Modeling and minimization of energy consumption in wireless sensor networks

Abo‐Zahhad, Mohammed; Farrag, Mohammed; Ali, Abdelhay

doi:10.1109/icecs.2015.7440412

Cited by 11 publications

(11 citation statements)

References 7 publications

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“…Problem in HDWSNs e EA is one of the most popular metaheuristic algorithms, which attempts to mimic the procedure of natural selection [19]. It is also an exploit method that mimics the procedure of natural selection in nature, which is an optimization algorithm to search an input region while minimizing a result function under given restraints [23,52,53].…”

Section: Ccpea-based Low-energy Clusteringmentioning

confidence: 99%

“…Due to their size limitation, small wireless sensors have constrained clustering ability [17]. Considering that the power supply capability of sensors is limited, low-energy clusters play a vital role in minimizing communication energy consumption, while most research for low-power clustering is corresponding to a low-power clustering algorithm [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Yang

Zhou

2021

Scientific Programming

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Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

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Section: Ccpea-based Low-energy Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Yang

Zhou

2021

Scientific Programming

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“…Given the above architecture, a high level communication scheme is imagined where the leaves "puncture" the established communication between main nodes, to upload data to their respective hubs. This approach allows the system to take full advantage of the channel bandwidth available, enabling high data rate with simple communication scheme, offering a better energy efficiency [9].…”

Section: Heartbeat-based Synchronization Principle a Human Intranet A...mentioning

confidence: 99%

Heartbeat-Based Synchronization Scheme for the Human Intranet: Modeling and Analysis

Benarrouch

Moin

Solt

et al. 2020

2020 IEEE International Symposium on Circuits and Systems (ISCAS)

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Sharing a common clock signal among the nodes is crucial for communication in synchronized networks. This work presents a heartbeat-based synchronization scheme for body-worn nodes. The principles of this coordination technique combined with a puncture-based communication method are introduced. Theoretical models of the hardware blocks are presented, outlining the impact of their specifications on the system. Moreover, we evaluate the synchronization efficiency in simulation and compare with a duty-cycled receiver topology. Improvement in power consumption of at least 26% and tight latency control are highlighted at no cost on the channel availability.

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“…In [15], the authors have proposed an energy consumption model for WSNs based on physical layer parameters by calculating the total energy that is required for successfully received one bit over Rayleigh fading channel.…”

Section: Related Workmentioning

confidence: 99%

Minimising the impact of IPv6 neighbour discovery messages on energy consumption in small objects networks

Ksimi

Leghris

2018

IET Networks

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Nowadays, it is important to evaluate the use of IPv6 wireless networks. Indeed, the implementation of IPv6 mechanisms and features must be examined. IPv6 requires many objects exchanges like IPv6 routing information which could be communicated by different messages multicast. These messages are too frequent; they could impact energy consumption related to battery-powered objects. Therefore, the energy consumption is nowadays, one of the most important issues in small object networks with IPv6. Thus, the aim of this study is to suggest a new algorithm called minimising energy consumption under connectivity constraint. The performance evaluation is based on several parameters that would help in maintaining connectivity between the mobile objects and their default router. The results showed that energy consumption could be minimised by optimising IPv6 multicast messages. Overall, this method results a significant effectiveness and might be used as a future wireless networks improvements.

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Modeling and minimization of energy consumption in wireless sensor networks

Cited by 11 publications

References 7 publications

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Clone Chaotic Parallel Evolutionary Algorithm for Low-Energy Clustering in High-Density Wireless Sensor Networks

Heartbeat-Based Synchronization Scheme for the Human Intranet: Modeling and Analysis

Minimising the impact of IPv6 neighbour discovery messages on energy consumption in small objects networks

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