Adaptive Swarm Control for Mobile Resource Placement in Wireless Ad-Hoc Networks

Fraser, Bradley; Coyle, Andrew; Szabo, Claudia; Hunjet, Robert

doi:10.1109/wowmom.2019.8792993

Cited by 3 publications

(3 citation statements)

References 31 publications

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“…Choosing the latency threshold to be 0 is equivalent to requiring that a path is available to every user node from every user node. 1 This occurs when the resource nodes are connected and their combined coverage encompasses all users in the network.…”

Section: Discussionmentioning

confidence: 99%

“…We recently introduced an adaptive resource swarm algorithm that is capable of heterogeneous macroscopic emergent behavior dependent on the network connectivity level. The algorithm bridges where possible and ferries where necessary [1]; adaptive to different topologies, number of users and number of resources. However, it is assumed that user node locations are known and are static but global information about other resource node peers is not known nor modeled.…”

Section: Adaptive Resource Algorithmmentioning

confidence: 99%

“…This produces a seemingly planned behavior where nodes appear to be assigned to different areas. The approach has been shown to be superior to multiple ferries optimallyspaced around an optimal Traveling Salesman Problem tour for various levels of connectivity [1].…”

Section: B Behaviormentioning

confidence: 99%

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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: 99%

Section: Adaptive Resource Algorithmmentioning

confidence: 99%