Multi-Agent Coordinated Interception of Multiple Rogue Drones

Valianti, Panayiota; Papaioannou, Savvas; Kolios, Panayiotis; Ellinas, Georgios

doi:10.1109/globecom42002.2020.9322489

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The concept of a rogue agent or manipulative agent exists in a number of research domains, including social networks [55,56], distributed systems [57,58], multi-agent systems [59,60,61], robotics [62,63], and many others. However, there are significant differences between a rogue agent in any of those fields and an MCF in terms of definition and functionality.…”

Section: Related Literaturementioning

confidence: 99%

Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

Banerjee,

Carle

2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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Over the past few years, network traffic has grown exponentially with the rise in mobile communication network users and services. This, in turn, increased the operational complexity and hiked the capital and operational expenses of mobile network operations. Present mobile networks are highly complex systems comprising automation mechanisms that leverage numerous technologies. These technologies and automation tools have been developed to deal with the configuration, optimization, and troubleshooting of mobile network operations, facilitating an efficient network management. Self-Organizing Networks (SONs) are an example of one such technology that aims to decrease human intervention by automating network management tasks. Although SON is used widely as an automation tool, it has some shortcomings; particularly, maintenance and upgrade of a rule-based management system like SON are challenging. To address these shortcomings, research is aimed at incorporating machine learning (ML) capabilities in SON and introduced a new management paradigm called cognitive autonomous networks (CANs). Like SON, CAN consists of multiple network automation functions called cognitive functions (CFs). A CF is an intelligent network function that is responsible for managing specific optimization related tasks. Throughout this thesis, we use the following CFs that are relevant for the use cases covered in this thesis: mobility load balancing (MLB), mobility robustness optimization (MRO), coverage and capacity optimization (CCO), and energy savings (ES). These functions have already been standardized by both the network operator community as well as the standardization bodies for SON use cases.These CFs operate on the same radio network in parallel and learn to optimize the same set of radio network parameters. Since they work independently but simultaneously try to adjust the same set of parameters, conflicts are likely to happen among them. These conflicts need to be resolved to keep the network stable and the network operations uninterrupted. The conflicts can be resolved by introducing some coordination among the CFs. In this thesis, we evaluate different paradigms of coordination for CAN and establish that centralized coordination is the most beneficial for CAN. In the first part of the thesis, we propose solutions for centralized coordination for conflict resolution among the CFs. These solutions are based on the Nash Social Welfare Function (NSWF) and a more advanced version of NSWF, the Fisher Market Model, combined with Eisenberg-Gale optimization. Our proposed solutions are designed to serve the combined interest of all the CFs while resolving a conflict. However, when compared between these two approaches, we find that the second one provides a significant improvement over the first one regarding overall network performance.Keeping with the current trend of making the network management system more open, flexible, and agile, in this thesis, we consider an open, multi-vendor CAN where different Finally, I sincerely thank Saya...

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Section: Related Literaturementioning

confidence: 99%

Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

Banerjee,

Carle

2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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“…More recently, the works in [29]- [32], investigate the problem of target jamming by multiple agents, and they propose centralized and distributed decision and control algorithms which can be used to tackle this problem. More specifically, in [29] the authors propose a centralized control approach where the jamming power level and mobility control actions of a team of pursuer agents are jointly decided in such way so that the jamming power received at the malicious drone is maximized, whereas in [30] a distributed control approach is proposed for the same problem. The works in [29], [30], however, tackle the problem in obstaclefree 2D environments with simplified discrete kinematic models.…”

mentioning

confidence: 99%

“…More specifically, in [29] the authors propose a centralized control approach where the jamming power level and mobility control actions of a team of pursuer agents are jointly decided in such way so that the jamming power received at the malicious drone is maximized, whereas in [30] a distributed control approach is proposed for the same problem. The works in [29], [30], however, tackle the problem in obstaclefree 2D environments with simplified discrete kinematic models. In addition, compared to the proposed approach, in which the problem is solved optimally over a rolling finite planning horizon, the approaches presented in [31], [32] are myopic greedy one-step ahead planners .…”

mentioning

confidence: 99%

Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

Papaioannou

Kolios

Ellinas

2022

IEEE Trans. on Mobile Comput.

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This work proposes a distributed estimation and control approach in which a team of aerial agents equipped with radio jamming devices collaborate in order to intercept and concurrently track-and-jam a malicious target, while at the same time minimizing the induced jamming interference amongst the team. Specifically, it is assumed that the malicious target maneuvers in 3D space, avoiding collisions with obstacles and other 3D structures in its way, according to a stochastic dynamical model. Based on this, a track-and-jam control approach is proposed which allows a team of distributed aerial agents to decide their control actions online, over a finite planning horizon, to achieve uninterrupted radio-jamming and tracking of the malicious target, in the presence of jamming interference constraints. The proposed approach is formulated as a distributed model predictive control (MPC) problem and is solved using mixed integer quadratic programming (MIQP). Extensive evaluation of the system's performance validates the applicability of the proposed approach in challenging scenarios with uncertain target dynamics, noisy measurements, and in the presence of obstacles.

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A survey of intelligent transmission line inspection based on unmanned aerial vehicle

et al. 2022

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Multi-Agent Coordinated Interception of Multiple Rogue Drones

Cited by 7 publications

References 14 publications

Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

Coordination, Trust and Orchestration of Cognitive, Open, Multi-Vendor Network Automation Functions

Distributed Estimation and Control for Jamming an Aerial Target With Multiple Agents

A survey of intelligent transmission line inspection based on unmanned aerial vehicle

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