Mohamed Ayoub Messous

Network connectivity and area coverage for UAV fleet mobility model with energy constraint

Our main focus through the present paper is on developing an original distributed mobility model for autonomous fleet of interconnected UAVs (Unmanned Aerial Vehicles) performing an area exploration mission. The UAVs, equipped with wireless ad-hoc capabilities, are required to optimally explore an area while maintaining connectivity with their neighboring UAVs and the base station. Because energy is a scarce resource, especially for UAVs, its wise management is quite beneficial for the network lifetime and mission success. Hence, the proposed mobility model, compared to other models in the literature, is the first to ever include the remaining energy level as decision criterion combined with area coverage and network connectivity. Based on these criterions and the information received from its neighbors, each UAV determines, using the information received from its neighbors, its next movement to be undertaken. The performances of the proposed approach are compared with those achieved through a randomized approach and a forces-based approach. Simulation results, using NS3, show that it outperforms the two other models in terms of coverage and connectivity.

Fault-tolerant multi-agent control architecture for autonomous mobile manipulators

Fault-tolerant multi-agent control architecture for autonomous mobile manipulators. We propose a generic multi-agent control architecture for mobile manipulators.Robot complex mathematical models are not required, so computing time is reduced.The strategy is fault-tolerant to breakdowns without needing specific treatments.The proposed approach works in 3D environments with high dof mobile manipulators.Accuracy of the proposed approach is good comparatively to classical approaches. This paper presents our ongoing efforts toward the development of a multi-agent distributed framework for autonomous control of mobile manipulators. The proposed scheme assigns a reactive agent to control each degree-of-freedom of the manipulator(s), a hybrid agent to control the mobile base, and a supervisory agent to coordinate and synchronize the work of the control agents. Each control agent implements a Simulation-Verification technique to optimize, locally and independently from the other agents, a predefined objective function. The final goal consists of bringing the end-effector as close as possible to imposed operational targets (reaching tasks).Different simulation scenarios are described and carried out for the case of RobuTER/ULM robot, with and without considering failures of some articulations of the manipulator or the mobile base. Results show that the main advantage of the proposed approach is that the system pledges a fault-tolerant response to some breakdowns without needing any specific additional treatment.

How to Detect Cyber-Attacks in Unmanned Aerial Vehicles Network?

Security issues in unmanned aerial vehicle (UAV) networks attract the attention of both industry and research community. This is due to the large number of attacks that can target such networks with a goal for instance to jam the communication, disturb the network operation, inject wrong data, etc. In this paper, we propose and implement a cyber security system to protect the UAVs against the most dangerous threats: cyber-attacks that target the data integrity and network availability. Our system is based on a cyber detection mechanism to promptly detect these attacks as soon as they unfold. Minimizing false positives and false negatives rates is a major issue since classifying a legitimate node as an intruder and vice versa may compromises the efficiency of the security system . Thereby, to address this issue, a threat estimation model based on Belief approach is proposed. Simulation results show that our security system exhibits a high accuracy detection compared to cyber detection system proposed in current literature.

Multi-Agent Fuzzy-Based Control Architecture for Autonomous Mobile Manipulators: Traditional Approaches and Multi-Agent Fuzzy-Based Approaches

This paper surveys the different control approaches for autonomous mobile manipulators traditional control approaches and multi-agent heuristic-based control approaches, and focuses mainly on multi-agent fuzzy-based approaches. Directions are discussed and properties of the state-of-the-art in control approaches are classified and compared depending on the techniques used for controlling the robots. The conclusion of the paper presents our point of view about the current state of designing multi-agent fuzzy-based control approaches for such autonomous robots.

Multi-agent Control Approach for Autonomous Mobile Manipulators: Simulation Results on RobuTER/ULM

Abstract This article presents a multi-agent approach for controlling autonomous mobile manipulators. The proposed approach assigns a hybrid agent (Mobile base agent) for the control of the mobile base, a reactive agent (Joint agent) to each degree-of-freedom (dof) of the manipulator, and a Supervisory agent to assure coordination and to synchronize the work of the whole agents of the system. The initial simulation results, obtained via different positioning tasks on RobuTER/ULM with and without considering breakdowns, show that the main advantage of such an approach is that it pledges a fault-tolerant response to various types of breakdowns without adding any specific dysfunction treatment.

Efficient Data Processing in Software-Defined UAV-Assisted Vehicular Networks: A Sequential Game Approach

In large scale networks like Vehicular Ad-hoc Networks (VANETs), the full coverage of fixed infrastructure is hard to ensure, making network management difficult. Whether in infrastructure-less environments where the network connectivity is poor or where the infrastructure deployment is difficult, costly or not profitable. Recently, in the one side, Unmanned Aerial Vehicles (UAVs) have been used as a new flexible solution to assist infrastructure-less vehicular networks for the investigation of inaccessible areas. In the other side, several works have shown interest in the use of the emerging network paradigm of Software-Defined Networking (SDN) to facilitate the management and improve the performances of vehicular networks. In this paper, we propose a novel distributed SDN-based architecture for UAV-assisted infrastructure-less vehicular networks. The main goal is to fill the gap that no SDN-based architecture has been proposed for these networks. We focus particularly on a road safety use-case that incorporates UAVs to assist emergency vehicles in the exploration of affected zones in critical emergency situations. Moreover, we investigate how to achieve efficient data processing policy through a computation offloading/sharing decision-making problem. The main challenge is to reach the best tradeoff between computation delay and energy consumption for computation-intensive tasks in a delay-sensitive context. We formulate this decision problem as a two-player sequential game approach and design distributed computation algorithms to solve the problem. Numerical results show that data processing policy of distributed offloading/sharing algorithms achieves efficient computation performances in terms of delay and energy whilst ensuring until 28% gain of system cost and 95% better response time, compared to native computation scenarios and related data delivery UAV-assisted VANET works, respectively.

Computation offloading game for an UAV network in mobile edge computing

Due to the limitations of mobile devices in terms of processing power and battery lifetime, cloud based solutions offer an attractive approach to answer these shortcomings. Since offloading intensive computation tasks to an edge/cloud server would achieve impressive performances, computation offloading paradigm has attracted the focus of many research groups in the last few years. This paper considers the problem of computation offloading while achieving a tradeoff between execution time and energy consumption. The proposed solution is intended for a fleet of small drones that are required to achieve highly intensive computation tasks. Drones need to detect, identify and classify objects or situations. Thus, they are brought to deal with intensive tasks such as pattern recognition and video preprocessing. The latter implement very complex calculations and typically require dedicated and powerful processors, which would definitely accentuate the dilemma between energy and delay. We adopted a game theory model where the players are all the drones in the network with three possible strategies. We defined the cost function to be minimized as a combination of energy overhead and delay. The simulation results are very promising and the achieved performances outperformed their counterparts in terms of average system wide cost and scalability.

Implementing an emerging mobility model for a fleet of UAVs based on a fuzzy logic inference system

In this paper, we design and implement a novel generic mobility model, named Alpha-based, for a fleet of small interconnected UAVs (Unmanned Aerial Vehicles) that collaborate to explore a geographic area (battle field, research and rescue missions, surveillance applications, etc.). In fact, due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the UAV’s attributes. Hence, we propose to use a combination of energy level, coverage-area and network connectivity for mobility decision-making, in contrast to the literature where only network connectivity and area coverage are investigated. On the one hand, these two metrics are very important, especially for applications where achieving the best area-coverage and maintaining network connectivity represent an essential requirement. On the other hand, energy is another equally noteworthy constraint that should be taken into account. In fact, being a crucial resource for all mobile devices and especially for UAVs, the energy becomes vital to ensure the network lifetime and mission success. As far as we know, Alpha-based mobility model is the first to ever consider a combination of these three metrics within the same decision-making criterion. A distributed scheme is adopted, where each UAV determines locally its future movement based on the information it receives from its neighbors. Moreover, a novel fuzzy inference system is implemented in order to compute the values of a followship weighting parameter, named Alpha. This latter is used to choose the most suitable neighboring UAV to follow. To validate the proposed mobility model, rigorous testing has been accomplished, through simulation work. Compared to Random-based and Forces-based mobility models, the Alpha-based mobility model achieves good coverage rate while maintaining connectivity.

An efficient management of the control channel bandwidth in VANETs

The management of radio congestion in the control channel is one of the active research areas in Vehicular Ad-hoc Networks (VANETs). Many congestion control protocols have already been proposed to ensure an optimal management of the radio control channel. LIMERIC is a well-known congestion control protocol which was adopted by the current ETSI standardization process to be applied in the future deployment of VANETs. This protocol uses a mathematical equation to adjust the beaconing rate for each vehicle based on the measured channel load and a targeted channel load. However, efficiently managing all the available bandwidth using LIMERIC is yet to be achieved and still an open challenge. To address this issue, we propose a new approach that enhances LIMERIC protocol so that the available bandwidth would be used efficiently. Our aim is to bring the measured channel load as close as possible to the targeted level of channel load. Our method combines LIMERIC with a novel local density estimation approach called Segment based Local Density Estimation (SLDE). The performance evaluation shows that our approach uses the bandwidth efficiently and allows higher beaconing rate with a fair division of the available bandwidth.

Multi-agent control architecture for RFID cyberphysical robotic systems initial validation of tagged objects detection and identification using Player/Stage

The objective of this paper is to describe and validate a multi-agent architecture proposed to control RFID Cyber-Physical Robotic Systems. This environment may contain human operators, robots (mobiles, manipulators, mobile manipulators, etc.), places (workrooms, walls, etc.) and other objects (tables, chairs, etc.). The proposed control architecture is composed of two types of agents dispatched on two levels. We find at the Organization level a Supervisory agent to allow operators to configure, manage and interact with the overall control system. At the Control level, we distinguish the Robots agents, to each robot (mobiles, manipulators or mobile manipulators) is assigned a Robot agent to carry out operations. We simulated RFID readers with fiducial finders and RFID tags with fiducials system using Player/Stage. Fiducial tags are attached to all the objects of interest of the environment; fiducial finders are installed on the robots (Robots agents) to be able to detect and identify each tagged object in the robots vicinity. Finally, obtained data are sent to the Supervisory agent to be saved and organized into a centralized database available for all the robots of the system.