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Georgios Ellinas

Publications and source records attributed to Georgios Ellinas.

26 records · Page 2Linked to original sources
arXiv2021

Scheduling Aerial Vehicles in an Urban Air Mobility Scheme

Highly populated cities face several challenges, one of them being the intense traffic congestion. In recent years, the concept of Urban Air Mobility has been put forward by large companies and organizations as a way to address this problem, and this approach has been rapidly gaining ground. This disruptive technology involves aerial vehicles (AVs) for hire than can be utilized by customers to travel between locations within large cities. This concept has the potential to drastically decrease traffic congestion and reduce air pollution, since these vehicles typically use electric motors powered by batteries. This work studies the problem of scheduling the assignment of AVs to customers, having as a goal to maximize the serviced customers and minimize the energy consumption of the AVs by forcing them to fly at the lowest possible altitude. Initially, an Integer Linear Program (ILP) formulation is presented, that is solved offline and optimally, followed by a near-optimal algorithm, that solves the problem incrementally, one AV at a time, to address scalability issues, allowing scheduling in problems involving large numbers of locations, AVs, and customer requests.

cs.AI↗
arXiv2020

Fair Resource Allocation in Optical Networks under Tidal Traffic

We propose an alpha-fair routing and spectrum allocation (RSA) framework for reconfigurable elastic optical networks under modeled tidal traffic, that is based on the maximization of the social welfare function parameterized by a scalar alpha (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 efficiency. A set of existing metrics are examined (i.e., connection blocking, resource utilization, coefficient 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 traffic), allowing a network operator to derive and evaluate in advance a set of alpha-fair RSA solutions and select the one that best fits the performance requirements of both the individual connections and the overall network. We show that an egalitarian SA better utilizes the network resources by significantly 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 traffic 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.

cs.NI↗
arXiv2020

Relative Positioning of Autonomous Systems using Signals of Opportunity

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.

eess.SP↗
arXiv2020

Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions

In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations.

cs.AI↗
arXiv2020

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.

cs.NI↗
arXiv2019

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.

cs.NI↗
arXiv2016

Fast Shortest Path Routing in Transportation Networks with Time-Dependent Road Speeds

The current paper deals with the subject of shortest path routing in transportation networks (in terms of travelling time), where the speed in several of the network's roads is a function of the time interval. The main contribution of the paper is a procedure that is faster compared to the conventional approaches, that derives the road's traversal time according to the time instant of departure, for the case where the road's speed has a constant value inside each time interval (in general, different value for each time interval). Furthermore, the case where the road's speed is a linear function of time inside each time interval (in general, different linear function for each time interval) is investigated. A procedure that derives the road's traversal time according to the time instant of departure is proposed for this case as well. The proposed procedures are combined with Dijkstra's algorithm and the resulting algorithms, that are practically applicable and of low complexity, provide optimal shortest path routing in the networks under investigation.

cs.DS↗