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Time series anomaly detection is widely used in IoT and cyber-physical systems, yet its evaluation remains challenging due to diverse application objectives and heterogeneous metric assumptions. This study introduces a problem-oriented…
IoT devices are decentralized and deployed in un-stable environments, which causes them to be prone to various kinds of faults, such as device failure and network disruption. Yet, current IoT platforms require programmers to handle faults…
This paper presents a comprehensive investigation into developing a fault detection and classification system for real-world IIoT applications. The study addresses challenges in data collection, annotation, algorithm development, and…
The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their…
Internet of Things (IoT) systems allow software to directly interact with the physical world. Recent IoT failures can be attributed to recurring software design flaws, suggesting IoT software engineers may not be learning from past…
The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large…
The compliance of IoT platforms to quality is paramount to achieve users satisfaction. Currently, we do not have a comprehensive set of guidelines to appraise and select the most suitable IoT platform architectures that meet relevant…
The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification model depends highly on…
The widespread use of Internet-of-Things (IoT) across different application domains leads to an increased concern regarding their dependability, especially as the number of potentially mission-critical systems becomes considerable.…
Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT…
Both the volume and the collection velocity of time series generated by monitoring sensors are increasing in the Internet of Things (IoT). Data management and analysis requires high quality and applicability of the IoT data. However, errors…
Context: IoT systems, networks of connected devices powered by software, require studying software quality for maintenance. Despite extensive studies on non-IoT software quality, research on IoT software quality is lacking. It is uncertain…
Internet of Things (IoT) sensors are ubiquitous technologies deployed across smart cities, industrial sites, and healthcare systems. They continuously generate time series data that enable advanced analytics and automation in industries.…
To ensure reliability and service availability, next-generation networks are expected to rely on automated anomaly detection systems powered by advanced machine learning methods with the capability of handling multi-dimensional data. Such…
As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive…
Internet of Things (IoT) sensor data or readings evince variations in timestamp range, sampling frequency, geographical location, unit of measurement, etc. Such presented sequence data heterogeneity makes it difficult for traditional time…
Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process…
Internet of Things (IoT) systems are vulnerable to data collection errors and these errors can significantly degrade the quality of collected data, impact data analysis and lead to inaccurate or distorted results. This article emphasizes…
The rapid expansion of the industrial Internet of things (IIoT) has introduced new challenges in securing critical infrastructures against sophisticated cyberthreats. This study presents the development and evaluation of an advanced…
Quality and reliability metrics play an important role in the evaluation of the state of a system during the development and testing phases, and serve as tools to optimize the testing process or to define the exit or acceptance criteria of…