PSO-DEC: PSO based deterministic energy efficient clustering protocol for IoT

Dohare, Indu; Singh, Karan

doi:10.1080/09720529.2019.1695898

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Accuracy is an essential factor in measuring the performance of the learning model. We conduct experiments with the proposed scheme, and the results are compared with the existing IDS security models like Support vector machines [19], PSO [20], FFSO [21] , GA [22] as shown in figure 3 The value of the counter that each node keeps track of impacts how long it takes to identify a rogue node. Every time a generally given the total score of 0, it increases its buffer capacity by one and flags the transmitted node as hostile on the source nodes.…”

Section: Resultsmentioning

confidence: 99%

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Hemalatha¹,

Dhanalakshmi

2022

IJRITCC

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Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison.

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Section: Resultsmentioning

confidence: 99%

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Hemalatha¹,

Dhanalakshmi

2022

IJRITCC

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“…Dohare and Singh [31] introduced the PSO-based deterministic energy-efficient clustering (PSODEC) in IoT. Based on the largest remaining energy, this method chooses the CH node among the CMs.…”

Section: Related Workmentioning

confidence: 99%

“…This phase is repeated in each round, as shown in Fig. (2), because PSODEC [31] The K-means technique is used to build the cluster. Then,the PSODEC method is used to select the CH node per cluster.…”

Section: Proposed Fuzzy Logic Leach Technique-based Psomentioning

confidence: 99%

Enhancing the Lifetime of Wireless Sensor Networks Using Fuzzy Logic LEACH Technique-Based Particle Swarm Optimization

et al. 2022

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Wireless sensor networks (WSNs) have attracted significant attention because of their widespread use in health care, habitat tracking, disaster prevention, agriculture, monitoring areas, fire tracking, and other real-life applications. The lifetime of WSNs must be prolonged to increase their use for various applications. One of the most effective methods for improving the network's lifetime is clustering with the optimal cluster head (CH). This study proposes a fuzzy Logic (FL) low-energy adaptive clustering hierarchy (LEACH) technique-based particle swarm optimization (PSO). It employs hybrid PSO and a K-means clustering algorithm for cluster formation. It selects the primary CH (PCH) and secondary CH (SCH) using FL. Extensive simulations were conducted using a simulation program to validate the proposed protocol's performance. Furthermore, the proposed protocol was compared with traditional algorithms, such as fuzzy c-means (FCM) clustering and FLS-based CH selection to enhance the sustainability of WSNs for environmental monitoring applications, LEACH-Fuzzy clustering protocol, and LEACH based on energy consumption equilibrium. The results confirmed that the proposed protocol efficiently balances energy consumption to improve wireless sensor network performance and to maximize throughput. The simulated results indicated that network lifetime was improved by more than 46% and packet transmission by 17.6%.

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“…Dohare and Singh [37] proposed the PSO-based deterministic energy-efficient clustering (PSODEC) in IoT. This algorithm selects the CH node from the cluster nodes based on maximum remaining energy.…”

Section: Related Workmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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PSO-DEC: PSO based deterministic energy efficient clustering protocol for IoT

Cited by 13 publications

References 14 publications

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT

Enhancing the Lifetime of Wireless Sensor Networks Using Fuzzy Logic LEACH Technique-Based Particle Swarm Optimization

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

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