On ant routing algorithms in ad hoc networks with critical connectivity

Rosati, Laura; Berioli, Matteo; Reali, Gianluca

doi:10.1016/j.adhoc.2007.07.003

Cited by 70 publications

(40 citation statements)

References 33 publications

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“…The ant-based probabilistic routing in MANETs has been widely discussed in [13,16,14]. One example of ant-based routing, Ant-colony Based Routing Algorithm (ARA), was introduced by Gunes et al in [14].…”

Section: Related Workmentioning

confidence: 99%

“…Instead of frequently acquiring new information for each time the network topology changes, the Probabilistic protocols try to statistically predict these changes based on pre-collected information [13][14][15][16][17][18]. Therefore, the frequency of updating and also the amount of control information for each update is reduced in comparison with the deterministic protocols.…”

Section: Related Workmentioning

confidence: 99%

“…Probabilistic protocols, on the other hand, approximately predict the network topology using only probabilistic and traceable information [13][14][15][16][17][18]. Therefore, they can significantly reduce the amount of overhead in comparison with the deterministic protocols and thus are more scalable and topology independent.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic proactive routing with active route trace-back for MANETs

et al. 2010

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a b s t r a c tMobile Ad Hoc network (MANET) is a very attractive networking technology for providing flexible communication in an anytime and anywhere fashion. However, MANET is infrastructure-less and highly dynamic, so the design of an efficient routing strategy for reliable end-to-end communication in such a network has been a challenging issue. The available routing protocols and their embedded information retrieval methods for MAMETs work well only for networks with certain limited assumptions of number of nodes, density of nodes and mobility. With the continuous expansion of the MANETs in real applications, it is now imperative to develop a new routing protocol for MAMETs that is more scalable and topology independent. In this paper, we introduce such a routing scheme for MANETs, which works well under a wide range of network topologies, nodes-density, coverage area size and nodes-mobility. The proposed scheme is based on a novel enhancement of the hint-based probabilistic protocol. Instead of broadcasting extensive control packets for network topology information retrieval as that of conventional routing schemes, the proposed scheme carefully reuses the feedback information carried in unicast packets for this purpose without introducing any extra overhead. The efficiency of the proposed scheme is demonstrated through both mathematical analysis and an extensive simulations study.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Probabilistic proactive routing with active route trace-back for MANETs

et al. 2010

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“…According to the classification provided by [22], ACO routing algorithms for Mobile Ad-Hoc Networks (MANETs) can be differentiated with respect to: (1) How ants are created and how destinations are chosen: algorithms can adopt a proactive or reactive behavior as discussed later; (2) Which kind of information the ants can gather in each path: only the identities of visited nodes or more specific information about them; (3) Which information the ants can use to choose the next hop: pheromone concentration and/or information about crossed nodes that can be incorporated in heuristic functions, generally indicating an explicit influence toward useful local information; (4) How much pheromone the ants deposit in a path: a constant or a variable amount of pheromone depending on information gathered or depending on local parameters.…”

Section: Aco For Routingmentioning

confidence: 99%

GrAnt: Inferring best forwarders from complex networks’ dynamics through a greedy Ant Colony Optimization

et al. 2012

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This paper presents a new prediction-based forwarding protocol for the complex and dynamic Delay Tolerant Networks (DTN). The proposed protocol is called GrAnt (Greedy Ant) as it uses a greedy transition rule for the Ant Colony Optimization (ACO) metaheuristic to select the most promising forwarder nodes or to provide the exploitation of good paths previously found. The main motivation for the use of ACO is to take advantage of its population-based search and of the rapid adaptation of its learning framework. Considering data from heuristic functions and pheromone concentration, the GrAnt protocol includes three modules: routing, scheduling, and buffer management. To the best of our knowledge, this is the first unicast protocol that employs a greedy ACO which: (1) infers best promising forwarders from nodes' social connectivity, (2) determines the best paths to be followed to a message reach its destination, while limiting the message replications and droppings, (3) performs message transmission scheduling and buffer space management. GrAnt is compared to Epidemic and PROPHET protocols in two different scenarios: a working day and a community mobility model. Simulation results obtained by ONE simulator show that in both environments, GrAnt achieves higher delivery ratio, lower messages redundancy, and fewer dropped messages than Epidemic and PROPHET.

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“…Therefore, the available deterministic protocols can carry out the routing process only under a group of restricted constraints on the network coverage area size, node density or mobility. Probabilistic protocols, on the other hand, approximately predict the network topology using only probabilistic and traceable information [11,16]. Therefore, they can significantly reduce the amount of overhead in comparison with the deterministic protocols and thus are more scalable and topology independent.…”

Section: Introductionmentioning

confidence: 99%