ArticleSensors (Basel, Switzerland)2021
A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Industrial Soft Sensor Optimized by Improved PSO: A Deep Representation-Learning Approach.Sensors (Basel, Switzerland) · 2022Article
- The Desirable Systemic Uncertainty in Complex IoT Sensor Networks-General Anticipatory Foresight Perspective.Sensors (Basel, Switzerland) · 2022Article
- Eventual Convergence of the Reputation-Based Algorithm in IoT Sensor Networks.Sensors (Basel, Switzerland) · 2021Article
- Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.