Secure Autonomous UAVs Fleets by Using New Specific Embedded Secure Elements

International audienc

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Decentralized Control of a Swarm of Unmanned Aerial Vehicles

Molti operatori controllano un solo Unmanned Aerial Vehicle -- Veicolo Aereo Non Equipaggiato -- rendendo il sistema di controllo non scalabile. Attualmente, nell'ambito del controllo di questo tipo di veicoli, la tendenza \'e quella di gestire un gruppo di Unmanned Aerial Vehicle tramite un solo operatore in modo da avere un sistema in grado di operare con migliaia di Unmanned Aerial Vehicle che volano sopra una nazione. Swarm Intelligence, basata sui cosiddetti insetti sociali, fornisce le linee guida per progettare sistemi decentralizzati. In particolare, gli insetti sociali sono in grado di perseguire diversi obiettivi, dalla costruzione e difesa del nido, alla ricerca del cibo, al prendersi cura del nido, all'assegnazione di squadre di operai, alla costruzione di ponti. Questa tesi presenta un framework per il controllo decentralizzato di uno sciame di Unmanned Aerial Vehicle basato su funzioni di potentiale artificiale caratterizzate da propriet\'a attrattive e repulsive, che sono usate rispettivamente per raggiungere l'obiettivo e per evitare le eventuali collisioni. Ciascun veicolo dello sciame utilizza un numero limitato di informazioni degli altri veicoli, ed inoltre \'e caratterizzato come un agente con dinamica molto semplice. In questo schema, pi\'u agenti di uno sciame sono in grado di raggiungere una configurazione e di mantenerla, mentre migrano come gruppo ed evitano collisioni tra di loro. Pertanto, i comportamenti del sistema a sciame proposto in questa tesi sono la configurazione e la migrazione del gruppo, e includono la elusione di collisioni. In particolare, questa tesi analizza diverse espressioni di potenziale per determinare in quanto tempo lo sciame converge alla direzione e velocit\'a desiderata, e quanto \'e capace lo sciame ad evitare le collisioni tra gli agenti. Inoltre, sono state determinate due metriche che forniscono la stima del migliore potenziale in un determinato scenario. Una metrica quantifica quanto velocemente lo sciame converge ad una data velocit\'a, e la seconda analizza quanto robusto \'e il potenziale per evitare le collisioni. Le simulazioni mostrano che la soluzione proposta permette di costruire un sistema a sciame in grado di gestire la migrazione e la configurazione del gruppo in presenza di ostacoli utilizzando un numero limitato di informazioni

A Genetic Algorithm for UAV Routing Integrated with a Parallel Swarm Simulation

This research investigation addresses the problem of routing and simulating swarms of UAVs. Sorties are modeled as instantiations of the NP-Complete Vehicle Routing Problem, and this work uses genetic algorithms (GAs) to provide a fast and robust algorithm for a priori and dynamic routing applications. Swarms of UAVs are modeled based on extensions of Reynolds\u27 swarm research and are simulated on a Beowulf cluster as a parallel computing application using the Synchronous Environment for Emulation and Discrete Event Simulation (SPEEDES). In a test suite, standard measures such as benchmark problems, best published results, and parallel metrics are used as performance measures. The GA consistently provides efficient and effective results for a variety of VRP benchmarks. Analysis of the solution quality over time verifies that the GA exponentially improves solution quality and is robust to changing search landscapes - making it an ideal tool for employment in UAV routing applications. Parallel computing metrics calculated from the results of a PDES show that consistent speedup (almost linear in many cases) can be obtained using SPEEDES as the communication library for this UAV routing application. Results from the routing application and parallel simulation are synthesized to produce a more advanced model for routing UAVs

Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDue to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more than one UAVs for the effective scheduling under multi-task requests. Based on this scenario, we clarify the issue on how to maintain the energy efficiency of UAVs and guarantee the reputation gain during the scheduling deployment. In this paper, we first formulate the energy consumption and reputation into the decision model of UAVs scheduling. A game-theoretic scheme is then developed for the optimal decision searching. With the developed model, a range of important parameters of UAV scheduling are thoroughly investigated. Our numerical results show that the proposed scheduling strategy is able to increase the reputation and decrease the energy consumption of UAVs simultaneously. In addition, in the game process, the profit of an operator can be maximized and the network economy research can be explored.Engineering and Physical Sciences Research Council (EPSRC

Learning to Optimise a Swarm of UAVs

The use of Unmanned Aerial Vehicles (UAVs) has shown a drastic increase in interest in the past few years. Current applications mainly depend on single UAV operations, which face critical limitations such as mission range or resilience. Using several autonomous UAVs as a swarm is a promising approach to overcome these. However, designing an efficient swarm is a challenging task, since its global behaviour emerges solely from local decisions and interactions. These properties make classical multirobot design techniques not applicable, while evolutionary swarm robotics is typically limited to a single use case. This work, thus, proposes an automated swarming algorithm design approach, and more precisely, a generative hyper-heuristic relying on multi-objective reinforcement learning, that permits us to obtain not only efficient but also reusable swarming behaviours. Experimental results on a three-objective variant of the Coverage of a Connected UAV Swarm problem demonstrate that it not only permits one to generate swarming heuristics that outperform the state-of-the-art in terms of coverage by a swarm of UAVs but also provides high stability. Indeed, it is empirically demonstrated that the model trained on a certain class of instances generates heuristics and is capable of performing well on instances with a different size or swarm density

Autonomisen multikopteriparven hallinta etsintä- ja pelastustehtävissä

This thesis presents the requirements and implementation of a Ground Control Station (GCS) application for controlling a fleet of multicopters to perform a Search And Rescue (SAR) mission. The requirements are put together by analysing existing drone types, SAR practices, and available GCS applications. Multicopters are found to be the most feasible drone to use for the SAR use case because of their maneuverability, despite not having the best endurance. Several existing area coverage methods are presented and their usefulness is analyzed for SAR scenarios where different amounts of prior knowledge is available. It is stated that most search patterns can be used with a fleet of drones, by creating drone formations and by dividing the target area into sub-areas. It is noted that most currently available GCS applications are focused on controlling a single drone for either industrial or hobby use. A proof of concept prototype is developed on top of an open source GCS and tested in field tests. Based on all the previous learnings from the protype and research, a new GCS is designed and developed. The development on optimizing communications between the GCS and the autopilot leads to a filed patent application. The new software is tested with three multicopters in a water rescue scenario and several user interface improvements are made as a result of the learnings. The development of a GCS for controlling a drone fleet for search and rescue is proven feasible.Työssä esitetään multikopteriparven hallintaan käytettävän Ground Control Station (GCS) ohjelmiston vaatimukset ja toteutus Search And Rescue (SAR) etsintä- ja pelastustehtävien suorittamiseksi. Vaatimukset kootaan yhteen analysoimalla saatavilla olevia droonityyppejä, SAR pelastuskäytäntöjä, sekä GCS ohjelmistoja. Multikopterit osoittautuvat liikkuvuutensa ansiosta pelastustehtäviin sopivimmaksi vaihtoehdoksi, vaikka niiden saavutettavissa oleva lentoaika ei ole parhaimmasta päästä. Erilaisia etsintämetodeja esitetään alueiden kattamiseksi ja niiden hyödyllisyyttä analysoidaan SAR tilanteissa, joissa ennakkotietoa on saatavilla vaihtelevasti. Osoitetaan, että useimpia etsintäalgoritmeja voidaan hyödyntää drooniparvella, muodostamalla lentomuodostelmia, sekä jakamalla kohdealue pienempiin osa-alueisiin. Huomataan, että suurin osa tällä hetkellä saatavilla olevista GCS ohjelmistoista on suunnattu teollisuuden tai harrastelijoiden käyttöön, pääasiassa yksittäisen droonin hallintaan. Prototyyppi kehitetään avoimen lähdekoodin GCS ohjelmiston pohjalta ja testataan kenttätesteissä. Tästä saadun tiedon avulla suunnitellaan ja kehitetään uusi GCS ohjelmisto. Kehitystyö viestinnän optimoinniksi autopilotin ja GCS ohjelmiston välillä johtaa patenttihakemukseen. Uusi ohjelmisto testataan kolmella multikopterilla vesipelastustilanteessa ja sen seurauksena käyttöliittymään tehdään useita parannuksia. GCS ohjelmiston luominen drooniparven hallintaan etsintä- ja pelastustehtävissä todetaan mahdolliseksi