Network Coding-based Routing and Spectrum Allocation in Elastic Optical Networks for Enhanced Physical Layer Security

In this work, an eavesdropping-aware routing and spectrum allocation approach is proposed utilizing network coding (NC) in elastic optical networks (EONs). To provide physical layer security in EONs and secure the confidential connections against eavesdropping attacks using NC, the signals of the confidential connections are combined (XOR-ed) with other signals at different nodes in their path, while transmitted through the network. The combination of signals through NC significantly increases the security of confidential connections, since an eavesdropper must access all individual signals, traversing different links, in order to decrypt the combined signal. A novel heuristic approach is proposed, that solves the combined network coding and routing and spectrum allocation (NC-RSA) problem, that also takes into account additional NC constraints that are required in order to consider a confidential connection as secure. Different routing and spectrum allocation strategies are proposed, aiming to maximize the level of security provided for the confidential demands, followed by an extensive performance evaluation of each approach in terms of the level of security provided, as well as the spectrum utilization and blocking probability, under different network parameters. Performance results demonstrate that the proposed approaches can provide efficient solutions in terms of network performance, while providing the level of security required for each demand

Ant Colony Optimization for the Electric Vehicle Routing Problem

Ant colony optimization (ACO) algorithms have proved to be powerful tools to solve difficult optimization problems. In this paper, ACO is applied to the electric vehicle routing problem (EVRP). New challenges arise with the consideration of electric vehicles instead of conventional vehicles because their energy level is affected by several uncertain factors. Therefore, a feasible route of an electric vehicle (EV) has to consider visit(s) to recharging station(s) during its daily operation (if needed). A look ahead strategy is incorporated into the proposed ACO for EVRP (ACO-EVRP) that estimates whether at any time EVs have within their range a recharging station. From the simulation results on several benchmark problems it is shown that the proposed ACO-EVRP approach is able to output feasible routes, in terms of energy, for a fleet of EVs

Multi-Period Attack-Aware Optical Network Planning under Demand Uncertainty

In this chapter, novel attack‐aware routing and wavelength assignment (Aa‐RWA) algorithms for multiperiod network planning are proposed. The considered physical layer attacks addressed in this chapter are high‐power jamming attacks. These attacks are modeled as interactions among lightpaths as a result of intra‐channel and/or inter‐channel crosstalk. The proposed Aa‐RWA algorithm first solves the problem for given traffic demands, and subsequently, the algorithm is enhanced in order to deal with demands under uncertainties. The demand uncertainty is considered in order to provide a solution for several periods, where the knowledge of demands for future periods can only be estimated. The objective of the Aa‐RWA algorithm is to minimize the impact of possible physical layer attacks and at the same time minimize the investment cost (in terms of switching equipment deployed) during the network planning phase

Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks

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.Comment: accepted for presentation at the IEEE GLOBECOM 201

The law of activity delays

Delays in activities completion drive human projects to schedule and cost overruns. It is believed activity delays are the consequence of multiple idiosyncrasies without specific patterns or rules. Here we show that is not the case. Using data for 180 construction project schedules, we demonstrate that activity delays satisfy a universal model that we call the law of activity delays. After we correct for delay risk factors, what remains follows a log-normal distribution.Comment: 7 pages, 4 figures, 1 tabl

A Multi-task Learning Framework for Drone State Identification and Trajectory Prediction

The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV. This work addresses this problem by proposing an innovative multi-task learning framework (MLF-ST) for UAV state identification and trajectory prediction, that aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Moreover, a novel loss function is proposed that combines the two objectives, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of UAV flights, illustrating that it is able to outperform various state-of-the-art baseline techniques in terms of both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, demonstrates that it can enable applications such as UAV-based surveillance and monitoring, while also improving the safety and efficiency of UAV operations