Energy‐efficient routing protocol to improve energy consumption in wireless sensors networks

Elkamel, Rabiaa; Cherif, Adnane

doi:10.1002/dac.3360

Cited by 20 publications

(13 citation statements)

References 25 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the CHs will determine the best location for the initial CH. In addition to that, Elkamel and Cherif [16] proposed a new technique in surmounting the energy usage issues. Their research aims were the incorporation of an enhanced algorithm, that entails the K-means in the generation of balanced energy in the clusters, in the addition of the utilization of the Gaussian elimination algorithm whilst the assignment of the CHs were being executed, which will ensure the dissemination of energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Using the K-means algorithm, the clusters have a better formation where the average distance of each node of the cluster is minimized. It is more efficient to balance the load of the network to distribute the nodes between clusters [16]. This algorithm is very beneficial to construct the clusters for various applications of WSN [8].…”

Section: Clustering Algorithms 31 K-means Algorithm (Km)mentioning

confidence: 99%

“…calculation of the distances between the two entities that would have otherwise effect an extreme compromise in terms of operation duration and memory space. So, there are a few techniques which are based on the K-means algorithm used in clustering[16]. Ray and De[8] recommended an Energy-Efficient Clustering Protocol based on K-means midpoint algorithm (EECPK-means) for WSN.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

View full text Add to dashboard Cite

The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Clustering Algorithms 31 K-means Algorithm (Km)mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Hassan¹,

Shah²,

Othman³

et al. 2020

IJECE

View full text Add to dashboard Cite

show abstract

“…For example, in feedback‐based chemical process automation and control, sensors measure the temperature; if the temperature crosses a certain threshold value, they inform actuators (eg, connected to a valve) to reduce the temperature to a desired value so that the process remains in a stable state. Such applications impose strict constraints on reliability and low latency because measurements of the sensors have to reach the actuator timely and reliably so that the act of controlling valve is performed on time.…”

Section: Role Of Wsn In Industry 40mentioning

confidence: 99%

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

Raza

Faheem

Guenes

2019

Int J Communication

View full text Add to dashboard Cite

Summary The vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. In this article, we explore IWSAN, its applications, requirements, challenges, and solutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards and technologies. A number of application‐specific MAC protocols developed to support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, we emphasize on existing challenges and present important future directions.

show abstract

“…1, LEACH can divide the entire network into different layers and use cluster head to form an upper hierarchy, which may simplify the management of clusters, decrease the number of routing packets, and conserve network energy to some extent [7]. Nevertheless, due to its dynamical but random cluster-head selection approach, it suffers from unsymmetrical cluster-head distribution, imbalanced energy burden, and roundabout data transmission [8].…”

Section: Introductionmentioning

confidence: 99%

A modified cluster-head selection algorithm in wireless sensor networks based on LEACH

Zhao

2018

J Wireless Com Network

View full text Add to dashboard Cite

In order to overcome drawbacks of unreasonable cluster-head selection and excessive energy consumption in wireless sensor networks (WSNs), a modified cluster-head selection algorithm based on LEACH (LEACH-M) was proposed. Based on distributed address assignment mechanism (DAAM) of ZigBee, both residual energy and network address of nodes were taken into account to optimize cluster-head threshold equation. Furthermore, by leveraging a cluster-head competitive mechanism, LEACH-M successfully balanced the network energy burden and dramatically improved energy efficiency. The simulation results in NS-2.35 show that the proposed algorithm can prolong the network lifetime, minimize the energy consumption, and increase the amount of data received at base station whether region is in a 100 × 100m 2 or in a 300 × 300m 2 .

show abstract

Energy‐efficient routing protocol to improve energy consumption in wireless sensors networks

Cited by 20 publications

References 25 publications

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

A modified cluster-head selection algorithm in wireless sensor networks based on LEACH

Contact Info

Product

Resources

About