Ant-Routing-Algorithm for Mobile Multi-Hop Ad-Hoc Networks

Güneş, Mesut; Spaniol, Otto

doi:10.1007/978-0-387-35703-4_9

Cited by 50 publications

(37 citation statements)

References 8 publications

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“…Other approaches are discussed in [6,21,22,26]. Best effort address assignment mechanisms have been suggested in [24,25,28], but some of these mechanisms only provide weak duplicate address detection, which allows for duplicate addresses but ensures that packets are routed to the correct destination.…”

Section: Dynamic Assignment and Management Of Ip Addressesmentioning

confidence: 99%

911-NOW: A network on wheels for emergency response and disaster recovery operations

Abusch-Magder¹,

Bösch²,

Klein³

et al. 2007

Bell Labs Tech. J.

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Section: Dynamic Assignment and Management Of Ip Addressesmentioning

confidence: 99%

911-NOW: A network on wheels for emergency response and disaster recovery operations

Abusch-Magder¹,

Bösch²,

Klein³

et al. 2007

Bell Labs Tech. J.

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“…The original Ant-like Routing Algorithm was improved in 2003 by the same authors [55]. This version incorporates several changes including, probabilistic data forwarding, continuous pheromone decay, ant packet prioritization, backward ant flooding, data packet buffering, and neighbor broadcast.…”

Section: Ara2mentioning

confidence: 99%

“…It is noted that the implementation of ARA2 presented in [55] optimizes for hop count, where this approach works very well.…”

Section: Ara2mentioning

confidence: 99%

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Termite: A swarm intelligent routing algorithm for mobilewireless Ad-Hoc networks

Wicker

Roth

2006

Studies in Computational Intelligence

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A biologically inspired algorithm is presented to route messages in mobile wireless ad-hoc networks. Such computer networks are primarily characterized by their quickly changing topologies due to frequent node mobility. The principles of swarm intelligence are used to define a probabilistic algorithm for which routing through paths of maximum utility is an emergent property. This adaptive algorithm, dubbed Termite, uses stigmergy to reduce the amount of control traffic needed to maintain performance. Strong routing robustness is achieved through the use of multiple paths; each packet is routed randomly and independently. Once the basic operation of Termite is verified, alternative metric estimation techniques are tested via simulation. Optimal system parameters are selected by testing over orders of magnitude. A simple analytical model is built in order to explain the simulation results. The model also used to propose two heuristics for determining the optimal pheromone decay rate. All of the enhancements to Termite, now known as ReTermite, are consolidated and tested against Ad-hoc On-demand Distance Vector (AODV), a leading ad-hoc routing algorithm. ReTermite is shown to be superior in many primary metrics and the reasons for this explained. Previous heuristic models are also compared to simulation results.

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“…Ant-colony-based Routing Algorithm (ARA). ARA is another reactive routing protocol (Günes & Spaniel, 2003) for MANETs. The routing table entries in ARA contain pheromone values for the choice of a neighbour as the next hop for each destination.…”

mentioning

confidence: 99%