An improved energy-efficient head election protocol for clustering techniques of wireless sensor network (June 2020)

Saleh, Safa'a S.; Mabrouk, Tamer F.; Tarabishi, Rana A.

doi:10.1016/j.eij.2021.01.003

Cited by 36 publications

(4 citation statements)

References 23 publications

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“…Depending on the assumption that the consumed energy rises by increasing the distance, much effort is dedicated to enhancing the setup phase of LEACH by selecting the shortest distance to minimize energy consumption [Aashif et al, 2019;Akshay et al, 2020;Daanoune et al, 2019]. As stated in [Saleh et al, 2021], the main problem of most works recommending LEACH is the assumption that distance is the only factor that affects energy consumption. This hypothesis does not necessarily propose an efficient method of selecting the proper CHs for SNs that can extend the network durability.…”

Section: Related Workmentioning

confidence: 99%

“…The communication cost is the major energy consumer in nodes [Sunil and Eyong, 2018]. While the sensing and computational costs are fixed for each node, the communication cost is affected by many factors, such as the number of transmitted data packets (network traffic load), the distance of transmission, and the bandwidth of the transmission link [Saleh et al, 2021]. Therefore, an effective energy optimization technique is always needed to minimize the energy consumption in the communication process in WSNs.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Hierarchal Cluster Formation Approach for Smart Cities

Saleh

Alansari

Hamiaz

et al. 2023

Preprint

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A smart city uses Internet of Things (IoT) to enhance the management of many daily routine tasks such as traffic system, energy consumption, and waste collection. The Quality of Service (QoS) of these daily routine tasks are based on an assistive observation system. Wireless Sensors Networks (WSNs) as the key component of IoT are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of surveillance subsystems, many clustering techniques are introduced. The low-power adaptive clustering protocol is a key technique of the Internet of Things (IoT). However, this protocol has deterring limitations, especially in the cluster formation step, which negatively affect many nodes. Considering this problem, the current research proposes an Opt-LEACH system that attempts to optimize the low-energy adaptive clustering hierarchy. The proposed system depends on the suitability of residual energy in nodes to cover the communication energy with CHs as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weight the accepted CHs and adjust the optimized size of clusters in the secondary competition. The impact factor of each candidate member node is applied in the third competition. The simulation results clarify the ability of Opt-LEACH to improve the cluster formation and to enhance communication within clusters. The advantages and efficiency of Opt-LEACH are observed via the increased number of surviving nodes, increased residual energy of nodes and higher network lifetime.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimized Hierarchal Cluster Formation Approach for Smart Cities

Saleh

Alansari

Hamiaz

et al. 2023

Preprint

Self Cite

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“…To overcome these challenges, this research presents a Gateway Clustering Energy-Efficient Centroid-(GCEEC-) proposed scheme, in which the CH is chosen from centroid region and gateways nodes from each cluster [20]. Furthermore, LEACH has some flaws, particularly during the setup phase, when CH is chosen at random and is improved by determining a CH that consumes the least amount of energy [21,22]. The High-Quality Clustering Algorithm (HQCA) is a process for making elevated clusters [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Localization Based Distributed Selective Cluster Scheduling for Sustainable Energy Consumption and Efficient Data Transmission

2022

IJIES

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Wireless Sensor Network (WSN) is a transitional layer in Internet of Things (IoT) framework, and its success is determined by the distribution strategy deprived of compromising throughput or energy efficacy. The network is split in sets of sensors in order to utilize resources and minimize energy utilization. Clustering is effective method to lessen the energy utilization in WSN. As WSNs have substantial nodes dispersed at different locations, and they may or might not remain static at their own location, localization is a key challenge. Numerous distance and location estimate algorithms are applied in localization. Throughout transmission, the received signal strength (RSS) plays an essential part in minimising node energy usage. To harvest the energy of network for optimal data transmission, appropriate use of RSS values with clustering is essential. In this paper, we concentrate on Localization Based Distributed Selective Cluster Scheduling in WSN that will demonstrate how to lessen placement mistakes, enhance accuracy, and balance energy consumption by varying cluster size. The method achieves appropriate cluster head dispersion amid nodes and prevents recurrent cluster head selection associated with the input nodes' Signal Intensity and remaining energy. SECCP assurances an advanced packet distribution proportion of 95.25 %, energy performance of 0.8226J with added data transmissions and throughput of SECCP as 6.059 x104 Kbps to I-SEP, EBCT, ECSS, and EDCHSA as 2.698x10 4 Kbps, 6.085 x10 4 Kbps, 1.128 x10 4 Kbps and 0.5434 x10 4 Kbps respectively.

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“…The implementation of a WSN is influenced by several design factors such as scalability, fault tolerant, topological layout, transmission media, and power consumption [37,3,26]. Fault tolerant, for example, ensures that a failure of a SN caused by a lack of power or physical damage should not affect the rest of SNs or severely degrade the overall performance.…”

mentioning

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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An improved energy-efficient head election protocol for clustering techniques of wireless sensor network (June 2020)

Cited by 36 publications

References 23 publications

An Optimized Hierarchal Cluster Formation Approach for Smart Cities

An Optimized Hierarchal Cluster Formation Approach for Smart Cities

Localization Based Distributed Selective Cluster Scheduling for Sustainable Energy Consumption and Efficient Data Transmission

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

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