Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Wang, Jin; Cao, Yiquan; Li, Bin; Kim, Hye-Jin; Lee, Sungyoung

doi:10.1016/j.future.2016.08.004

Cited by 271 publications

(156 citation statements)

References 18 publications

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“…However this process of developing grid consumes more energy of SNs. In [5], here author focused on energy efficient routing and clustering based on PSO algorithm. Here authors have also presented a technique which extends the network lifetime by eliminating the traffic load of the gateways whose remaining energy is beyond a particular threshold value; however authors have considered only failure of the gateways due to complete energy depletion.…”

Section: Related Workmentioning

confidence: 99%

“…Here authors have also presented a technique which extends the network lifetime by eliminating the traffic load of the gateways whose remaining energy is beyond a particular threshold value; however authors have considered only failure of the gateways due to complete energy depletion. The EPMS algorithm [5] performs the virtual clustering by using PSO algorithm to improve the network performance.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore we have motivated towards research in sink mobility which has emerged in WSNs to properly handle the hot spot problem and to decrease the energy communication overheads [4]. Traditionally, mobile sink [5,6] needs to visit every cluster head [2] to collect the data, leading to longer mobile sink traversal path which in turns creates data delivery latency [7,8] and higher energy consumption. For this reason, we proposed RLBCA and ODMST algorithm upon reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

“…(iii) Comparison of these above-mentioned algorithms with existing algorithm like CLIQUE [12], TTDD [13,14], EPMS [5], DBRkM [3], RLLO [15], and RL-CRC [16] to better prove our simulation results.…”

Section: Introductionmentioning

confidence: 99%

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Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Soni

Shrivastava

2018

Wireless Communications and Mobile Computing

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Generally, wireless sensor network is a group of sensor nodes which is used to continuously monitor and record the various physical, environmental, and critical real time application data. Data traffic received by sink in WSN decreases the energy of nearby sensor nodes as compared to other sensor nodes. This problem is known as hot spot problem in wireless sensor network. In this research study, two novel algorithms are proposed based upon reinforcement learning to solve hot spot problem in wireless sensor network. The first proposed algorithm RLBCA, created cluster heads to reduce the energy consumption and save about 40% of battery power. In the second proposed algorithm ODMST, mobile sink is used to collect the data from cluster heads as per the demand/request generated from cluster heads. Here mobile sink is used to keep record of incoming request from cluster heads in a routing table and visits accordingly. These algorithms did not create the extra overhead on mobile sink and save the energy as well. Finally, the proposed algorithms are compared with existing algorithms like CLIQUE, TTDD, DBRkM, EPMS, RLLO, and RL-CRC to better prove this research study.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Soni

Shrivastava

2018

Wireless Communications and Mobile Computing

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“…Determining the optimal routes would lead to reduce transportation costs and improve service quality as one of the vital operational decisions in urban services organization [2][3][4]. Transportation imposes some irreparable impacts on the environment.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

et al. 2018

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Greenhouse gases (GHG) are the main reason for the global warming during the past decades. On the other hand, establishing a well-structured transportation system will yield to create least cost-pollution. This paper addresses a novel model for the multi-trip Green Capacitated Arc Routing Problem (G-CARP) with the aim of minimizing total cost including the cost of generation and emission of greenhouse gases, the cost of vehicle usage and routing cost. The cost of generation and emission of greenhouse gases is based on the calculation of the amount of carbon dioxide emitted from vehicles, which depends on such factors as the vehicle speed, weather conditions, load on the vehicle and traveled distance. The main applications of this problem are in municipalities for urban waste collection, road surface marking and so forth. Due to NP-hardness of the problem, a Hybrid Genetic Algorithm (HGA) is developed, wherein a heuristic and simulated annealing algorithm are applied to generate initial solutions and a Genetic Algorithm (GA) is then used to generate the best possible solution. The obtained numerical results indicate that the proposed algorithm could present desirable performance within a suitable computational run time. Finally, a sensitivity analysis is implemented on the maximum available time of the vehicles in order to determine the optimal policy.

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Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications

Devika

Ramesh

Karegowda

2020

Swarm Intelligence Optimization

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Particle swarm optimization based clustering algorithm with mobile sink for WSNs

Cited by 271 publications

References 18 publications

Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

Novel Learning Algorithms for Efficient Mobile Sink Data Collection Using Reinforcement Learning in Wireless Sensor Network

A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services

Swarm Intelligence–Based Energy‐Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications

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