IoT, Volume 4, Issue 4 (December 2023) – 6 articles

The article discusses the modeling of the use of digital twin technologies (a digital twin) for the task of organizing long-term storage of various types of documents. A digital twin in this respect differs from a digital copy, which has no connection with the real original object. A review of the problem was carried out. We argue that, despite the active use of digital twin technology in various areas, its applications in the area of long-term storage of documents remain limited. At the same time, the task of increasing the durability of documents during long-term storage remains important and largely unresolved. The complexity of solving the problem of long-term storage of documents is considered, and a formal statement of the problem of long-term storage using digital twin technologies is carried out. Destructive factors that affect long-term storage documents and significantly reduce their durability have been identified. A system of indicators has been developed to assess the durability (preservation) of documents. The modeling of the use of digital twin technology in the organization of long-term preservation within the framework of Industry 4.0 was carried out. In the course of modeling, the goals and the strategies of long-term storage were established. Primary mathematical models for controlling destructive factors as well as technological solutions for digital twin long-term storage are proposed. It is assumed that the key part of this technology is the Industrial Internet of Things. The effectiveness of the use of digital twin technologies for solving the problem was assessed. The spheres of application and further ways of research are determined. Full article

The novel contribution of this research is decentralised IOTA-based concepts of digital trust for securing remote driving in an urban environment. The conceptual solutions are studied and described, and respective experimental solutions are developed relying on digital identities, public key cryptography with a decentralised approach using decentralised identifiers (DIDs) and verifiable credentials (VCs), and an IOTA-based distributed ledger. The provided digital trust solutions were validated by executing them according to the remote driving scenario but with a simulated vehicle and simulated remote driving system. The hybrid simulation mainly focused on the validation of functional, causal temporal correctness, feasibility, and capabilities of the provided solutions. The evaluations indicate that the concepts of digital trust fulfil the purpose and contribute towards making remote driving more trustable. A supervisory stakeholder was used as a verifier, requiring a set of example verifiable credentials from the vehicle and the remote driver, and accepting them to the security control channel. The separation of control and data planes from each other was found to be a good solution because the delays caused by required security control can be limited to the initiation of the remote driving session without causing additional delays in the actual real-time remote driving control data flow. The application of the IOTA Tangle as the verifiable data registry was found to be sufficient for security control purposes. During the evaluations, the need for further studies related to scalability, application of wallets, dynamic trust situations, time-sensitive behaviour, and autonomous operations, as well as smart contract(s) between multiple stakeholders, were detected. As the next step of this research, the provided digital trust solutions will be integrated with a vehicle, remote driving system and traffic infrastructure for evaluation of the performance, reliability, scalability, and flexibility in real-world experiments of remote driving of an electric bus in an urban environment. Full article

Fires kill and injure people, destroy residences, pollute the air, and cause economic loss. The damage of the fire can be reduced if we can detect the fire early and notify the firefighters as soon as possible. In this project, a novel Internet of Things (IoT)-based fire detector device is developed that automatically detects a fire, recognizes the object that is burning, finds out the class of fire extinguisher needed, and then sends notifications with location information to the user and the emergency responders smartphones within a second. This will help firefighters to arrive quickly with the correct fire extinguisher—thus, the spread of fire can be reduced. The device detects fire using a thermal camera and common objects using a red-green-blue (RGB) camera with a deep-learning-based algorithm. When a fire is detected, the device sends data using the Internet to a central server, and it then sends notifications to the smartphone apps. No smoke detector or fire alarm is available in the literature that can automatically suggest the class of fire extinguisher needed, and this research fills this gap. Prototypes of the fire detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully. Full article

The use of data centers is ubiquitous, as they support multiple technologies across domains for storing, processing, and disseminating data. IoT applications utilize both cloud data centers and edge data centers based on the nature of the workload. Due to the stringent latency requirements of IoT applications, the workloads are run on hardware accelerators such as FPGAs and GPUs for faster execution. The introduction of such hardware alongside existing variations in the hardware and software configurations of the machines in the data center, increases the heterogeneity of the infrastructure. Optimal job performance necessitates the satisfaction of task placement constraints. This is accomplished through constraint-aware scheduling, where tasks are scheduled on worker nodes with appropriate machine configurations. The presence of placement constraints limits the number of suitable resources available to run a task, leading to queuing delays. As federated schedulers have gained prominence for their speed and scalability, we assess the performance of two such schedulers, Megha and Pigeon, within a constraint-aware context. We extend our previous work on Megha by comparing its performance with a constraint-aware version of the state-of-the-art federated scheduler Pigeon, PigeonC. The results of our experiments with synthetic and real-world cluster traces show that Megha reduces the 99th percentile of job response time delays by a factor of 10 when compared to PigeonC. We also describe enhancements made to Megha’s architecture to improve its scheduling efficiency. Full article

Bowling is a target sport that is popular among all age groups with professionals and amateur players. Delivering an accurate and consistent bowling throw into the lane requires the incorporation of motion techniques. Consequently, this research presents a novel IoT Cloud-based system for providing real-time monitoring and coaching services to bowling athletes. The system includes two inertial measurement units (IMUs) sensors for capturing motion data, a mobile application, and a Cloud server for processing the data. First, the quality of each phase of a throw is assessed using a Dynamic Time Warping (DTW)-based algorithm. Second, an on-device-level technique is proposed to identify common bowling errors. Finally, an SVM classification model is employed for assessing the skill level of bowler athletes. We recruited nine right-handed bowlers to perform 50 throws wearing the two sensors and using the proposed system. The results of our experiments suggest that the proposed system can effectively and efficiently assess the quality of the throw, detect common bowling errors, and classify the skill level of the bowler. Full article

The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems. Full article