Sensor node data validation techniques for realtime IoT/WSN application

Alduais, Nayef Abdulwahab Mohammed; Abdullah, Jiwa; Jamil, Ansar; Audah, Lukman; Alias, Rozlan

doi:10.1109/ssd.2017.8166984

Cited by 12 publications

(8 citation statements)

References 4 publications

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“…(ii) Rule-based methods derive heuristic rules and constraints for the sensor readings often by exploiting domain or expert knowledge. Such approaches can range from adaptive thresholds of the sensor data [33] over signature-based fault detection [34] up to applying distributed state filters on the sensor data [35].…”

Section: Sensor Data Analysismentioning

confidence: 99%

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

Widhalm

Goeschka

Kästner

2021

Sensors

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In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.

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Section: Sensor Data Analysismentioning

confidence: 99%

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

Widhalm

Goeschka

Kästner

2021

Sensors

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“…One of the procedures for sensor information validation, which depends on an adaptive threshold for real-time IoT/WSN sensor node level. The proposed methodology thought about the recognition of different kinds of detected information errors and tended to a straightforward mechanism to classify between error and event [215].…”

Section: E Sensor Validitymentioning

confidence: 99%

Advancing Modern Healthcare With Nanotechnology, Nanobiosensors, and Internet of Nano Things: Taxonomies, Applications, Architecture, and Challenges

Pramanik

Solanki

Debnath³

et al. 2020

IEEE Access

103

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Healthcare sector is probably the most benefited from the applications of nanotechnology. The nanotechnology, in the forms of nanomedicine, nanoimplants, nanobiosensors along with the internet of nano things (IoNT), has the potential to bring a revolutionizing advancement in the field of medicine and healthcare services. The primary aim of this paper is to explore the clinical and medical possibilities of these different implementations of nanotechnology. This paper provides a comprehensive overview of nanotechnology, biosensors, nanobiosensors, and IoNT. Furthermore, multilevel taxonomies of nanotechnology, nanoparticles, biosensors, nanobiosensors, and nanozymes are presented. The potential medical and clinical applications of these technologies are discussed in details with several examples. This paper specifically focuses on IoNT and its role in healthcare. In addition to describing a general architecture of IoNT for healthcare, the communication architecture of the IoNT is also explained. The challenges in the successful realization of IoNT are also discussed critically, along with a special discussion on internet of bio-nano things (IoBNT) and its potential in making IoNT more compatible to human body.

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“…The Modified-Z score method [35] was used to detect outliers. Similarly, in [36], they proposed a new realtime algorithm for observational verification of sensor data at the node level, which is named Validity of the measuring sensor reading at node level (VSNL). VSNL is a sensor data verification algorithm based on an adaptive threshold.…”

Section: ) Reduce the Number Of Transmitted Incorrect Datamentioning

confidence: 99%

“…GSB network used sensor nodes to measure the metrological characteristics of the environment which are ambient temperature, surface temperature and relative humidity '' [53]; (iii) ''Lausanne Urban Canopy Experiment dataset (LUCE). LUCE measure critical environment quantities which are ambient temperature, surface temperature and relative humidity'' [54]; (iv) UTHM_LAB measure air quality which are temperature and humidity [36]. MATLAB is used to simulate the algorithms effect in the performances of IoT edge node.…”

Section: Implementation and Performance Evaluationmentioning

confidence: 99%

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RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

2019

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In the application of the Internet of Things (IoT), a sensor board depends on a battery that has a limited lifetime to function. Furthermore, the IoT sensor board with multivariate sensors influences the battery lifetime , since there are additional data transmissions that must be supported by the board causing it to drain the battery much faster than the sensor board with one sensor. The main aim of this paper is to increase the battery life of the IoT sensor node. To do so, this paper proposes an efficient real-time data collection model for multivariate sensors in IoT/WSN applications named RDCM. The general structure of RDCM is composed of two main levels: the IoT sensor board level and the fusion center level. The IoT sensor board level is implemented in real time by all the IoT sensor boards simultaneously in each cycle and fusion center level is executed by the fusion center. The IoT sensor board level includes various stages as follows: check the physical conditions of the IoT edge device (board) stage and update data strategy stage, data validation stage, and sensed data reduction stage. The average of the total percentage of energy saved by the application of RDCM to real-time data sets injected with various percentages of errors for all nodes is 98%. In summary, the RDCM has a very high performance in terms of energy consumption compared with other algorithms. This paper concludes with the limitation of the current study and some further research opportunities.

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Sensor node data validation techniques for realtime IoT/WSN application

Cited by 12 publications

References 4 publications

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications

Advancing Modern Healthcare With Nanotechnology, Nanobiosensors, and Internet of Nano Things: Taxonomies, Applications, Architecture, and Challenges

RDCM: An Efficient Real-Time Data Collection Model for IoT/WSN Edge With Multivariate Sensors

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