Particle Swarm Optimization-Based Unequal and Fault Tolerant Clustering Protocol for Wireless Sensor Networks

Kaur, Tarunpreet; Kumar, Dilip

doi:10.1109/jsen.2018.2828099

Cited by 124 publications

(60 citation statements)

References 17 publications

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“…For example in [181], the real-time retransmission algorithm based on network coding (NCRR) was proposed to make the average number of transmissions as less as possible, the algorithm could effectively reduce the average number of transmissions and improve the transmission efficiency. In [182] the authors proposed a particle swarm optimization (PSO)-based unequal and fault tolerant clustering protocol, the proposed protocol addresses imbalanced clustering and fault tolerance issues in the existing energy-balanced unequal clustering protocol for the longrun operation of the network. For the problem of deploying a WSN with a specified minimum level of reliability at a minimum deployment cost is addressed.…”

Section: ) Link Retransmits Methodsmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: ) Link Retransmits Methodsmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…The simulation results showed that the proposed method minimized the energy and the packet loss with reduced delay. Unbalanced clustering and fault tolerance are considered in the particle swarm optimization-based unequal and faulttolerant clustering (PSO-UFC) protocol [25]. To solve the hot spot problem, the proposed protocol considers an additional CH, which is referred to as the secondary cluster head (SCH) for CH nodes.…”

Section: A Related Workmentioning

confidence: 99%

Fault Tolerance Mechanism Combining Static Backup and Dynamic Timing Monitoring for Cluster Heads

et al. 2020

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Internet of things (IoT) monitoring systems have been extensively applied in smart homes, underwater monitoring, volcano monitoring, and health monitoring. In IoT applications, a wireless sensor network (WSN) is deployed to collect data. Hierarchical routing protocols that effectively maintain the energy consumed by sensor nodes (SNs) are usually employed in WSNs. Cluster heads (CHs) are important in this type of protocol. An effective fault tolerance mechanism for CHs in this system can guarantee reliable data acquisition. In this paper, a fault tolerance mechanism that combines CH static backup and dynamic timing monitoring (SBDTM) is proposed, a CH reliability model based on the Markov model is developed, and the minimum number of CHs necessary to satisfy the given reliability requirement is obtained. The data structures and fault-tolerant operations are described, and the energy consumption and the latency of the recovery of the SBDTM mechanism are quantitatively analysed. Simulations were carried out to compare the total network energy consumption, number of dead nodes, throughput, and packet loss rate of the proposed model with those of other methods presented in the literature. The simulation results show that the proposed SBDTM fault tolerance mechanism is superior to current models. This study presents important theoretical and application-based knowledge that can guarantee reliable data acquisition for IoT-based monitoring systems. INDEX TERMS Cluster head failure, fault tolerance, Internet of Things, Markov model, reliability.

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“…After reviewing the existing mechanism of clustering optimization, the following are the open‐end research problems identified: Adoption of iterative approach: Majority of the existing mechanism have used iterative mechanism towards performing clustering operation. Multiple rounds of similar operation are carried out in recursive order for finding the best cluster head is witnessed.…”

Section: Research Problemmentioning

confidence: 99%

Interstellar‐based topology control scheme for optimal clustering performance in WSN

Jyothi

Usha

2020

Int J Communication

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Summary Clustering is one of the essential operations in wireless sensor network (WSN) to ensure organized data aggregation followed by energy efficiency. However, obtaining optimal clustering performance is yet an unsolved problem in WSN. Review of existing approaches towards cluster optimization shows that effective balance between energy efficiency and topology control is still missing. Therefore, the proposed system presents a unique topology control mechanism using a novel concept of interstellar orientation toward optimizing the clustering performance in WSN. Adopting an analytical research methodology, the proposed system introduces two interstellar‐based topology control system, which targets the maximum saving of resource consumption of the cluster head. The simulated outcome of the study shows that the proposed topology control system offers significant energy conservation performance in comparison to the existing hierarchical clustering scheme in WSN.

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Particle Swarm Optimization-Based Unequal and Fault Tolerant Clustering Protocol for Wireless Sensor Networks

Cited by 124 publications

References 17 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Fault Tolerance Mechanism Combining Static Backup and Dynamic Timing Monitoring for Cluster Heads

Interstellar‐based topology control scheme for optimal clustering performance in WSN

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