In this architecture, better decisions can be made for provisioning and managing network resources, leading to their more efficient use. Based on the proposed model, an integrated dynamic routing algorithm, that takes into account the combined topology and resource usage information at both the IP and optical layers, is developed.

We assess the benefits of using statistical techniques to ascertain the shareability of protection channels when computing shared mesh restored lightpaths. Current deterministic approaches require a detailed level of information proportional to the number of active lightpaths, and do not scale well as traffic demands and network grow. With the proposed approach, we show that less information, independent of the amount of traffic demand, is sufficient to determine the shareability of protection channels with remarkable accuracy. Experiments also demonstrate that our approach yields faster computation times with no significant penalty in terms of capacity usage.

A fault recovery system that is fast and reliable is essential to today's networks, as it can be used to minimize the impact of the fault on the operation of the network and the services it provides. This paper proposes a methodology for performing automatic protection switching (APS) in optical networks with arbitrary mesh topologies in order to protect the network from fiber link failures. All fiber links interconnecting the optical switches are assumed to be bidirectional. In the scenario considered, the layout of the protection fibers and the setup of the protection switches is implemented in nonreal time, during the setup of the network. When a fiber link fails, the connections that use that link are automatically restored and their signals are routed to their original destination using the protection fibers and protection switches. The protection process proposed is fast, distributed, and autonomous. It restores the network in real time, without relying on a central manager or a centralized database. It is also independent of the topology and the connection state of the network at the time of the failure.

This work proposes algorithms to achieve full mesh connectivity in 4-fiber WDM self-healing rings (SHRs). The number of wavelengths required is calculated for both odd and even number of nodes on the ring and an optimal wavelength assignment algorithm is presented. This algorithm uses a simple matrix approach for calculating the wavelength assignment between nodes on the ring so as to achieve full mesh connectivity while avoiding any possible violation of the color clash constraint.