Energy centroid clustering algorithm to enhance the network lifetime of wireless sensor networks

Loganathan, Sathyapriya; Arumugam, Jawahar

doi:10.1007/s11045-019-00687-y

Cited by 14 publications

(9 citation statements)

References 43 publications

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“…Random initialization generates executable determinants and initial data sets for each set of executable solutions. Calculate the fitness value; perform genetic operations in the initial data set, such as selective crossmutation; apply the fitness function to new individuals to calculate the fitness value; and select new individuals using simulated annealing algo-rithm [20], design the maximum number of loop iterations [21]. Determine whether the current temperature is lower than the end temperature.…”

Section: Multiobjective Flexible Workhopmentioning

confidence: 99%

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Huang

Wang

2022

Wireless Communications and Mobile Computing

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Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

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Section: Multiobjective Flexible Workhopmentioning

confidence: 99%

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Huang

Wang

2022

Wireless Communications and Mobile Computing

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“…Elmasry et al [11] propose a scalable energy-efficient algorithm that adopts a three-layer hierarchy to minimize workload on cluster-heads and randomize the clusterhead selection procedure. Loganathan et al [23] propose an energy-efficient clustering algorithm that uses a distance centroid algorithm to design each cluster. The energy centroid of each cluster is computed and employed together with the energy threshold of each SN to choose a cluster-head.…”

Section: Background and Related Workmentioning

confidence: 99%

Adaptive Probabilistic Model for Energy-Efficient Distance-based Clustering in WSNs (Adapt-P): A LEACH-based Analytical Study

Suleiman,

Hamdan

2021

Preprint

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Network lifetime and energy consumption of data transmission have been primary Quality of Service (QoS) obligations in Wireless Sensor Networks (WSNs). The environment of a WSN is often organized into clusters to mitigate the management complexity of such obligations. However, the distance between Sensor Nodes (SNs) and the number of clusters per round are vital factors that affect QoS performance of a WSN. The designer's conundrum resolves around the desire to sustain a balance between the limited residual energy of SNs and the demand for prolonged network lifetime. Any imbalance in controlling conflicting objectives may result in either QoS penalties due to draining SN energies which leaves WSN's environment unevenly covered, or an over-cost environment that is significantly difficult to distribute and operate. Low-Energy Adaptive Clustering Hierarchy (LEACH) is a well-known distributed clustering algorithm proposed to tackle such difficulties. Multiple LEACH-based algorithms have also been proposed to enhance QoS requirements. Proposed algorithms typically focus on residual energies of SNs to compute a probability function that selects cluster-heads. Some algorithms form an optimal energy-efficient path toward a destination SN. Nevertheless, these algorithms do not consider variations in network's state at run-time. Such a state changes in an adaptive manner according to existing network structures and conditions. Thus, cluster-heads per round are not elected adaptively depending on the state and distances between SNs. To tackle such complications, this paper proposes an energy-efficient adaptive distance-based clustering called Adapt-P, in which an adaptive probability function is developed to formulate clusters. A near-optimal distance between each cluster-head and its cluster-members is formulated so that energy consumption of the network is mitigated and accordingly the network lifetime is maximized. Distances between and residual energies of SNs are employed to obtain a maximum number of cluster-heads to be elected per round. The cluster-head selection probability is adapted at the end of each round based on the maximum number of cluster-heads permitted per round found a priori and the number of existing alive SNs in the network. The Adapt-P based algorithms proposed in this paper improve the performance of LEACH algorithm in term adaptivity and network lifetime.

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“…But their assumption that every gateway node should be in the range of its neighbor gateway node in their proposed topology proves high algorithm complexity and limits user defined random sensor node deployment process. The term energy centroid for sensor networks is used in [21] where each cluster is designed to own 25% of sensor nodes using distance centroid algorithm. Here CH selection is based on energy centroid while the communication between CHs and the basestation is distance centric.…”

Section: Centroid (Midpoint) Based Routing Approachmentioning

confidence: 99%

“…In the zone cluster, if the weight of ZC is known then position of centroid is calculated using Eq. (20) and (21). In the field of mathematics, calculation for centroid in cartesian co-ordinate system ( ̅̅̅̅ , ̅ ) is calculated as…”

Section: Zone Energy Centroid Selection Algorithmmentioning

confidence: 99%

Cluster Centroid-Based Energy Efficient Routing Protocol for WSN-Assisted IoT

Kumar¹,

Bala²

2020

Adv. sci. technol. eng. syst. j.

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Wireless sensor network is highly resource constrained, where energy efficiency and network lifetime plays a major role for its sustenance. As the sensor nodes are battery operated and deployed in hostile environments, either recharging or replacement of batteries in sensor nodes is not possible after its deployment in inaccessible areas. In such condition, energy is the vital factor for the survival of sensor node in the sensing field. In order to increase the network lifetime and balance the energy consumption, robust routing protocols are required. The proposed network routing has three phases: 1. Network initiation phase to create a zone which enables the communication among local nodes 2. Zone coordinator selection phase algorithm to form zone cluster and its re-election procedure and 3. Zone head selection with its replacement phase based on energy centroid positional information and distance to the basestation to distribute load equally among zone coordinators , local sensor nodes. The data path between zone heads and basestation is distance centric and is optimized at one hop and dual hop levels to avoid data packet loss at zoneheads. Each zone is designed to own atmost ¼ rth of deployed sensor node count through uniform random deployment. Simulations results when basestation is placed inside sensing field indicates that the proposed network algorithm outperforms when benchmarked against similar protocols like conventional LEACH, Traditional PEGASIS, existing PRRP, ES3 protocols in terms of performance metrics like Network energy consumption, Average energy consumed by sensor node, Packet delivery ratio, Packet loss percentage and Network throughput.

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Energy centroid clustering algorithm to enhance the network lifetime of wireless sensor networks

Cited by 14 publications

References 43 publications

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Adaptive Probabilistic Model for Energy-Efficient Distance-based Clustering in WSNs (Adapt-P): A LEACH-based Analytical Study

Cluster Centroid-Based Energy Efficient Routing Protocol for WSN-Assisted IoT

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