Energy consumption reduction for asynchronous message-passing applications

Fanfakh, Ahmed; Charr, Jean‐Claude; Couturier, Raphaël; Giersch, Arnaud

doi:10.1007/s11227-016-1926-1

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Both synchronous and asynchronous protocols have been planned to get better network connectivity in this research, each with its own set of benefits and drawbacks. Many academics have proposed approaches to increase the energy savings of the sensor node by modifying only a few of these technological properties [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Basha¹,

Aswini²,

Aarthini³

et al. 2023

Computer Systems Science and Engineering

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Wireless Sensor Network (WSN) technology is the real-time application that is growing rapidly as the result of smart environments. Battery power is one of the most significant resources in WSN. For enhancing a power factor, the clustering techniques are used. During the forward of data in WSN, more power is consumed. In the existing system, it works with Load Balanced Clustering Method (LBCM) and provides the lifespan of the network with scalability and reliability. In the existing system, it does not deal with end-to-end delay and delivery of packets. For overcoming these issues in WSN, the proposed Genetic Algorithm based on Chicken Swarm Optimization (GA-CSO) with Load Balanced Clustering Method (LBCM) is used. Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function. Chicken Swarm Optimization (CSO) helps to solve the complex optimization problems. Also, it consists of chickens, hens, and rooster. It divides the chicken into clusters. Load Balanced Clustering Method (LBCM) maintains the energy during communication among the sensor nodes and also it balances the load in the gateways. The proposed GA-CSO with LBCM improves the lifespan of the network. Moreover, it minimizes the energy consumption and also balances the load over the network. The proposed method outperforms by using the following metrics such as energy efficiency, ratio of packet delivery, throughput of the network, lifetime of the sensor nodes. Therefore, the evaluation result shows the energy efficiency that has achieved 83.56% and the delivery ratio of the packet has reached 99.12%. Also, it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

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Section: Related Workmentioning

confidence: 99%

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Basha¹,

Aswini²,

Aarthini³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…The cluster communication consumes high energy by nodes. This makes research to look for next solution in article [23]. It proposes centralized load balancing with energy efficient computation algorithm (CELBA).…”

Section: Related Workmentioning

confidence: 99%

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

Almasoud¹,

Eisa²,

Obayya³

et al. 2022

Computers, Materials &Amp; Continua

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In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does not work with the large -scale wireless sensor networks, therefore it causes reliability and energy consumption problems. To overcome this, the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic (LBC-COFL) algorithm is used. The main objective is to perform the lifespan by balancing the gateways with the load of less energy. The proposed algorithm is evaluated using the metrics such as energy consumption, throughput, central tendency, network lifespan, and total energy utilization.

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“…Two energy-aware scheduling algorithms for independent tasks have been proposed in the work of Sajid et al 24 The algorithms schedule tasks to the processors based on a weighted aggregation cost function, and then, a task migration phase follows. Many other scheduling algorithms for DVFS environments have been proposed in the literature (see other works [25][26][27][28] ). An earlier version of this article has concentrated on an experimental evaluation of T-schedules and E-schedules.…”

Section: Related Workmentioning

confidence: 99%

A scheduling selection process for energy‐efficient task execution on DVFS processors

Rauber

Rünger

2018

Concurrency and Computation

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Summary The efficient execution of parallel programs with respect to execution time and energy consumption is a major concern, and often, scheduling methods are used to achieve a good performance. In this article, we consider the problem of scheduling a set of independent tasks on a parallel system with homogeneous execution units providing frequency scaling. The set of tasks has the property that the tasks exhibit a task‐specific inhomogeneous and non‐linear behavior of their specific time‐energy relation. In addition, it is assumed that execution time and energy consumption behave in a non‐linear manner with respect to frequency scaling. For the assignment of these tasks to execution units, we propose a scheduling selection process combining scheduling algorithms, which determine a task assignment, with a subsequent selection of frequency scaling, which we call schedule execution modes. This process builds a rich set of alternative schedule execution modes from which efficient (or Pareto‐optimal) schedule execution modes can be selected. Experiments are done for the SPEC CPU benchmarks. Experimental results illustrate that the enriched scheduling process leads to task assignments resulting in an efficient execution on DVFS processors.

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Energy consumption reduction for asynchronous message-passing applications

Cited by 9 publications

References 35 publications

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

A scheduling selection process for energy‐efficient task execution on DVFS processors

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