Effect of optimal cluster head placement in MANET through multi objective GA

Sett, Sujoy; Thakurta, Parag Kumar Guha

doi:10.1109/icacea.2015.7164819

Cited by 11 publications

(8 citation statements)

References 10 publications

(10 reference statements)

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“…MOGA [44] is a genetic algorithm (GA) proposed to solve efficiently the problem of multi-objective optimization (MOO). The energy consumption of the CH depends on the level of several nodes in a cluster and overhead it has to handle for intra-cluster communication.…”

Section: ) Multi Objective Genetic Algorithm For Optimal Cluster Heamentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

2020

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Mobile ad hoc networks (MANETs) have recently received a lot of attention from researchers due to their ease of deployment and versatility. Clustering is one of the most efficient ways to coordinate routing in MANETs to improve network performance using different attributes and metrics. In this paper, the authors have extensively studied clustering schemes and divided the schemes into multiple types based on the Cluster Head (CH) selection criteria, which provides a good understanding of how each type of clustering algorithm differs from each other. The authors analyzed the performance of existing schemes based on the quality of service (QoS) metrics. Based on findings, the authors clarified some important tradeoffs between QoS metrics and also established some important factors influencing the efficiency of clustering schemes. These studies also contain open research challenges and proposed solutions to improve the performance of clustering schemes. INDEX TERMS Clustering, cluster head, MANETs, protocol and schemes.

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Section: ) Multi Objective Genetic Algorithm For Optimal Cluster Heamentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

2020

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“…Cluster based routing in MANETs can be differently classified. It may be classified as one hop clustering [25], [29], [30], [40], [45]- [53], multi hop clustering [28], [31], [34]- [36], [39], [42], [43], [54], [55], transmission range or signal strength based clustering [32], [33], [56]- [58] and zone based clustering [38]. MANETs can be classified based on a mobility model such as group mobility [51], [56], [59], [60], regular mobility [46] and random mobility [25], [28], [29], [31], [34], [35], [39], [40], [42], [44], [45], [48], [50], [52], [53], [55], [58], [61]- [64].…”

Section: How To Classify Clustering Schemes?mentioning

confidence: 99%

“…To prolong the network life and decrease the energy consumption, energy efficient clustering techniques was proposed [31], [37], [44], [47], [54], [62], [66]. To distribute the load on the whole network, load balancing based cluster formation techniques are discussed [38], [90], [99]. To avoid the selfish behavior of nodes during cluster formation and communication process, cooperative and Trust based clustering techniques are explained [36], [53], [61], [64], [65].…”

Section: How To Classify Clustering Schemes?mentioning

confidence: 99%

State-of-the-Art Clustering Schemes in Mobile Ad Hoc Networks: Objectives, Challenges, and Future Directions

et al. 2019

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Mobile ad hoc networks (MANETs) are self-organized networks without any fixed infrastructure. The topology changes are very frequent in MANETs due to nodes' mobility. The topology maintenance creates an extra overhead, as the mobility information of a single node is shared with all nodes in the network. To address the topology maintenance overhead problem in MANETs, the researchers proposed different cluster-based algorithms to reduce the size of a routing table. The clusters are formed to locally adjust the topology changes within the cluster. If a node wants to communicate with a node outside the cluster, it only communicates with its cluster head (CH). The CH communicates with other CHs to transmit data toward the destination. To efficiently utilize the clustering mechanism in MANETs, stable and balanced clusters are required. To form good quality and optimized clusters, some metrics, such as relative mobility (node speed and direction), node degree, residual energy, communication workload, and neighbor's behavior, are required. In this paper, we present a comprehensive survey of recent CAs in MANETs. We also present the objectives, goals, and contributions of recent research. Similarly, the findings, challenges, and future directions are stated. The validation of each proposed work is analyzed critically in terms of the mobility model, the simulation tool used during simulation, simulation metrics, and the performance metrics used in the validation process. INDEX TERMS MANETs, clustering algorithms, routing, cluster based MANET.

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“…The path with minimum energy was considered as BFL to perform multipath data transmission. A few efforts [20] have exploited clustering concept to perform routing decision where Genetic Algorithm (GA) was applied for Cluster Head (CH) selection. Clustering with node trustworthiness concept was applied in [21] to achieve reliable data transmission over MANETs.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization Assisted Network Condition Aware QOS-Routing Protocol for MANETS: MNCQM

K¹,

Siddesh²

2019

IJCNC

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The exponential rise in wireless communication systems and allied applications has revitalized academiaindustries to achieve more efficient data transmission system to meet Quality-of-Service (QoS) demands. Amongst major wireless communication techniques, Mobile Ad-hoc Network (MANET) is found potential to provide decentralized and infrastructure less communication among multiple distributed nodes across network region. However, dynamic network conditions such as changing topology, congestion, packet drop, intrusion possibilities etc often make MANET's routing a tedious task. On the other hand, mobile network feature broadens the horizon for intruders to penetrate the network and causes performance degradation. Unlike classical MANET protocols where major efforts have been made on single network parameter based routing decision, this research paper proposes a novel Elitist Genetic Algorithm (EGA) Multi-Objective Optimization assisted Network Condition Aware QoS-Routing Protocol for Mobile Ad-hoc Networks (MNCQM). Our proposed MNCQM protocol exhibits two phase implementation where at first it performs node-profiling under dynamic network topology for which three factors; irregular MAC information exchange, queuing overflow and topological variations have been considered. Towards this objective node features like Packet Forwarding Probability (PFP) at the MAC layer, Success Probability of Data Transmission (SPDT) of a neighboring node, and Probability of Successful Data Delivery (PSDD) have been obtained to estimate Node-Trustworthiness Index (NTI), which is further used to eliminate untrustworthy nodes. In the second phase of implementation, a novel Evolutionary Computing assisted nondisjoint best forwarding path selection model is developed that exploits node's and allied link's connectivity and availability features to identify the quasi-sub-optimal forwarding paths. EGA algorithm intends to reduce hop-counts, connectivity-loss and node or link unavailability to estimate best forwarding node. One key feature of the proposed model is dual-supplementary forwarding path selection that enables alternate path formation in case of link outage and thus avoids any iterative network discovery phase.

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Effect of optimal cluster head placement in MANET through multi objective GA

Cited by 11 publications

References 10 publications

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

Notice of Violation of IEEE Publication Principles: Clustering Schemes in MANETs: Performance Evaluation, Open Challenges, and Proposed Solutions

State-of-the-Art Clustering Schemes in Mobile Ad Hoc Networks: Objectives, Challenges, and Future Directions

Multi-Objective Optimization Assisted Network Condition Aware QOS-Routing Protocol for MANETS: MNCQM

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