In industrial environments, most production-related activities performed by human operators are often complex. Accurate detections of these activities are pivotal as it can greatly help to assess productivity that can lead to improvement in worker training, as well as in other scenarios ensure a safe work environment and reducing injuries. Existing datasets on wearable Internet of Things (IoT) for human activity recognition primarily focuses on general activities, such as walking, running, etc., and therefore, related machine learning models and datasets are not suitable for application to industrial environments. In this paper, we present a novel dataset for classifying human operator activities in a meat processing plant where production line operators use knives to cut, process and produce meat products. Our dataset contains human operator activity data captured using wearable IoT sensors collected from a meat processing production facility. Through extensive experiments using machine and deep learning, we demonstrate that our dataset is effective and useful for detecting different activities of a human operator working in an industrial environment. To the best of our knowledge, this is the only real-world IoT dataset that will be made publicly available to support further research into industrial activities recognition. Our dataset and related experiments are available at https://digitalinnovationlab.github.io/mppdataset.

Producing high-quality product consistently is crucial in manufacturing, as discarding or reprocessing low-quality products increases waste and energy consumption and reduces overall production efficiency. Ensuring high-quality manufactured products is challenging due to relying on human activities for product quality and related consistency assessment, which is often performed postproduction instead of assessing these during each production run and making real-time production adjustments that can mitigate product quality and related consistency issues. In this article, we proposes a novel machine-state learner algorithm that captures the dependencies between product quality and related consistency and machine data (specifically the machine settings and corresponding sensor data). In addition, this article shows how this novel machine-state learner algorithm can be used to predict product quality during the production runs and how such prediction are used to make machine setting recommendations that mitigate product quality and related consistency issues before or during the production runs. These advances in machine state-based data modeling, predictive data analysis and recommendation are incorporated into an inline prediction and decision support system that achieves significant improvement in producing high-quality products consistently by guiding decision-making via recommendations during production in a digital manufacturing environment. In this article, we present an evaluation of the above contributions in a real-world manufacturing plant and yield double digit first pass and nearly perfect second pass product improvements in terms of product quality and related consistency and production efficiency.

The unprecedented growth of Internet of Things (IoT) is leading to its increased usage in various domains, such as manufacturing, health, and smart cities. A majority of IoT applications are autonomic, i.e., they operate under minimal/no human intervention, and make decisions/actuations based on machine-to-machine communication and data analytics. A key challenge in the development of such applications is the ability to measure their quality while they are working in a diverse and heterogeneous IoT ecosystem. In this article, we propose an agent-based IoT-Quality Watch (IoT-QWatch) framework that provides the ability to measure IoT quality metrics at each stage of the autonomic IoT application life cycle running in the IoT ecosystem. We envision that IoT-QWatch will enable the development of a new generation of quality-aware autonomic IoT applications that are able to be resilient to the heterogeneous and uncertain nature of IoT ecosystems. We present architectural details and implementation of IoT-QWatch, and corresponding models used to measure IoT quality metrics at different stages. We conduct extensive experiments using a real-world IoT test bed from the domain of manufacturing to validate the efficacy of IoT-QWatch. Experimental outcomes provide promising results in realizing IoT-QWatch in real-world deployment, while the framework itself offers significant extensibility to include new models for measuring IoT quality metrics.

The unprecedented growth of Internet of Things (IoT) underpinned by machine to machine communication, analytics and actuation is spearheading the development of autonomic IoT applications in areas such as Smart Cities. Such autonomic IoT applications have minimal human involvement in the decision making and actuation process. A key challenge in developing such autonomic IoT applications is uncertainty in the data produced by the IoT devices with data freshness being a critical aspect. In this paper, we address this challenge by introducing Age of Data (AoD), a metric to quantify the freshness of the data produced by IoT devices. We analyse the impact of AoD on IoT applications and propose a model for computing AoD that can be used by IoT applications in the decision making process. We validate the proposed model via experimental evaluations using real-world data obtained from parking sensors. Our analysis found that in real-world scenarios, 21.4% of sensors provide data that is outdated by several hours. We show that incorporating AoD in the application logic leads to improved application decision making.

Human-computer interaction provides pervasive services advocating several exciting interactive systems, such as remote automation, surgery, and rehabilitation. This paper studies a tight synchronization between human and robot hands to establish near to real-time maneuvering. We leverage the computing resources of the participating units of the master domain to determine the useful data by overserving and predicting the immediate reaction of the human hand movement. Moreover, we consider the unreliable wireless channels that lead to packet error during data transmission from the master domain to the controlled domain. In particular, by bringing the concept of edge computing while utilizing the Raspberry Pis's available yet limited computing resources, we aim to determine the balance between useful data and redundant packets without any significant performance degradation. Finally, we implement the proposed synchronization method of human-robot arm interactions in a real testbed and compare the performance with baselines.

Due to the significant communication delay to user tasks, the cloud is not ideal for executing real-time tasks with stringent deadlines. Fog computing consists of low computation capability fog nodes, or cloudlets located in proximity to the source of the data generation: the users. These cloudlets are ideal for executing tasks that have early deadlines. In this paper, we propose algorithms that schedule a set of real-time tasks on such an embedded-fog-cloud architecture. We consider hard, firm and soft tasks. The execution framework consists of embedded, fog and cloud processors. Tasks are scheduled on appropriate processors based on their deadline requirements. In general, hard real-time tasks are executed on embedded processors, firm real-time tasks on fog processors, and soft real-time tasks on cloud processors. We also propose a sufficient schedulability condition. Simulation results from the CERIT trace as well as test-bed results show that the proposed algorithms offer superior performance as compared to algorithms that do not employ fog processors. Employing an$Embedded-fog-cloud$architecture offers an improvement of 62.37 percent for real-time Success Ratio (SR) and 35 percent for Average Response Time as compared to scheduling tasks on the$cloud$alone.