My IoT Puzzle: Debugging IF-THEN Rules Through the Jigsaw Metaphor

End users can nowadays define applications in the format of IF-THEN rules to personalize their IoT devices and online services. Along with the possibility to compose such applications, however, comes the need to debug them, e.g., to avoid unpredictable and dangerous behaviors. In this context, different questions are still unexplored: which visual languages are more appropriate for debugging IF-THEN rules? Which information do end users need to understand, identify, and correct errors? To answer these questions, we first conducted a literature analysis by reviewing previous works on end-user debugging, with the aim of extracting design guidelines. Then, we developed My IoT Puzzle, a tool to compose and debug IF-THEN rules based on the Jigsaw metaphor. My IoT Puzzle interactively assists users in the debugging process with different real-time feedback, and it allows the resolution of conflicts by providing textual and graphical explanations. An exploratory study with 6 participants preliminary confirms the effectiveness of our approach, showing that the usage of the Jigsaw metaphor, along with real-time feedback and explanations, helps users understand and fix conflicts among IF-THEN rules

Specification of Complex Logical Expressions for Task Automation: An EUD Approach

The growing availability of smart objects is stimulating researchers in investigating the IoT phenomenon from different perspectives. In the HCI area, and in particular from the EUD perspective, one prominent goal is to enable nontechnical users to be directly involved in configuring smart object behaviour. With this respect, this paper discusses three visual composition techniques to specify logical expressions in Event-Condition-Action rules used for synchronizing the behavior of smart objects

A statistical-based approach for fault detection in a three tank system

Fault detection in stochastic dynamical systems is usually carried out by the generation of residuals directly reflecting the magnitude of the faults. For this purpose, faults indicator is used to evaluate possible deviations from the normal operating conditions and the measurements of the system. This evaluation is often very difficult to implement in the multi-faults case. This article aims to demonstrate the efficiency of the coefficient of variation (CV) in detecting single and multi-faults in a multivariable laboratory three tank system DTS-200. The performance of the detection algorithm is based on the computation of the confidence intervals (CIs) which provide an estimate of the amount of error in the considered data and characterise the precision of the computed statistical estimates. The data variability may result from random measurement errors caused by the system parameters uncertainties, internal and external noises, and measuring instrument, which are not usually accurate. The CIs make the CV less sensitive to parameter uncertainties and to measure noises. The robustness and accuracy of the CV are shown in a healthy mode and various faulty situations in an entirely uncertain environmen