Data Collection Study Based on Spatio-Temporal Correlation in Event-Driven Sensor Networks

Lu, Yongling; Jiang, Haibo; Pang, Zhiyong; Wang, Zheng; Xu, Jiangtao; Liu, Yang; Gao, Chao; Hu, Caihong; Sun, Haiquan

doi:10.1109/access.2019.2957450

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…The results show that the proposed MWR algorithm effectively reduces the total data collection delay in different network scenarios and at the same time reduces the network energy consumption. Lu et al [ 16 ] proposed a data collection mechanism for space-time correlation perception for event-driven sensor networks. On the Sink node, the sensed data is gathered based on the temporal and spatial correlation, and the particle swarm optimization algorithm is used to obtain the best data collection path.…”

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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“…Data aggregation needs to estimate link cost authors design a ZigBee WSNs to improve route selection in multihop environment. U-RR procedure is implemented in this paper and simulation results are showing that cost of link is reduced and allowed different nodes to select better route for achievement of require QOS.V3 [9], [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…But the limitation of this work was that this only focused for industrial wireless sensor network, furthermore work can be done on internet of things technologies. Lu et al [10] proposed an autoregressive integrated moving average ( ARIMA) model to protect the data aggregatethe using Hadoop framework for Wireless sensor network to preserve the data privacy, Time series analmethodsethod are used to protect the data by adversaries as well as Author scheme provide the predication of data storaa ge in sensor node, data updating. Author experimental result shows that Author propshows method show high accuracy, communication and computation cost as well as achieve the best srity.…”

Section: Literature Reviewmentioning

confidence: 99%

Techniques For Reducing Energy And Delay For Data Aggregation In Wireless Sensor Networks

Akhtar

Feng

2022

EAI Endorsed Trans Creat Tech

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WSN have many applications in different fields like medical, military, health and agriculture, etc., due to its data sensing and gathering abilities to Base station. The main issue in wireless sensor network is energy efficiency under consideration of QOS parameter like delay and security. Many of techniques have been proposed in literature but few work on energy efficient network with QOS. Due to lack of prior research in this area of study, this research will optimize the existing result in manner of giving efficient energy mechanisms and will also provide QOS as well as reduction of delay. It is very important to calculate energy efficiency and data transmission rate in wireless sensor networks because it is widely used in every field of life, especially when we are talking about medical, military and navigation system. After identifying main issues, this research will focus on energy efficiency and delay reduction. But other factors like security and average transmission time is not fully focused.

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“…Low latency: Strict real-time applications (e.g., fire detection, earthquake, and intrusion detection) require a very low data transmission delay [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, full coverage is not necessary in some WSN applications [22]. • Low latency: Strict real-time applications (e.g., fire detection, earthquake, and intrusion detection) require a very low data transmission delay [23].…”

mentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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Data Collection Study Based on Spatio-Temporal Correlation in Event-Driven Sensor Networks

Cited by 4 publications

References 25 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

Techniques For Reducing Energy And Delay For Data Aggregation In Wireless Sensor Networks

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

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