Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

Shin, Chang-Sun; Lee, Meonghun

doi:10.3390/s20185164

Cited by 18 publications

(8 citation statements)

References 41 publications

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“…The suggested method can discover the best route with the least amount of overall energy consumption and balanced energy consumption on each node. With the goal of leveraging the benefits of the ACO algorithm, the swarm-intelligence-centric routing algorithm (SICROA) by Shin & Lee (2020) is proposed for usage in WSNs. Through collision avoidance, link-quality forecasting, and maintenance methods, the study overcomes the issues of the adhoc on-demand distance vector (AODV) and improves routing performance.…”

Section: Ai Based Solutions For Routing Challenge In Wsnsmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

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Wireless sensor networks (WSNs) are becoming increasingly important, providing pervasive real-time applications that have been used to enhance smart environments in various fields such as smart cities, manufacturing, and the Internet of Things (IoT). This survey reviews and analyzes the research trends related to the utilized Artificial Intelligence (AI) methods for WSN and the potential enhancement of WSNs using these methods. We highlight the routing challenge in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods in addressing the routing challenge to meet specific objectives of WSN, during the span of 2010 to 2020. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to routing challenge in WSN. In addition, a general evaluation is provided along with a comparison of utilized AI methods in WSNs, which guides the reader in identifying the most appropriate AI methods that can be utilized for solving the routing challenge. Finally, we conclude the paper by stating the open research issues and new directions for future research.

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Section: Ai Based Solutions For Routing Challenge In Wsnsmentioning

confidence: 99%

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Osamy

Khedr

Salim

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Chu‐hang et al 156 have designed the clustering‐based routing protocol using GA. Prakash et al 157 and Jong et al 158 have presented the machine learning based classification techniques for the energy improvement of routing protocols, Abbasi et al 159 proposed to the various architectures for the Internet of Vehicles (IoV) application of WSN. Recent concept of Swarm Intelligence 160 and AI 161 are still an open challenge for the WSN systems performance improvement. The neural network based fuzzy approach is proposed for improvement in Varun et al 162 But these techniques may lead more energy consumption and inaccuracy or delay also memory management is also an issue.…”

Section: Open Issues and Challengesmentioning

confidence: 99%

Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication technologies

Gupta

Singh

2022

Int J Communication

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Wireless sensor network (WSN) is frequently being used for increasing the data collection and monitoring critical parameters. Major challenge in WSN design is achieving the better energy efficiency and latency considering the dynamic sink location. Lots of research has been done for improving the performance of WSN systems in wide range of applications. Prime goal of this paper is to evaluate the performance of WSN routing under the grid-based dynamic sink locations. Later the performance of various existing critical protocols is compared to route data over centralized server. Various applications of WSNs are tabulated along with the sensor utilization. The paper contributed in three passes. Initial contribution of this research is to identify the issues and limitations of present WSN communication technologies. The paper contributed to present a case study to discover the implications of dynamic sink routing for building protocol to extend network's lifetime. The effectiveness of several clustering-based WSN routing algorithms is first compared in this paper. After that, the protocol with the longest network life is put to the test under a grid-based dynamic sink routing scenario. The significance of clustering-based protocols for improving lifetime of the network is evaluated as case study. The clustering-based energy efficient protocol like low energy adaptive clustering hierarchy (LEACH) and its different variants, PEGASIS, SEP, DEC, their variants, and so on, are compared in this paper. Various performance metrics like network delays, throughput, latency, energy consumption and network lifetime are defined for studying routing protocols. Significance of dynamic sink locations performance is evaluated for Enhanced Distributed Energy Efficient Clustering (EDEEC) protocol. Overall paper covers and reviewed many critical design aspects for WSN networks.

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“…The paper [20] is devoted to improving the efficiency of routing in wireless sensor networks based on the use of swarm intelligence. The authors propose a swarm-intelligencecentric routing algorithm (SICROA) based on the ant algorithm, which allows you to quickly adapt to the dynamically variable network topology, avoiding various obstacles in the transmission of data.…”

Section: Related Workmentioning

confidence: 99%

Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

et al. 2021

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With the constant growth of requirements to the quality of infocommunication services, special attention is paid to the management of information transfer in wireless self-organizing networks. The clustering algorithm based on the Motley signal propagation model has been improved, resulting in cluster formation based on the criterion of shortest distance and maximum signal power value. It is shown that the use of the improved clustering algorithm compared to its classical version is more efficient for the route search process. Ant and simulated annealing algorithms are presented to perform route search in a wireless sensor network based on the value of the quality of service parameter. A comprehensive routing method based on finding the global extremum of an ordered random search with node addition/removal is proposed by using the presented ant and simulated annealing algorithms. It is shown that the integration of the proposed clustering and routing solutions can reduce the route search duration up to two times.

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Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks

Cited by 18 publications

References 41 publications

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

A review on recent studies utilizing artificial intelligence methods for solving routing challenges in wireless sensor networks

Survey on energy efficient dynamic sink optimum routing for wireless sensor network and communication technologies

Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

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