Bio-inspired routing protocol for mobile
            <i>ad hoc</i>
            networks

Villalba, Luis Javier García; Cañas, Delfín Rupérez; Orozco, Ana Lucila Sandoval

doi:10.1049/iet-com.2009.0826

Cited by 27 publications

(13 citation statements)

References 4 publications

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“…Forward ants share the same queues as data packets, so that they experience the same traffic load. Destinations are locally selected according to the data traffic patterns generated by the local workload: if f sd is a measure (in bits or in the number of packets) of the data flow s → d, then the probability of creating at node s a forward ant with node d as destination is given by Villalba and Orozco [8] …”

Section: Antnet Algorithmmentioning

confidence: 99%

“…Whenever a probability in the routing table is increased by (8), it is necessary to ensure that the sum of the probabilities in each row remains 1. Equation (8) is used to normalise the values.…”

Section: Updating Routing Tablesmentioning

confidence: 99%

“…If the r factor at the current agent is less than the preset value then find the closest node, which is nearest to destination using range difference technique. 8. Set preset value of r = 0.3 for higher throughput and r = 0.9 for higher PDR.…”

Section: Oantgps Rulesmentioning

confidence: 99%

See 2 more Smart Citations

New approach for routing in mobile ad‐hoc networks based on ant colony optimisation with global positioning system

Karia¹,

Godbole²

2013

IET Networks

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The field of mobile ad-hoc networks (MANETs) has gained an important part of the interest of researchers and become very popular in past few years. MANETs can be used to set up a temporary mean of communication by having nodes behave as hosts and routers, with packets hopping between nodes to reach the destination node. This makes MANET suitable for rapid deployment, but because of the dynamic changes in network configurations, quickly finding the optimum route is difficult. In this study, the authors propose a biology-inspired ant routing protocol based on ant colony optimisation called as optimised antnet global positioning system, which is based on GPS and mobile software agents modelled on ants for routing in ad-hoc networks. The authors compare the performance of the authors protocol with Antnet, ad-hoc on-demand distance vector, Dynamic Source Routing (DSR) and Ad-hoc On Demand Multipath Distance Vector (AOMDV) protocols with respect to end-to-end delay, average throughput, packet delivery ratio and route cost.

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Section: Antnet Algorithmmentioning

confidence: 99%

Section: Updating Routing Tablesmentioning

confidence: 99%

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New approach for routing in mobile ad‐hoc networks based on ant colony optimisation with global positioning system

Karia¹,

Godbole²

2013

IET Networks

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“…The algorithm consists of both reactive and proactive components. Villalba, L.J.G et al [8] presented variant of the AntHocNet protocol, which improves the performance in important parameters such as delivered packet ratio, the overhead in the number of packets and the overhead in the number of bytes.…”

Section: Related Workmentioning

confidence: 99%

An improved location-aware ant colony optimization based routing algorithm for MANETs

Bandgar¹,

Thorat²

2013

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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Mobile ad hoc network (MANET) is a decentralized, multi-hop, and dynamic network with least infrastructure. Therefore, it is necessary that they should be self organizing, self healing, self configuring and adaptable to frequent changes in network. Ant Colony Optimization presents a problem-solving paradigm that suits the dynamicity and mobility of mobile ad hoc networks. In this paper we propose a new routing algorithm AntHocNet-LS(equipped with Location Service) inspired from AntHocNet which rely on simple mobile agents and their collective intelligence for the task of routing. The algorithm performs better in major performance parameters such as packet delivery ratio and control overhead.

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“…The authors adapted DYMO to MAR-DYMO by modifying the main component (a parameterized probabilistic model also known as the pheromone model [Dorigo and Blum 2005]) of the ant colony optimization algorithm. The modifications involved two bio-inspired mechanisms: the pheromone deposit (amount of pheromone indicates the path quality) and the evaporation process (prevents fast convergence towards a suboptimal area [Villalba et al 2010]). Different evaporation rates (which will cause routes to be removed) are used for different links using the rationale that that links behave differently.…”

Section: Introductionmentioning

confidence: 99%

A state-dependent time evolving multi-constraint routing algorithm

Mellouk

Hoceini

Zeadally

2013

ACM Trans. Auton. Adapt. Syst.

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This article proposes a state-dependent routing algorithm based on a global optimization cost function whose parameters are learned from the real-time state of the network with no a priori model. The proposed approach samples, estimates, and builds the model of pertinent and important aspects of the network environment such as type of traffic, QoS policies, resources, etc. It is based on the trial/error paradigm combined with swarm-adaptive approaches. The global system uses a model that combines both a stochastic planned prenavigation for the exploration phase with a deterministic approach for the backward phase. We conducted a performance analysis of the proposed algorithm using OPNET based on several topologies such as the Nippon telephone and telegraph network. The simulation results obtained demonstrate substantial performance improvements over traditional routing approaches as well as the benefits of learning approaches for networks with dynamically changing traffic.

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Bio-inspired routing protocol for mobile ad hoc networks

Cited by 27 publications

References 4 publications

New approach for routing in mobile ad‐hoc networks based on ant colony optimisation with global positioning system

New approach for routing in mobile ad‐hoc networks based on ant colony optimisation with global positioning system

An improved location-aware ant colony optimization based routing algorithm for MANETs

A state-dependent time evolving multi-constraint routing algorithm

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