Energy Efficient Data Collection Algorithm for Mobile Wireless Sensor Network

Saranya, V.; Shankar, S.; Kanagachidambaresan, G. R.

doi:10.1007/s11277-018-6109-3

Cited by 25 publications

(11 citation statements)

References 24 publications

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“…The results show that the proposed algorithm has certain applicability in real scenes. Saranya et al [ 18 ] proposed a novel energy-saving data collection scheme. The data collection of the mobile Sink is completed by a dynamic polling mechanism based on the arrival rate of cluster head nodes.…”

Section: Related Workmentioning

confidence: 99%

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

Yue

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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For the sensing layer of the Internet of Things, the mobile wireless sensor network has problems such as limited energy of the sensor nodes, unbalanced energy consumption, unreliability, and long transmission delay in the data collection process. It is proved by mathematical derivation and theory that this is a typical multiobjective optimization problem. In this paper, the optimization goal is to minimize the energy consumption and improve the reliability under time-delay constraints and propose a path optimization mechanism to optimize the mobile Sink of mobile wireless sensor networks based on the improved dragonfly optimization algorithm. The algorithm takes full advantage of the abundant storage space, sufficient energy, and strong computing power of the mobile Sink to ensure network connectivity and improve network communication efficiency. Through simulation comparison and analysis, compared with random movement method, artificial bee colony algorithm, and basic dragonfly optimization algorithm, the energy consumption of the network is reduced, the lifespan of the network is increased, and the connectivity and transmission delay of the network are improved. The proposed algorithm balances the energy consumption of the sensors nodes to meet the network service quality and improve the reliability of the network.

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Section: Related Workmentioning

confidence: 99%

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

Yue

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…However optimal path is not considered. V.Saranya et al, [23] has proposed a protocol in which, they have improved the throughput, network life time and waiting time. In the proposed work the authors have adopted clustering approach in which members of the cluster relays data to cluster head by N threshold method.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Data Gathering Algorithm For Wireless Sensor Networks Using Artificial Intelligence

Budyal

2021

Preprint

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The most challenging task in wireless sensor network is energy efficiency, as energy is the major constraint in the wireless sensor network to improve the life time of the network. Hence developing algorithms to improve network life time is the major task. In wireless sensor network most of the energy is wasted while gathering the data, hence an efficient algorithm which conserves energy has to be designed. Thus our proposed work A Novel Data Gathering Algorithm for Wireless Sensor Networks using Artificial Intelligence (NDGAI) uses mobile element and deals with the conservation of energy while gathering the data. Appropriate clustering, cluster leader selection and proper path determination of mobile element helps to conserve energy and improve the over all network life time. In our proposed work initially the clusters are forged by using Amended Expectation Maximization(AEM) algorithm, which is the maximum likelihood estimate. It is used along with Gap statistic method to find the optimal number of clusters. AEM algorithm helps in obtaining the centres of the cluster with maximum number of nodes near the cluster centres. For each cluster, Cluster Leader (CL) is selected by using Fuzzy Logic. Fuzzy logic selects the node which is near to the cluster centre by using parameters such as Closeness of node to the Cluster Centroid, direction of node towards base station, number of Neighbouring Nodes. After the CL’s are determined, to reduce the path length virtual points(VP) are selected so that mobile element reaches this virtual point and collects the data. These VP’s are selected only when the CL has data in it. The mobile elements can reach these virtual points intelligently by using optimal path,that is obtained by using hybrid of Particle Swarm Optimization and Artificial Bee Colony algorithm. Thus the mobile element travels in the optimal path and gathers the data from the entire network intelligently and efficiently with less amount of energy. With this approach the performance and life time of the network is improved while gathering the data. The simulation results are compared with Scalable Grid-Based Data Gathering Algorithm for Environmental Monitoring Wireless Sensor Networks (SGBDN) and proved that the proposed method is better than SGBDN .

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“…This ensures that the energy harvested can be used directly or can be stored in buffer for future use. Though there are various ambient sources like solar, wind, vibration and RF are available for harvesting, the solar energy is widely preferred due to its abundance [5]- [8]. The type of application decides the source of energy to be harvested.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Based Energy Management (Rl-EM) Algorithm For Green Wireless Sensor

Mahima¹,

Chitra²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Green network is basically capable of producing its own energy through renewable energy resources. The sensor nodes serving to collect the physical parameters are equipped with small scale solar panels and tiny wind mills to harvest their own energy. The limitations on battery and availability of renewable energy resource create challenges in continuous monitoring without missing any events. The energy harvesting sensor network requires dynamic adaptation to the time varying behavior of the environment. The reinforcement learning based energy management algorithm named RL-EM is proposed which observes and learns from the environment. The voltage level of the sensor node is considered for designing the algorithm. The RL-EM outperforms existing algorithms with 40% reduced sleep nodes and 60% reduced energy overflow with LEACH protocol. The proposed RL-EM algorithm outperforms the LEACH, SEP-M and ACO algorithms in terms of throughput, sleep nodes, energy overflow, and load distributions.

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Energy Efficient Data Collection Algorithm for Mobile Wireless Sensor Network

Cited by 25 publications

References 24 publications

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm

A Novel Data Gathering Algorithm For Wireless Sensor Networks Using Artificial Intelligence

Reinforcement Learning Based Energy Management (Rl-EM) Algorithm For Green Wireless Sensor

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