Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

Harb, Hassan; Makhoul, Abdallah; Jaber, Ali; Tawbi, Samar

doi:10.1504/ijsnet.2019.097547

Cited by 14 publications

(9 citation statements)

References 28 publications

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“…Although, this model is not scalable and data latency is not considered which results in delay. Harb et al 88 in 2019 proposed energy-aware spatial-temporal scheduling (ESTS) and reliable-ESTS (RESTS) to resolve the issues of the density distribution of dynamic objects which introduced high data correlation among SNs. ESTS exploits the spatiotemporal correlation between neighboring nodes by employing the Euclidean metrics.…”

Section: Data Transmission Reduction Techniques At the Ch Stagementioning

confidence: 99%

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

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Wireless sensor networks (WSNs) have now become a substantial part of our life as it provides an incredible setup for monitoring environmental conditions such as glaciers melting, earthquakes, climate change, volcanic eruption, agriculture, and other natural calamities. One of the most important challenges in data gathering and examining accurate data is "energy consumption." However, continuously changing environmental conditions, mobility in devices and extreme resource usage, such as battery capacity in WSNs collectively contribute to a high possibility of redundant data transmission thus making it tremendously hard to achieve a satisfactory network lifetime. Additionally, these environmental monitoring applications periodically senses and aggregate data which usually exhibits high temporal and spatial redundancies. As a result, exploiting these redundancies is essential for both resource utilization, as well as accurate data transmission. Since a huge amount of energy is exhausted in transmitting the redundant data, which has become a bottleneck in scaling such applications. In this context, this article provides a thorough examination of existing energy-efficient redundant data transmission reduction techniques, as well as their strengths and drawbacks. The idea of redundant data transmission reduction concept is divided into three stages: the sensor nodes (SN) at the first stage, the cluster heads (CHs) at the second stage, and the base station (BS) at the third stage. Furthermore, this survey focuses on the current issues and challenges, as well as the future directions in reducing redundant data transmission for future investigation.

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Section: Data Transmission Reduction Techniques At the Ch Stagementioning

confidence: 99%

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Jain

Kumar

Singh

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Event-driven model applications are used for emergency and disaster recoverybased applications such as health emergencies, forest fires, earthquakes, monitoring of air quality, animal movement, rain, lava eruption, military applications [96], and volcanic eruption [97][98][99]. The main feature of the event-driven model is that the collection of data is not formed on a continuous, regular basis while an event is occurring.…”

Section: Event-driven Modelmentioning

confidence: 99%

Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

et al. 2021

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Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.

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“…In order to schedule the sampling intervals of sensor nodes and reduce energy transmission, some approaches rely on the spatial-temporal correlation between sensor nodes deployed in the monitoring area [28]- [32]. The Authors in [28] proposed an Efficient Data Collection Aware of spatial-temporal Correlation (EAST).…”

Section: Related Workmentioning

confidence: 99%

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

et al. 2019

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In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the "nonsampled" parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle nonstationary data more effectively.

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Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

Cited by 14 publications

References 28 publications

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Data transmission reduction techniques for improving network lifetime in wireless sensor networks: An up‐to‐date survey from 2017 to 2022

Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

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