References of "Brust, Matthias R. 50025542"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailSwarm-based counter UAV defense system
Brust, Matthias R. UL; Danoy, Grégoire UL; Stolfi Rosso, Daniel UL et al

in Discover Internet of Things (2021), 1(1),

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs ... [more ▼]

Unmanned Aerial Vehicles (UAVs) have quickly become one of the promising Internet-of-Things (IoT) devices for smart cities. Thanks to their mobility, agility, and onboard sensors'customizability, UAVs have already demonstrated immense potential for numerous commercial applications. The UAVs expansion will come at the price of a dense, high-speed and dynamic traffic prone to UAVs going rogue or deployed with malicious intent. Counter UAV systems (C-UAS) are thus required to ensure their operations are safe. Existing C-UAS, which for the majority come from the military domain, lack scalability or induce collateral damages. This paper proposes a C-UAS able to intercept and escort intruders. It relies on an autonomous defense UAV swarm, capable of self-organizing their defense formation and to intercept the malicious UAV. This fully localized and GPS-free approach follows a modular design regarding the defense phases and it uses a newly developed balanced clustering to realize the intercept- and capture-formation. The resulting networked defense UAV swarm is resilient to communication losses. Finally, a prototype UAV simulator has been implemented. Through extensive simulations, we demonstrate the feasibility and performance of our approach. [less ▲]

Detailed reference viewed: 64 (14 UL)
Full Text
Peer Reviewed
See detailA Distributed Pareto-based Path Planning Algorithm for Autonomous Unmanned Aerial Vehicles (Extended Abstract)
Samir Labib, Nader UL; Danoy, Grégoire UL; Brust, Matthias R. UL et al

Scientific Conference (2021, January 07)

Autonomous Unmanned Aerial Vehicles (UAVs) are in increasing demand thanks to their applicability in a wide range of domains. However, to fully exploit such potential, UAVs should be capable of ... [more ▼]

Autonomous Unmanned Aerial Vehicles (UAVs) are in increasing demand thanks to their applicability in a wide range of domains. However, to fully exploit such potential, UAVs should be capable of intelligently planning their collision-free paths as that impacts greatly the execution quality of their applications. While being a problem well addressed in literature, most presented solutions are either computationally complex centralised approaches or ones not suitable for the multiobjective requirements of most UAV use-cases. This extended abstract introduces ongoing research on a novel distributed Pareto path planning algorithm incorporating a dynamic multi-criteria decision matrix allowing each UAV to plan its collision-free path relying on local knowledge gained via digital stigmergy. The article presents some initial simulations results of a distributed UAV Traffic Management system (UTM) on a weighted multilayer network. [less ▲]

Detailed reference viewed: 206 (42 UL)
Full Text
Peer Reviewed
See detailUAV-UGV-UMV Multi-Swarms for Cooperative Surveillance
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Frontiers in Robotics and AI (2021), 8

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 74 (5 UL)
Full Text
Peer Reviewed
See detailCommunity Detection in Complex Networks: A Survey on Local Approaches
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

Scientific Conference (2021)

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient ... [more ▼]

Early approaches of community detection algorithms often depend on the network’s global structure with a time complexity correlated to the network size. Local algorithms emerged as a more efficient solution to deal with large-scale networks with millions to billions of nodes. This methodology has shifted the attention from global structure towards the local level to deal with a network using only a portion of nodes. Investigating the state-of-the-art, we notice the absence of a standard definition of locality between community detection algorithms. Different goals have been explored under the local terminology of community detection approaches that can be misunderstood. This paper probes existing contributions to extract the scopes where an algorithm performs locally. Our purpose is to interpret the concept of locality in community detection algorithms. We propose a locality exploration scheme to investigate the concept of locality at each stage of an existing community detection workflow. We summarized terminologies concerning the locality in the state-of-the-art community detection approaches. In some cases, we observe how different terms are used for the same concept. We demonstrate the applicability of our algorithm by providing a review of some algorithms using our proposed scheme. Our review highlights a research gap in community detection algorithms and initiates new research topics in this domain. [less ▲]

Detailed reference viewed: 241 (26 UL)
Full Text
Peer Reviewed
See detailImproving Pheromone Communication for UAV Swarm Mobility Management
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 13th International Conference on Computational Collective Intelligence (ICCCI 2021) (2021)

Detailed reference viewed: 40 (6 UL)
Full Text
Peer Reviewed
See detailA competitive Predator–Prey approach to enhance surveillance by UAV swarms
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Applied Soft Computing (2021), 111

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a ... [more ▼]

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator–Prey approach. We propose a Competitive Coevolutionary Genetic Algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC’s detection rates are always under 80% in all cases. [less ▲]

Detailed reference viewed: 43 (4 UL)
Full Text
Peer Reviewed
See detailCONSOLE: intruder detection using a UAV swarm and security rings
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Swarm Intell. (2021), 15(3), 205--235

Detailed reference viewed: 50 (5 UL)
Full Text
Peer Reviewed
See detailOptimising pheromone communication in a UAV swarm
Stolfi, Daniel H.; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in GECCO '21: Genetic and Evolutionary Computation Conference, Companion Volume, Lille, France, July 10-14, 2021 (2021)

Detailed reference viewed: 39 (2 UL)
Full Text
Peer Reviewed
See detailInnovation Networks from Inter-organizational Research Collaborations
Esmaeilzadeh Dilmaghani, Saharnaz UL; Piyatumrong, Apivadee UL; Danoy, Grégoire UL et al

in Heuristics for Optimization and Learning (2020)

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we ... [more ▼]

We consider the problem of automatizing network generation from inter-organizational research collaboration data. The resulting networks promise to obtain crucial advanced insights. In this paper, we propose a method to convert relational data to a set of networks using a single parameter, called Linkage Threshold (LT). To analyze the impact of the LT-value, we apply standard network metrics such as network density and centrality measures on each network produced. The feasibility and impact of our approach are demonstrated by using a real-world collaboration data set from an established research institution. We show how the produced network layers can reveal insights and patterns by presenting a correlation matrix. [less ▲]

Detailed reference viewed: 136 (22 UL)
Full Text
Peer Reviewed
See detailLocal Community Detection Algorithm with Self-defining Source Nodes
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Complex Networks & Their Applications IX (2020, September 01)

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing ... [more ▼]

Surprising insights in community structures of complex networks have raised tremendous interest in developing various kinds of community detection algorithms. Considering the growing size of existing networks, local community detection methods have gained attention in contrast to global methods that impose a top-down view of global network information. Current local community detection algorithms are mainly aimed to discover local communities around a given node. Besides, their performance is influenced by the quality of the source node. In this paper, we propose a community detection algorithm that outputs all the communities of a network benefiting from a set of local principles and a self-defining source node selection. Each node in our algorithm progressively adjusts its community label based on an even more restrictive level of locality, considering its neighbours local information solely. Our algorithm offers a computational complexity of linear order with respect to the network size. Experiments on both artificial and real networks show that our algorithm gains moreover networks with weak community structures compared to networks with strong community structures. Additionally, we provide experiments to demonstrate the ability of the self-defining source node of our algorithm by implementing various source node selection methods from the literature. [less ▲]

Detailed reference viewed: 111 (23 UL)
Full Text
Peer Reviewed
See detailPrivacy and Security of Big Data in AI Systems:A Research and Standards Perspective
Esmaeilzadeh Dilmaghani, Saharnaz UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2019 IEEE International Conference on Big Data (Big Data), 9-12 December 2019 (2020, February 24)

Detailed reference viewed: 410 (33 UL)
Full Text
Peer Reviewed
See detailNGAP: a novel hybrid metaheuristic algorithm for round-trip carsharing fleet planning
Changaival, Boonyarit UL; Danoy, Grégoire UL; Kliazovich et al

in GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020 (2020)

Detailed reference viewed: 111 (19 UL)
Full Text
Peer Reviewed
See detailA Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in IEEE 17th Annual Consumer Communications & Networking Conference CCNC 2020, Las Vegas, NV, USA, January 10-13, 2020 (2020)

Detailed reference viewed: 124 (20 UL)
Full Text
Peer Reviewed
See detailCompetitive Evolution of a UAV Swarm for Improving Intruder Detection Rates
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in 2020 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020, New Orleans, LA, USA, May 18-22, 2020 (2020)

Detailed reference viewed: 190 (21 UL)
Full Text
Peer Reviewed
See detailOptimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
Stolfi Rosso, Daniel UL; Brust, Matthias R. UL; Danoy, Grégoire UL et al

in Optimization and Learning - Third International Conference, OLA 2020, Cádiz, Spain, February 17-19, 2020, Proceedings (2020)

Detailed reference viewed: 153 (24 UL)
Full Text
Peer Reviewed
See detailBayesian optimisation to select Rössler system parameters used in Chaotic Ant Colony Optimisation for Coverage
Rosalie, Martin; Kieffer, Emmanuel UL; Brust, Matthias R. UL et al

in Journal of Computational Science (2020), 41

Detailed reference viewed: 159 (34 UL)
Full Text
Peer Reviewed
See detailTackling Large-Scale and Combinatorial Bi-Level Problems With a Genetic Programming Hyper-Heuristic
Kieffer, Emmanuel UL; Danoy, Grégoire UL; Brust, Matthias R. UL et al

in IEEE Transactions on Evolutionary Computation (2020), 24(1), 44--56

Detailed reference viewed: 115 (22 UL)
Full Text
Peer Reviewed
See detailDesign Challenges of Trustworthy Artificial Intelligence Learning Systems
Brust, Matthias R. UL; Bouvry, Pascal UL; Danoy, Grégoire UL et al

in Intelligent Information and Database Systems - 12th Asian Conference ACIIDS 2020, Phuket, Thailand, March 23-26, 2020, Companion Proceedings (2020)

Detailed reference viewed: 117 (22 UL)