Improved multi-objective weighted clustering algorithm in Wireless Sensor Network

Ouchitachen, Hicham; Haïr, Abdellatif; Idrissi, Najlae

doi:10.1016/j.eij.2016.06.001

Cited by 34 publications

(15 citation statements)

References 29 publications

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“…Considering the importance of routing in WSNs for balancing the energy consumption of the nodes, the proposed method is compared with a previous method in terms of energy consumption and other evaluation measures. To this end, the proposed method is compared with [5,11,29] in terms of energy consumption and network lifetime. Figure 17 compares the proposed method with a previous method in terms of average energy consumption.…”

Section: Comparison Of the Proposed Methods With Previousmentioning

confidence: 99%

“…The CH nodes collect data from all sensor nodes of the cluster and transmit it to the base station after data aggregation and removing the duplicate data. Thus, the CH has to organize the network, collect data, and transmit data from the sensor nodes to the sink and the base station and it consumes more energy compared to other nodes [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

et al. 2021

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With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

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Section: Comparison Of the Proposed Methods With Previousmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

et al. 2021

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“…With increased variety of devices came the notion of grouping the devices giving rise to cluster based algorithms. WCA [15], PWCA [17] and DWCA [13] unveiled the idea of device clustering with master node formation to carry out smooth routing within clusters. The approach proved to be highly effective over conventional ones in controlling the broadcast and reducing the forwarding rate by way of permissions.…”

Section: Related Contributionsmentioning

confidence: 99%

MCDM Approach to Improve the Quality of Broadcasting Technique in Wireless Ad-hoc Networks

Ahmad¹,

Sethi²,

Mg³

et al. 2021

Preprint

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Contemporary research work in the field of Wireless Ad-hoc Networks (WANETs) are focused on most important issue of performance, minimize the congestion in the network. A number of research proposals have come up with innovative query transmission approaches in order to scale down congestion, route latency and energy consumption to the feasible extent. It has been shown experimentally that these approaches have mainly focused on checking propagation of route query using query broadcast repealing technique which unfortunately paves way to additional routing overhead. This leads to increased energy-consumption which offsets the benefits. This paper introduces a self-aware query-broadcast approach which is dynamic in nature and obtains desirable path in discovery phase without counterbalancing the advantages reaped from confining the request zones. The proposed approach relies on Analytic Hierarchy Process (AHP), which utilizes the routing metrics in estimating the weighing factors for combined weight metrics. For evaluation of the proposed technique, critical and analytical assessment is done in terms of query-diffusion, packet-delivery ratio and route-latency. The results reveal the proposed approach is ideal, optimal and highly practical for application in WANETs.

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“…While the optimal location of EHs and the layout optimisation of large-scale integrated energy systems are seldom considered in the existing research. As an effective solution, the clustering algorithm has been widely applied in industrial fields to reduce the complexity of a large-scale system [20][21][22][23]. To facilitate the layout optimisation of large-scale offshore wind farms, a fuzzy c-means clustering algorithm is used to allocate each wind turbine to the geographically nearest substation [24,25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive clustering‐based hierarchical layout optimisation for large‐scale integrated energy systems

Hui

Shi

Wang

et al. 2020

IET Renewable Power Generation

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Different energy systems are generally planned and operated independently, which result in the low energy utilisation, weak self-healing ability and low system reliability. Therefore, an adaptive clustering-based hierarchical layout optimisation method is proposed for a large-scale integrated energy system, considering energy balance, transmission losses and construction costs. First, an adaptive clustering partition method based on energy balance and load moments is proposed to determine the optimal location of energy hubs and to allocate each distributed generation and load to different energy hubs, forming multiple regional integrated energy systems adaptively. Then, the proposed hierarchical layout optimisation model is formulated as to find the modified minimum spanning tree of the regional integrated energy system and multi-regional integrated energy systems, respectively, to construct an economical and reliable interconnection network. Finally, the effectiveness of the optimisation model and strategy is verified by simulations.

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Improved multi-objective weighted clustering algorithm in Wireless Sensor Network

Cited by 34 publications

References 29 publications

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

MCDM Approach to Improve the Quality of Broadcasting Technique in Wireless Ad-hoc Networks

Adaptive clustering‐based hierarchical layout optimisation for large‐scale integrated energy systems

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