Abstract
The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework’s raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent.
Highlights
Internet of Things (IoT) refers to a network of all physical objects interacting and sharing information via machine-to-machine (M2M) communications [1,2]
The Calibrated Random Forest (CRF) was compared to other Machine Learning (ML) models including uncalibrated Random Forest algorithms (RF), Logistic regression (LR) and RF calibrated with sigmoid scaling, and the results are presented in 78 percent with a log loss of 0.76 and a Brier score of 0.30
Our detailed review of related research has shown that IoT systems can be used for reliable decision making despite the existence of several sources of uncertainty
Summary
Internet of Things (IoT) refers to a network of all physical objects (things) interacting and sharing information via machine-to-machine (M2M) communications [1,2]. In an IoT setup, a large number of raw data are collected from different types of sensors and are transmitted for further processing through long- and short-range communication technologies [2,4]. The existence of several sources of uncertainty is a key challenge that has significant impacts on decision making in IoT systems [5]. The existence of uncertainty may lead decision makers to take inappropriate actions that might have significant impacts, especially in cases where IoT systems are associated with high-risk decisions [7]. The sources of uncertainty in IoT-based decision-making include uncertainties associated with data acquisition, data processing, data analysis and incomplete coverage of a specific domain [8]
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