Data collection model for energy-efficient wireless sensor networks

Gautam, Nidhi; Sofat, Sanjeev; Vig, Renu

doi:10.1007/s12243-015-0471-x

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The time complexity of the algorithm in this paper is Oðn 2 Þ. Mobile nodes are used to repair the edge nodes that cover the loopholes in turn and can quickly converge [38,39]. Simulation experiments prove that for the overall wireless sensor network, the coverage loopholes existing in it can be basically repaired, and the monitoring information requirements of the coverage target area can be met [40,41].…”

Section: Using Virtual Force To Move Redundant Nodes Tomentioning

confidence: 99%

Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

Hao

Dang

et al. 2020

Journal of Sensors

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Target sensing and information monitoring using wireless sensor networks have become an important research field. Based on two-dimensional plane research, information monitoring, and transmission for three-dimensional curved target events, due to the uneven deployment of nodes and failures in sensor networks, there are a lot of coverage loopholes in the network. In this paper, a method of detecting and repairing loopholes in monitoring the coverage of three-dimensional surface targets with hybrid nodes is proposed. In the target monitoring area where the hybrid nodes are randomly deployed, the three-dimensional surface cube is meshed, and the coverage loopholes are gradually detected according to the method of computational geometry, and then, the redundant mobile nodes around the coverage loopholes are selected. According to the calculated distance to cover the moving direction and distance of the loophole, the virtual force is used to adjust the mobile nodes to repair the coverage loopholes. Simulation results show that compared with other algorithms, this algorithm has a higher utilization rate of mobile nodes, uses fewer nodes to complete coverage, reduces network coverage costs, meets the overall network coverage requirements, and has lower mobile energy consumption and longer network life. The actual scene further verifies the good connectivity and high coverage of the whole network.

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Section: Using Virtual Force To Move Redundant Nodes Tomentioning

confidence: 99%

Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

Hao

Dang

et al. 2020

Journal of Sensors

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“…Mobile cluster-head data collection model was presented in [17] for improving the packet delivery and minimizing the end to end delay. But this model failed to balance the non-uniform flow of data.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

J.Srimathi¹,

Mayil²

2019

IJCNC

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The energy is a major resource to obtain efficient data gathering and increasing network lifetime (NL). The various techniques are introduced for data aggregation, but energy optimized sensor node (SN) selection was not carried out to further enhance NL. In order to improve the energy efficient data gathering in WSN, a Fuzzy Gene Energy Optimized Reweight Boosting Classification (FGEORBC) Technique is introduced with lesser time consumption. In FGEORBC technique, the Residual Energy (RE) of SN in the WSN is computed. After calculating SN residual energy, fuzzy logic is applied to determine higher energy nodes and lower energy nodes using threshold value. For finding the optimal higher energy SNs, then Ranked Gaussian gene optimization technique is applied. If the node satisfies the fitness criterion, then the node is selected as an optimal higher energy SN. Otherwise, the rank selection, ring crossover, and Gaussian mutation process are carried out until the condition gets satisfied. After that, the sink node collects the data packets (DP) from the optimal higher energy SNs. In the sink node, Reweight Boosting Classification is carried out to classify the sensed DP and it sends to the base station (BS) for further processing. Simulation of FGEORBC technique is carried out using different parameters such as energy consumption (EC), NL, data gathering time and classification accuracy (CA) with respect to a number of SN and a number of DP. The results observed that FGEORBC technique improves the data gathering and NL with minimum time as well as EC than the state-of-the-art methods.

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“…One important parameter in mobility-based WSNs is the velocity of mobile sinks. Recently, this parameter has been adjusted to reduce end-to-end packet delays [ 18 ] and to increase the packet delivery ratio. Adjusting the velocity of MSs in different layers of the network can solve the energy hole problem, which was previously not considered.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs

Gharaei

Bakar

Hashim

et al. 2017

Sensors

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Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network’s lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network’s lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

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Data collection model for energy-efficient wireless sensor networks

Cited by 6 publications

References 29 publications

Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

Method for Patching Three-Dimensional Surface Coverage Loopholes of Hybrid Nodes in Wireless Sensor Networks

Fuzzy Gene Optimized Reweight Boosting Classification for Energy Efficient Data Gathering in WSN

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs

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