Simulating the effect of degraded wireless communications on emergent behavior

Fraser, Bradley; Hunjet, Robert; Szabo, Claudia

doi:10.1109/wsc.2017.8248117

Cited by 13 publications

(10 citation statements)

References 22 publications

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“…Thus, in order for the emergent property to form, interactions are necessary. Indeed, studies have characterized how the emergent property breaks down when those interactions are interfered with [56]. The results above demonstrate a similar phenomenon whereby reducing the opportunity for interaction (by reducing the density via range or node numbers) reduces the development of localized resource node behaviour presenting as a less efficient algorithm.…”

Section: Discussionmentioning

confidence: 81%

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

et al. 2020

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Next-hop data-ferrying is a data-driven approach to ferrying data between graph components of a disconnected network. In contrast to pre-planned routing methods like the Traveling Salesman tour, only the next-hop is planned, allowing the ferry to be reactive to the data flows within the network. When multiple ferries are used, explicit coordination between them is difficult due to the data-driven approach of the algorithm, and the distributed and disconnected nature of the problem. However, coordinated behavior can still be produced through swarm intelligent means and while this is a useful pragmatic result, mathematical models describing system properties are difficult to develop. This generally results in algorithm correctness being demonstrated through simulation. In this article, we describe the development of a mathematical model of such a next-hop ferrying swarm. We show that the closed-form expression for user data latency is a close match to simulation results for Random Geometric Graphs under the assumption of a zero expected degree. In non-zero degree graphs, a near constant offset between the model and simulation is observed.INDEX TERMS Data ferrying, emergent behavior, MANET, random geometric graph.

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Section: Discussionmentioning

confidence: 81%

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

et al. 2020

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“…Although this method gives realistic insights for the RF measurements, it does not tell how the multi-agent system is impacted by these fluctuations. On this subject, in [6], the authors explain how an emergent behavior, such as flocking, is impacted when we use a relatively accurate propagation model and tune the inherent losses due to shadowing effects, SNR, jamming and so on. However, these studies consider only simplistic flocking models, far from realistic fleet of UAVs.…”

Section: Communication and Neighbourhood In Presence Of Obstaclesmentioning

confidence: 99%

“…The use of simple assumptions on communications appears to be one of the main issue when it comes to real experiments. The work of [6] gives a good idea of how realistic communications can affect an emergent behavior. However, the controller involved in this paper is quite simple and hence doesn't reflect how standard flocking models are affected.…”

Section: Introductionmentioning

confidence: 99%

Extension of Flocking Models to Environments with Obstacles and Degraded Communications

Bonnefond

Simonin

Guérin-Lassous

2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we study existing flocking models and propose extensions to improve their abilities to deal with environments having obstacles impacting the communication quality. Often depicted as robust systems, there is yet a lack of understanding how flocking models compare and how they are impacted by the communication quality when they exchange control data. We extend two standard models to improve their ability to stay connected while evolving in environments with different obstacles distributions. By taking into account the radio propagation, we model the obstacles impact on communications in a simulator that we use to optimize flocking parameters. The simulation results show the efficiency of the proposed models and how they adapt to different environmental constraints.

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“…One of the most common reasons of a failure approach is the limited communication [3], including communication delay or data loss. In case of delayed communication, it is difficult to accurately estimate the real position of one group member.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Self-Organizing Leader-Follower Control in a Swarm Mobile Robots System Under Limited Communication

Qiao

Zhang

et al. 2020

IEEE Access

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The leader-follower model is found efficient for robot swarm exploration tasks. When a swarm of mobile robots collaboratively explore an unknown environment with limited communication, such as communication delay or even message loss, it is necessary to adaptively regroup robots according to the dynamic communication conditions and task goals. In this paper, we propose to analyze the leader-follower topology in a graph fashion and employ Ratio Cut of the proposed graph for membership re-assignment. We take each robot as a node and consider both the communication delay and the goal, i.e., exploring towards unknown area, as the edge weight. Then we use the Laplacian matrix to bipartition the graph repeatedly to optimize group partition. Simulation results validate the effectiveness of the proposed method.

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Simulating the effect of degraded wireless communications on emergent behavior

Cited by 13 publications

References 22 publications

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

An Analytic Latency Model for a Next-Hop Data-Ferrying Swarm on Random Geometric Graphs

Extension of Flocking Models to Environments with Obstacles and Degraded Communications

Dynamic Self-Organizing Leader-Follower Control in a Swarm Mobile Robots System Under Limited Communication

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