A Comparison of Bio-Inspired Approaches for the Cluster-Head Selection Problem in WSN

Miranda, Karen; Zapotecas-Martínez, Saúl; López-Jaimes, Antonio; García-Nájera, Abel

doi:10.1007/978-3-319-96451-5_7

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…The heuristics depend on arriving at a tradeoff between various parameters for determining the node with optimal residual energy, with better connectivity to other nodes in the network. In such applications we propose the use of bioinspired heuristics [28][29][30][31][32][33]116] and fuzzy-based methods to dynamically estimate the residual-energies of nodes and choose the best amongst them as lead node. To estimate the residual energy of nodes and their connectivity, the fitness function (in the case of bio-inspired algorithms) and fuzzy classifiers need to factor the number transmissions and receptions, idle time, and computational overhead to grade the nodes according to their residual energies.…”

Section: Few Posers and Conclusionmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

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Section: Few Posers and Conclusionmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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“…Also, buffer space is a constraint for such network nodes. Many of the previous works done in this domain focus on preserving battery resources or extending the lifetime of the network 3,4 . When the network boots up, each node is given some initial battery power.…”

Section: Introductionmentioning

confidence: 99%

Bio‐inspired Mimosa pudica algorithm for energy‐efficient wireless video sensor networks

Murugeswari

Murugan

2020

Int J Communication

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Wireless video sensor network (WVSN) has attracted much research interest due to its multi-faceted potential applications in different fields. An energyefficient protocol is fundamental for WVSN since the network has to deal with traffic-intensive multimedia data, such as images and videos. A few issues come in front for any such setting like energy inefficiency, throughput unfairness, and low network capacity. In many cases, we need to strike a balance between required parameters or aspects, but there is no alternative to ensuring appropriate energy efficiency in the process. Keeping this in mind, this paper proposes a holistic solution named energy-efficient bio-inspired Mimosa pudica (EEBIMP) algorithm. EEBIMP for WVSN can reduce the time delay and avoid unnecessary energy consumption due to clustering the network. Our proposed algorithm has been compared with one of the most recent notable solutions, like GREENNESS (Green wiReless vidEo sENsor NEtworks uSing out-of-band Signalling), PSO (Particle Swarm Optimization), and CUCKOO search algorithm. The result shows that EEBIMP achieved 95% of energy efficiency and throughput when compared with other algorithms of GREENNESS, PSO, and CUCKOO.

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“…The nature-inspired optimization techniques such as particle swarm optimization, artificial bee colony, genetic algorithm and harmony search algorithm used to select the cluster head in wireless sensor network [20]. Miranda et al compared the NSGA-II, SMS-EMOA, and MOEA/D multi-objective optimization algorithms to solve the cluster head selection problem [21]. The self-adaptive mutation factor cross-over probability-based differential evolution algorithm developed by Annepu and Rajesh to solve node localization problem in wireless sensor network [22].…”

Section: Introductionmentioning

confidence: 99%

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane

Adamuthe

Omane³

2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

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A Comparison of Bio-Inspired Approaches for the Cluster-Head Selection Problem in WSN

Cited by 6 publications

References 43 publications

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Bio‐inspired Mimosa pudica algorithm for energy‐efficient wireless video sensor networks

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

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