An Energy Efficient Mesh LNN Routing Protocol Based on Ant Colony optimization

Marques, Vinícius de Figueiredo; Kniess, Janine; Parpinelli, Rafael Stubs

doi:10.1109/indin.2018.8471965

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…The simulation parameter settings are shown in Table 3 . These parameters in the table were defined empirically [ 21 ].…”

Section: Simulationsmentioning

confidence: 99%

“…IEMACO achieves the lowest end-to-end delay because the last hop node performs route discovery when the next hop node is unreachable when the link fails, which can significantly reduce the end-to-end delay. Since the probability of a route to be selected is calculated based on the sum of pheromone in the literature [ 21 ], the operation becomes simple, but the convergence speed is slow. In addition, because the energy metric takes into account the values of processing, transmission and reception, the curve changes less.…”

Section: Simulationsmentioning

confidence: 99%

“…Therefore, in this protocol, the selection of path is more inclined to nodes with more node energy than the shortest path, and the network energy consumption is balanced to some extent. In [ 21 ], Ant System concept is used in the mesh routing networks in order to discover the best routes while maintaining the distributed coordination between the nodes in Low-Power and Lossy network(LNNs) with low power consumption. The Ant Colony Optimization in [ 21 ] we called LNNACO improves packet transmission rate by considering energy consumption in the MMP ant colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 21 ], Ant System concept is used in the mesh routing networks in order to discover the best routes while maintaining the distributed coordination between the nodes in Low-Power and Lossy network(LNNs) with low power consumption. The Ant Colony Optimization in [ 21 ] we called LNNACO improves packet transmission rate by considering energy consumption in the MMP ant colony algorithm. Because the LNNACO algorithm does not consider the impact of high mobility of nodes, it is not suitable for networks with higher mobile speeds.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

Yang

Xia

et al. 2018

Sensors

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The mobile ad hoc network (MANET) is a multi-hop, non-central network composed of mobile terminals with self-organizing features. Aiming at the problem of extra energy consumption caused by node motion in MANETs, this paper proposes an improved energy and mobility ant colony optimization (IEMACO) routing algorithm. Firstly, the algorithm accelerates the convergence speed of the routing algorithm and reduces the number of route discovery packets by introducing an offset coefficient of the transition probability. Then, based on the energy consumption rate, the remaining lifetime of nodes (RLTn) is considered. The position and velocity information predicts the remaining lifetime of the link (RLTl). The algorithm combines RLTn and RLTl to design the pheromone generation method, which selects the better quality path according to the transition probability to ensure continuous data transmission. As a result, the energy consumption in the network is balanced. The simulation results show that compared to the Ad Hoc on-demand multipath distance vector (AOMDV) algorithm with multipath routing and the Ant Hoc Max-Min-Path (AntHocMMP) algorithm in consideration of node energy consumption and mobility, the IEMACO algorithm can reduce the frequency of route discovery and has lower end-to-end delay as well as packet loss rate especially when nodes move, and can extend the network lifetime.

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“…The simulation parameter settings are shown in Table 3 . These parameters in the table were defined empirically [ 21 ].…”

Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

Yang

Xia

et al. 2018

Sensors

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show abstract

“…Here, weighting factors of 2 characteristics in a protocol gain various metrics according to different cases, whereas pheromones of each node are expanded over residual energy (RE). In de Figueiredo Marques et al [14], ACO is utilized in mesh routing system to identify best routes while managing shared coordination among nodes with Low-Power and Lossy network (LNNs) with minimum energy application. Now, ACO is named LNNACO which enhances the transmission rate by considering the power utilization in Max-Min-Path(MMP) ACO method.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Quantum Glowworm Swarm Optimization based Clustering With Secure Routing Protocol for Mobile Adhoc Networks

Srinivas¹,

Patnaik

2021

Preprint

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Mobile adhoc network (MANETs) comprises a collection of independent, compact sized, and inexpensive sensor nodes, which are commonly used to sense the physical parameters in the geographical location and transmit it to the base station (BS). Since clustering and routing are considered as the commonly used energy efficient techniques, several metaheuristic algorithms have been employed to determine optimal cluster heads (CHs) and routes to destination. But most of the metaheuristic techniques have failed to achieve effective clustering and routing solutions in large search space and the chance of generating optimal solutions is also considerably reduced. To resolve these issues, this paper presents a new Metaheuristic Quantum Glowworm Swarm Optimization based Clustering with Secure Routing Protocol for MANET, named QGSOC-SRP. The presented QGSOC-SRP technique follows two stage processes, namely optimal CH selection and route selection. Firstly, the QGSO algorithm derives a fitness function using four variables such as energy, distance, node degree, and trust factor for optimal election of secure CHs. Secondly, the SRP using oppositional gravitational search algorithm (OGSA) is applied for the optimal selection of routes to BS. The traditional GSA is inspired by the law of gravity and interaction among masses. To improve the effectiveness of the GSA, OGSA is derived based on the oppositional based learning concept for population initialization and generation jumping. For validating the effective results of the presented OGSOC-SRP technique, a set of experiments were performed and the results are determined interms of distinct measures.

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Clustering with a high-performance secure routing protocol for mobile ad hoc networks

Srinivas

Patnaik

2022

J Supercomput

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An Energy Efficient Mesh LNN Routing Protocol Based on Ant Colony optimization

Cited by 7 publications

References 14 publications

Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks

Metaheuristic Quantum Glowworm Swarm Optimization based Clustering With Secure Routing Protocol for Mobile Adhoc Networks

Clustering with a high-performance secure routing protocol for mobile ad hoc networks

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