On the use of chaotic dynamics for mobile network design and analysis: towards a trace data generator

With the constant increase of the number of autonomous vehicles and connected objects, tools to understand and reproduce their mobility models are required. We focus on chaotic dynamics and review their applications in the design of mobility models. We also provide a review of the nonlinear tools used to characterize mobility models, as it can be found in the literature. Finally, we propose a method to generate traces for a given scenario involving moving people, using tools from the nonlinear analysis domain usually dedicated to topological analysis of chaotic attractors.Comment: 22 pages, 7 figures, to be published in Journal of Difference Equations and Application

Automatic system supporting multicopter swarms with manual guidance

[EN] Currently, there are some scenarios, such as search and rescue operations,where the deployment of manually guided swarms of UAVs can be necessary. In such cases, the pilot's commands are unknown a priori (unpredictable), meaning that the UAVs must respond in near real time to the movements of the leader UAV in order to maintain swarm consistency. In this paper we develop a protocol for the coordination of UAVs in a swarm where the swarm leader is controlled by a real pilot, and the other UAVs must follow it in real time to maintain swarm cohesion. We validate our solution using a realistic simulation software that we developed (ArduSim), testing flights with multiple numbers of UAVs and different swarm configurations. Simulation results show the validity of the proposed swarm coordination protocol, detailing the responsiveness limits of our solution, and finding the minimum distances between UAVs to avoid collisions.This work was partially supported by the "Programa Estatal de Investigation, Desarrollo e Innovation Orientada a Retos de la Sociedad, Proyecto TEC2014-52690-R", Spain, the "Universidad Laica Eloy Alfaro de Manabi," and the "Programa de Becas SENESCYT de la Republica del Ecuador."Fabra Collado, FJ.; Zamora, W.; Masanet, J.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2019). Automatic system supporting multicopter swarms with manual guidance. Computers & Electrical Engineering. 74:413-428. https://doi.org/10.1016/j.compeleceng.2019.01.0264134287

Control mechanisms for mobile devices

In this paper we consider control mechanisms for mobile devices in a stochastic environment. In particular, we consider a device in n-dimensional space subject to Brownian perturbations where a control mechanism moves the device towards its target location at a speed which is a function of its displacement. For this scenario, we construct stochastic differential equations for the mobility process and solve for the steady state probability density function of displacement. From this we are able to give general solutions to key metrics such as displacement outage (the long term probability of exceeding a given distance from the target), connectivity probability (derived from the SNR distribution in a Rayleigh channel with pathloss), the mean time at which the device first exceeds a given distance from the target, and the mean kinetic energy required by the control mechanism. We evaluate these metrics for important special cases of the control mechanism and also study the optimization problem of minimizing kinetic energy over the parameters of the control function

UAV-UGV-UMV Multi-Swarms for Cooperative Surveillance

In this paper we present a surveillance system for early detection of escapers from a restricted area based on a new swarming mobility model called CROMM-MS (Chaotic Rössler Mobility Model for Multi-Swarms). CROMM-MS is designed for controlling the trajectories of heterogeneous multi-swarms of aerial, ground and marine unmanned vehicles with important features such as prioritising early detections and success rate. A new Competitive Coevolutionary Genetic Algorithm (CompCGA) is proposed to optimise the vehicles’ parameters and escapers’ evasion ability using a predator-prey approach. Our results show that CROMM-MS is not only viable for surveillance tasks but also that its results are competitive in regard to the state-of-the-art approaches

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

Learning Optimisation Algorithms over Graphs

The paradigm of learning to optimise relies on the following principle: instead of designing an algorithm to solve a problem, we design an algorithm which will automate the design of such a solver. The initial idea was to alleviate the limitations stated by the No Free Lunch Theorem by producing an algorithm which efficiency is less dependent upon known instances of the problem to tackle. Hyper-heuristics constitute the main learning-to-optimise techniques. These rely on a high-level algorithm performing a search process into a space of low-level heuristics to tackle a given problem. Because the latter search space is problem-dependent, the vast majority of hyper-heuristics are designed to tackle a specific problem. Due to this lack of generality, existing works fully redesign hyper-heuristics when tackling a new problem, despite the fact that they may share a similar structure. In this dissertation, we tackle this challenge by proposing a generic way for learning to optimise any problem. To this end, this thesis introduces three main contributions: (i) an analysis of the formal functioning of learning-to-optimise techniques; (ii) a model of generic hyper-heuristic, named Algorithm Learner for Graph Optimisation problems (ALGO), constituting the central point of this work; (iii) a real-world use case where we use our generic hyper-heuristic to automate the design of behaviours within a swarm of drones. In the first part, we provide a formalism for optimisation and learning concepts, which we use to describe the large body of knowledge that combines two layers of optimisation and/or learning. We then put an emphasis on approaches using learning to improve an optimisation process, i.e., aiming at learning to optimise. In the second part, we present ALGO, our model of generic hyper-heuristic. We explain how we abstract from a given problem with a graph structure so that it can be used to tackle any optimisation problem. We also detail the steps to follow in order to use ALGO to tackle a given problem. We finally present the modularity of ALGO with inner components that a user can implement. The second part ends with a validation of our model, i.e., using ALGO to tackle a classical optimisation problem. In the third part, we use ALGO to tackle the problem of area surveillance with a swarm of drones. We demonstrate that ALGO constitutes a novel and efficient way to automate the design of such a distributed and multi-objective problem

Decentralizing decision making in modern military organizations

Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2003.Includes bibliographical references (leaves 108-111).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.For organizations, the value of information is to improve decision making. In the military in particular, information's role in warfare has always been to affect decisions at all levels -- from strategic to tactical - to put one's forces in a position of advantage. In the information age, the cost of communicating such information has been so phenomenally reduced that it now becomes possible for individuals and entire organizations to tap vast amounts of information. This thesis seeks to address the question of how the modern military can best be designed to harness the power of the information revolution to enhance its ability to make faster, better decisions and thus to become more effective in war as well as in times of peace. To do so, the thesis first considers lessons from military history on the essence of decision making, analyzes the implications of the declining cost of communications and examines new organizational trends in both the corporate world and the military. With this foundation, new organizational designs for the military are proposed and scenarios for their use are described. These new organizational designs are optimized for the information age and incorporate increasingly decentralized making structures. Noting that such formal organizational restructuring by itself is inadequate, the thesis then looks at the shifts in leadership orientation and organizational culture necessary to create the environment that encourages empowerment of individuals as well as the competencies for the individual that are becoming increasingly important in an increasingly decentralized world. Finally, a framework that synthesizes the different ingredients necessary for designing the military organization in the 21st century is proposed.by Boon Kim Tan.S.M.M.O.T