Reinforcement Learning based Routing Protocols in WSNs: A Survey

Arya, Anju

doi:10.22214/ijraset.2018.4584

Cited by 4 publications

(3 citation statements)

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“…The agent would try several actions, and learns from its experience the best action it has to choose in order to optimize the network performance [8,9,10,20]. Most of the RL based protocols are used in some clustering algorithms in WSN finding optimal paths and prolonging network lifetime [11].…”

Section: Optimized Hierarchical Based Routing Protocolsmentioning

confidence: 99%

“…which have limited resources like sensor nodes used in WSN. SI approaches can be classify into Particle Swarm, Ant Colony, and Bee Colony Optimizations [8,11,43].…”

Section: Optimized Hierarchical Based Routing Protocolsmentioning

confidence: 99%

“…The clustering solutions based on CI or ML consider environmental and biological behaviors and outperform most of the traditional clustering solutions in terms of scalability, reliability, fault tolerance, amount of data delivered, energy consumption, better coverage of the experimental field, and the increase of the network lifetime [7,8,9,10,11]. Meanwhile, according to the type of application, the choice of the appropriate approach is very important both on the cost of the network deployment and its lifetime.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review

Sambo

Yenké

Förster

et al. 2019

Sensors

132

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During the past few years, Wireless Sensor Networks (WSNs) have become widely used due to their large amount of applications. The use of WSNs is an imperative necessity for future revolutionary areas like ecological fields or smart cities in which more than hundreds or thousands of sensor nodes are deployed. In those large scale WSNs, hierarchical approaches improve the performance of the network and increase its lifetime. Hierarchy inside a WSN consists in cutting the whole network into sub-networks called clusters which are led by Cluster Heads. In spite of the advantages of the clustering on large WSNs, it remains a non-deterministic polynomial hard problem which is not solved efficiently by traditional clustering. The recent researches conducted on Machine Learning, Computational Intelligence, and WSNs bring out the optimized clustering algorithms for WSNs. These kinds of clustering are based on environmental behaviors and outperform the traditional clustering algorithms. However, due to the diversity of WSN applications, the choice of an appropriate paradigm for a clustering solution remains a problem. In this paper, we conduct a wide review of proposed optimized clustering solutions nowadays. In order to evaluate them, we consider 10 parameters. Based on these parameters, we propose a comparison of these optimized clustering approaches. From the analysis, we observe that centralized clustering solutions based on the Swarm Intelligence paradigm are more adapted for applications with low energy consumption, high data delivery rate, or high scalability than algorithms based on the other presented paradigms. Moreover, when an application does not need a large amount of nodes within a field, the Fuzzy Logic based solution are suitable.

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Section: Optimized Hierarchical Based Routing Protocolsmentioning

confidence: 99%

“…which have limited resources like sensor nodes used in WSN. SI approaches can be classify into Particle Swarm, Ant Colony, and Bee Colony Optimizations [8,11,43].…”

Section: Optimized Hierarchical Based Routing Protocolsmentioning

confidence: 99%