Showing papers by "Dimitrios Georgakopoulos published in 2019"

An Industrial IoT Solution for Evaluating Workers' Performance Via Activity Recognition

Abstract: The Industrial Internet of Things (IIoT) is a key pillar of the Fourth Industrial Evolution or Industry 4.0. It aims to achieve direct information exchange between industrial machines, people, and processes. By tapping and analysing such data, IIoT can more importantly provide for significant improvements in productivity, product quality, and safety via proactive detection of problems in the performance and reliability of production machines, workers, and industrial processes. While the majority of existing IIoT research is currently focusing on the predictive maintenance of industrial machines (unplanned production stoppages lead to significant increases in costs and lost plant productivity), this paper focuses on monitoring and assessing worker productivity. This IIoT research is particularly important for large manufacturing plants where most production activities are performed by workers using tools and operating machines. With this aim, this paper introduces a novel industrial IoT solution for monitoring, evaluating, and improving worker and related plant productivity based on workers activity recognition using a distributed platform and wearable sensors. More specifically, this IIoT solution captures acceleration and gyroscopic data from wearable sensors in edge computers and analyses them in powerful processing servers in the cloud to provide a timely evaluation of the performance and productivity of each individual worker in the production line. These are achieved by classifying worker production activities and computing Key Performance Indicators (KPIs) from the captured sensor data. We present a real-world case study that utilises our IIoT solution in a large meat processing plant (MPP). We illustrate the design of the IIoT solution, describe the in-plant data collection during normal operation, and present the sensor data analysis and related KPI computation, as well as the outcomes and lessons learnt.

Advancements towards Global IoT Device Discovery and Integration

Abstract: This position paper presents recent results in developing a blockchain-based solution for Global IoT Device Discovery and Integration (GIDDI). GIDDI makes IoT application development more efficient and cost-effective via enabling (1) sharing and reuse of existing IoT devices owned and maintained by different providers, and (2) deployment of new IoT devices that is supported by a revenue generation scheme for their providers. More specifically, this paper proposes a distributed IoT ledger that is specifically designed for managing the metadata needed for GIDDI. This GIDDI Blockchain is IoT-owned (i.e., it is not controlled by any individual or organization) and is IoT-scaled (i.e., it can support the discovery and reuse of millions of IoT devices). In addition to the GIDDI Blockchain, this paper outlines the design of a GIDDI Marketplace that provides the functionality needed for IoT device registration, query, integration, payment and security via the proposed GIDDI Blockchain. Finally, the paper outlines a prototype GIDDI Blockchain implementation and discusses future research in this area.

Internet of Things-based Hydrocarbon Sensing for Real-time Environmental Monitoring

Abstract: The Internet of Things (IoT) aims to address important challenges in our environment, industries, cities, homes and society by collecting, integrating and analysing data from potentially millions of sensors and other internet-connected devices. In this paper we propose a novel IoT-based solution that provides real-time detection of hydrocarbon pollution that can be generated by retail fuel outlets (which are also referred to as service or filling stations). Our solution includes a low-cost but highly accurate fibre optic sensor that can detect hydrocarbons in ground water and can be easily deployed in existing monitoring wells. These hydrocarbon sensors are a key part of an IoT sensor data collection and analysis platform that utilises commercially available sensor nodes to communicate via low power networks (e.g. LORA) to collect and analyse hydrocarbon pollution data in the cloud. This novel IoT platform that combines hydrocarbon sensing with cloud-based data analysis to produce continuously updated hydrocarbon pollution maps and alerts can detect and report hydrocarbon pollution in real-time. The platform could potentially collect hydrocarbon pollution levels from millions of such sensors deployed in thousands of service stations around the world and automatically analyse such data in the cloud to produce continuously and instantaneously updated hydrocarbon pollution maps and related alerts for individual service stations, corporate chains and environmental monitoring agencies.