POSE.3C: Prediction-Based Opportunistic Sensing Using Distributed Classification, Clustering, and Control in Heterogeneous Sensor Networks

2019 ◽  
Vol 6 (4) ◽  
pp. 1438-1450 ◽  
Author(s):  
James Zachary Hare ◽  
Shalabh Gupta ◽  
Thomas A. Wettergren
Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1827
Author(s):  
Piotr Cofta ◽  
Kostas Karatzas ◽  
Cezary Orłowski

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.


2012 ◽  
Vol 8 (3) ◽  
pp. 1-34 ◽  
Author(s):  
Novella Bartolini ◽  
Tiziana Calamoneri ◽  
Tom La Porta ◽  
Chiara Petrioli ◽  
Simone Silvestri

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