ACS LEACH protocol using optimized clustering and improved orthogonal matching pursuit in WSN

Patil, Nandini S.; Parveen, Asma

doi:10.1007/s41870-021-00791-y

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…Patil and Parveen [11] presented an Adoptive Compressed Sensing (ACS) to resolve problems of hybrid CS. e presented model chooses an ideal size of the cluster and considers the relationship among the several nodes and measurement M. Furthermore, the data collection method uses the traditional Fourier transform to attain better stability with lower computation di culty.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2) Read input parameter of SSA (3) Create random position for n number of squirrels (4) Estimate tness of all the squirrel positions (5) Arrange the position of squirrel individual in ascending order on the basis of cost function value (6) Allocate the squirrel individual on normal tree, hickory nut tree, and acorn nut tree (7) Arbitrarily chooses squirrel individual from normal tree for shifting towards hickory nut trees and transmits the remaining squirrels to acorn nut trees (8) While (End condition is false) (9) For t 1 to n 1 (n 1 Number of squirrel individuals i.e., gliding from acorn tree to hickory nut trees) (10) if r l ≥ P dp (11) Upgrade the location of squirrel individuals (12) else (13) Arbitrarily create the location of squirrel individuals within the searching area. ( 14) end ( 15) end ( 16) For t 1 to n 2 (n 2 Number of squirrel individuals i.e., gliding from normal tree to acorn tree) (17) if r 2 ≥ P dp (18) Upgrade the location of squirrel individuals (19) else (20) Arbitrarily create the location of squirrel individuals within the searching area.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

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Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

A-Matarneh

Alqaralleh

Aldhaban

et al. 2022

Computational Intelligence and Neuroscience

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Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

A-Matarneh

Alqaralleh

Aldhaban

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…To enhance the availability of WSNs, clustering techniques are introduced to play a vital role in reducing the number of transmitted data packets and in conserving the limited power resources [Patil and Parveen, 2022]. Using clustering, the network is divided into an optimized number of clusters with a lower number of related sensing nodes (SNs) whose function is just to collect and forwards data to their equivalent cluster header nodes [Abu Salem and Shudifat, 2019].…”

Section: Introductionmentioning

confidence: 99%

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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“…To enhance the availability of WSNs and conserve energy, clustering techniques are introduced to play a vital role in reducing the number of transmitted data packets and in preserving the limited power resources [3,4]. Using clustering, a WSN is divided into a number of clusters with a lower number of related sensing nodes (SNs), whose function is just to collect and forward data to their equivalent cluster header nodes [5].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

Saleh,

Alansari,

Farouk

et al. 2023

Applied Sciences

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A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor 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 the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

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ACS LEACH protocol using optimized clustering and improved orthogonal matching pursuit in WSN

Cited by 11 publications

References 27 publications

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

An Optimized Hierarchal Cluster Formation Approach for Smart Cities

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

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