An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks

Liazid, Hidaya; Lehsaini, Mohamed; Liazid, Abdelkrim

doi:10.1007/s11276-019-01950-7

Cited by 18 publications

(28 citation statements)

References 21 publications

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“…When 1 > i ≥ j , X ij is used to describe the total amount of data that node i receives node j in the wireless sensor network. 22,23…”

Section: Methodsmentioning

confidence: 99%

“…i ø j, X ij is used to describe the total amount of data that node i receives node j in the wireless sensor network. 22,23 The data that other nodes forward in the network and the data that the node itself perceives in the network are the same as the total amount of data forwarded by the node and can be expressed by the following formula…”

Section: Network Model Constructionmentioning

confidence: 99%

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High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

Zhang

Qiu

et al. 2020

International Journal of Distributed Sensor Networks

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In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

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“…When 1 > i ≥ j , X ij is used to describe the total amount of data that node i receives node j in the wireless sensor network. 22,23…”

Section: Methodsmentioning

confidence: 99%

Section: Network Model Constructionmentioning

confidence: 99%

High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

Zhang

Qiu

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

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“…Other works such as [17][18][19][20] are focused on data prediction approach that aims to build a predictive model to send to the sink instead of the whole collected data. The authors of [17] propose a hybrid prediction model based on two algorithms; a stagewise algorithm which is applied at sensor level and uses a set of data points to build a predictive model to reduce sensor data transmission.…”

Section: Related Workmentioning

confidence: 99%

“…The other algorithm is used by the sink node and aims to reconstruct the raw data generated by the sensors. In [18], the authors propose an adapted version of dual prediction scheme (DPS) algorithm. The newest version uses a collection of models for data prediction during the past sequences of DPS algorithm, without updating classically the history data table.…”

Section: Related Workmentioning

confidence: 99%

All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications

Ibrahim

Harb

Mansour

et al. 2021

Peer-to-Peer Netw. Appl.

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Big data collection and storage have become one of the most obvious challenge in this era. Indeed, much of that data is collected thanky to a huge number of connected devices in sensing-based IoT applications. Thus, in order to deal with data growth in such applications, researchers have focused on data reduction approach as an efficient solution for minimizing the amount of data collection and saving the limited sensor energy in such networks. Mainly, data reduction approach relies on various kinds of data processing techniques such that aggregation, compression, prediction, clustering, sensing frequency adaptation and spatial-temporal correlation. However, each of those techniques has its own advantages and disadvantages regarding sensor energy saving, data reduction ratio, data accuracy, complexity, etc. In this paper, we propose a hybrid data collection and energy saving mechanism, called All-in-One, for sensing-based IoT applications. The proposed mechanism takes advantages from existing data reduction techniques while optimizing various performance metrics. All-in-One relies on the cluster network architecture and works on three main phases: on-period, in-period and in-node. The first phase, e.g. on-period, allows each sensor node to search the similarity among its periodic collected data then to reduce its data transmission to the Cluster-Head (CH) by applying either data aggregation, compression or prediction technique. The second phase, e.g. in-period, allows each sensor to study the variation of the monitored condition then to reduce its data collection according to two techniques, on-off transmission or adapting sensing frequency. The last phase, e.g. in-node, is applied at the CH level and aims to remove the re-

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“…Another technique to address these challenges differently in reducing radio transmission as it ingests higher energy after sensing data. Dual data prediction 21,24‐32 emerges to be an influential approach in this direction. An identical data prediction model is used in dual data prediction between the SNs or CHs and the BS.…”

Section: Introductionmentioning

confidence: 99%

A lightweight data transmission reduction method based on a dual prediction technique for sensor networks

Jain

Kumar

2021

Trans Emerging Tel Tech

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An essential design concern in a resource‐constraint sensor network is optimizing data transmission for each sensor node (SN) to prolong the network lifetime. Many research works cited that the dual prediction technique remains the most efficient technique for data reduction. A large amount of redundant data is usually transmitted across the network, leading to collisions, loss of data, and energy dissipation. This article proposes a data transmission reduction method (DTRM) to solve these problems, implemented on the cluster heads and operates in rounds. DTRM is lightweight in processing, has low complexity costs, and needs a limited memory footprint, but it is robust and effective. It can be combined with any form of cluster‐based data aggregation. We have incorporated the proposed DTRM with the data aggregation‐adaptive frame method (DA‐AFM), implemented on the SNs within the clusters. DA‐AFM can eliminate temporal redundancies and correlations in the sensor's time‐series readings. This helps the SN take fewer readings, which improves the efficiency of reducing data transmission and decreases the amount of energy spent during sensing. The proposed DTRM approach decreases the average transmission rates of data while maintaining data quality. This study is evaluated on real data obtained from the Intel Berkeley Lab and compared with three recent data reduction techniques focused on prediction. The results show that DTRM consumes up to 70% less energy while preserving the expected quality of data and reducing transmission.

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An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks

Cited by 18 publications

References 21 publications

High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

High-density data transmission and scheduling method in wireless sensor networks based on Wi-Fi

All-in-one: Toward hybrid data collection and energy saving mechanism in sensing-based IoT applications

A lightweight data transmission reduction method based on a dual prediction technique for sensor networks

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