Decentralized Congestion Control in Random Ant Interaction Networks

  • Andreas KasprzokEmail author
  • Beshah Ayalew
  • Chad Lau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)


Interaction networks formed by foraging ants are among the most studied self-organizing multi-agent systems in nature that have inspired many practical applications. However, the vast majority of prior investigations assume pheromone trails or stigmergic strategies used by the ants to create foraging behaviors. We first review an ant network model where the direction and speed of each ant’s correlated random walk are influenced by direct and minimalist interactions, such as antennal contact. We incorporate basic ant memory with nest and food compasses, and adopt a discrete time, non-deterministic forager recruitment strategy to regulate the foraging population. The paper’s main focus is on decentralized congestion control and avoidance schemes that are activated with a quorum sensing mechanism. The model relies on individual ants’ ability to estimate a perceived avoidance sector from recent interactions. Through simulation experiments it is shown that a randomized congestion avoidance scheme improves performance over alternative static schemes.


Decentralized Congestion Control Congestion Avoidance Scheme Recent Interaction Avoidance Sectors Foraging Population 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Clemson University International Center for Automotive ResearchGreenvilleUSA
  2. 2.Harris CorporationPalm BayUSA

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