Emerging Internet of Things (IoT) platforms provide sophisticated capabilities to automate IoT services by enabling occupants to create trigger-action rules. Multiple trigger-action rules can physically interact with each other via shared environment channels, such as temperature, humidity, and illumination. We refer to inter-rule interactions via shared environment channels as a physical inter-rule vulnerability. Such vulnerability can be exploited by attackers to launch attacks against IoT systems. We propose a new framework to proactively discover possible physical inter-rule interactions from user requirement specifications (i.e., descriptions) using a deep learning approach. Specifically, we utilize the Transformer model to generate trigger-action rules from their associated descriptions. We discover two types of physical inter-rule vulnerabilities and determine associated environment channels using natural language processing (NLP) tools. Given the extracted trigger-action rules and associated environment channels, an approach is proposed to identify hidden physical inter-rule vulnerabilities among them. Our experiment on 27983 IFTTT style rules shows that the Transformer can successfully extract trigger-action rules from descriptions with 95.22% accuracy. We also validate the effectiveness of our approach on 60 SmartThings official IoT apps and discover 99 possible physical inter-rule vulnerabilities.

As we start 2023, the IoT Analytics team has again evaluated the past year's main IoT developments in the global "Internet of Things" arena. This article highlights some general observations and our top 10 IoT stories from 2022, a year characterized by a skyrocketing inflation rate, ongoing supply disruptions, and a looming recession. The 4.4% global growth forecast for the year (from January 2022) was almost certainly not reached, and the outlook for 2023 has been lowered to a meager 2.7% (as of Oct 2022). The Nasdaq Composite, one of the key indices for technology companies, fell 33.1% in 2022. Against this backdrop, IoT 2022 markets held up somewhat steadily, with the number of connected IoT devices growing to approximately 14.4 billion (exact update coming in a few weeks) with roughly $202 billion in IoT enterprise spending (IoT Analytics will publish the 2022 IoT spending actuals shortly). The public relevance of the term "IoT," which had been on the decline since October 2018, climbed back up by more than 30% to reach its all-time high levels in Q1 2022. Throughout 2022, we monitored significant developments regarding IoT technology. In the aftermath of the global pandemic and war in Ukraine, reports of congested ports, suppliers halting production, or critical cargo going missing became normal news in 2022.

The Internet of things (IoT) is taking the world by storm, thanks to the proliferation of sensors and actuators embedded in everyday things, coupled with the wide availability of high-speed Internet50 and evolution of the 5th-generation (5G) networks.34 IoT devices are increasingly supplying information about the physical environment (for example, infrastructure, assets, homes, and cars). The advent of IoT is enabling not only the connection and integration of devices that monitor physical world phenomena (for example, temperature, pollution, energy consumption, human activities, and movement), but also data-driven and AI-augmented intelligence. At all levels, synergies from advances in IoT, data analytics, and artificial intelligence (AI) are firmly recognized as strategic priorities for digital transformation.10,41,50 IoT poses two key challenges:36 Communication with things and management of things.41 The service paradigm is a key mechanism to overcome these challenges by transforming IoT devices into IoT services, where they will be treated as first-class objects through the prism of services.9 In a nutshell, services are at a higher level of abstraction than data. Services descriptions consist of two parts: functional and non-functional, such as, Quality of Service (QoS) attributes.27 Services often transform data into an actionable knowledge or achieve physical state changes in the operating context.9 As a result, the service paradigm is the perfect basis for understanding the transformation of data into actionable knowledge, that is, making it useful. Despite the increasing uptake of IoT services, most organizations have not yet mastered the requisite knowledge, skills, or understanding to craft a successful IoT strategy.

We propose a novel framework that detects conflicts in IoT-based smart homes. Conflicts may arise during interactions between the resident and IoT services in smart homes. We propose a generic knowledge graph to represent the relations between IoT services and environment entities. We also profile a generic knowledge graph to a specific smart home setting based on the context information. We propose a conflict taxonomy to capture different types of conflicts in a single resident smart home setting. A conflict detection algorithm is proposed to identify potential conflicts using the profiled knowledge graph. We conduct a set of experiments on real datasets and synthesized datasets to validate the effectiveness and efficiency of our proposed approach.

In this paper, we propose a novel approach to support domain-specific Model-Driven Software Engineering (MDSE) for the real-world use-case scenarios of smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We argue that the majority of available data in the nature for Artificial Intelligence (AI), specifically Machine Learning (ML) are unlabeled. Hence, unsupervised and/or semi-supervised ML approaches are the practical choices. However, prior work in the literature of MDSE has considered supervised ML approaches, which only work with labeled training data. Our proposed approach is fully implemented and integrated with an existing state-of-the-art MDSE tool to serve the CPS/IoT domain. Moreover, we validate the proposed approach using a portion of the open data of the REFIT reference dataset for the smart energy systems domain. Our model-to-code transformations (code generators) provide the full source code of the desired IoT services out of the model instances in an automated manner. Currently, we generate the source code in Java and Python. The Python code is responsible for the ML functionalities and uses the APIs of several ML libraries and frameworks, namely Scikit-Learn, Keras and TensorFlow. For unsupervised and semi-supervised learning, the APIs of Scikit-Learn are deployed. In addition to the pure MDSE approach, where certain ML methods, e.g., K-Means, Mini-Batch K-Means, DB-SCAN, Spectral Clustering, Gaussian Mixture Model, Self-Training, Label Propagation and Label Spreading are supported, a more flexible, hybrid approach is also enabled to support the practitioner in deploying a pre-trained ML model with any arbitrary architecture and learning algorithm.

In this paper, we present the novel early tool prototype of ML-Quadrat, which is an open source research prototype, based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers, who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Data Analytics and Machine Learning (DAML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github: https://github.com/arminmoin/ML-Quadrat. Additionally, the novel early tool prototype of DriotData, a Low-Code platform targeting citizen data scientists and citizen/end-user software developers is demonstrated. DriotData exploits and adopts ML-Quadrat and offers an extended version of it as a web-based service to companies, especially Small- and Medium-Sized Enterprises (SME). A basic web-based demo of the Minimum Viable Product (MVP) of DriotData is already available. Finally, a short video demonstrating the tools is available on YouTube: https://youtu.be/YCNFfhmy_JY.

With the emergence of IoT, there is a rising interest in applying Internet of Things (IoT) technology in the smart homes for making occupants' life more convenient. The convenience is underpinned by the principle of the least effort, i.e. the premise that humans would usually want to achieve goals with the least cognitive and physical efforts [2]. IoT refers to the networked interconnection of everyday things, which are augmented with capabilities such as sensing, actuating, and communication [21]. The availability of IoT devices including switch sensors, infrared motion sensors, pressure sensor, wearable sensors, accelerators, temperature, humidity, and light sensors have the potential to realize the convenience. It is a challenge that IoT devices are highly diverse in supporting infrastructure such as different programming language and communication protocols [5].

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting. We discover activity patterns and temporal relations such as the order of activities from real data to develop a prompting system. Analysis of real data collected from smart homes was used to validate the proposed method.

The majority of IoT services include (or claim to include) some aspect of AI in their solution. This is due to a wide diversity in AI definitions (supervised/unsupervised, reinforced/deep learning) and the hype surrounding AI. (Note: All IoT services should take advantage of this hype while it lasts.) Let's look at the most common AI features and IoT industries to consider how IoT service owners can best evaluate AI and answer the questions above. IoT cloud platform providers are offering powerful AI visual recognition APIs. For example, developing a human visual recognition tool has now become a trivial exercise for developers, and the cost of using visual recognition in IoT services has reduced drastically.

AWS IoT 1-Click, AWS IoT Device Management, AWS IoT Device Defender, AWS IoT Analytics, Amazon FreeRTOS, and AWS Greengrass ML Inference make getting started with IoT as easy as one click, enable customers to rapidly onboard and easily manage large fleets of devices, audit and enforce consistent security policies, and analyze IoT device data at scale. Amazon FreeRTOS is an operating system that extends the rich functionality of AWS IoT to devices with very low computing power, such as lightbulbs, smoke detectors, and conveyor belts. And, AWS Greengrass ML Inference is a new capability for AWS Greengrass that allows machine learning models to be deployed directly to devices, where they can run machine learning inference to make decisions quickly, even when devices are not connected to the cloud. To get started, visit: https://aws.amazon.com/iot "AWS IoT Device Management has helped streamline our device onboarding, which has enabled us to meet our planned production throughput for connected devices. With AWS doing the undifferentiated heavy lifting for our IoT platform, we can spend more time on our customers than on our infrastructure."