ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management

Self Cite

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Section: Related Work In Situation Awareness Systems and Cyber-physic...mentioning

confidence: 99%

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness

Mirza

Georgakopoulos

Yavari

2023

Self Cite

“…Despite the manufacturing industry being one of the early adopters of these technologies, it still faces significant challenges in terms of implementation and adoption [ 4 , 5 ]. While in some areas companies have successfully integrated advanced technologies such as the Internet of Things (IoT) [ 6 , 7 ], artificial intelligence (AI) [ 8 ], and robotics [ 9 ] into their operations, others have struggled to keep up [ 10 , 11 ]. In various industries such as oil and gas refineries [ 12 , 13 ], chemicals [ 14 ], and manufacturing [ 15 , 16 ], numerous physical processes require monitoring of values like voltage, current, pressure, and temperature.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Solution for Analog Gauge Monitoring Using Computer Vision in an IoT Platform

Peixoto,

Sousa,

Carvalho

et al. 2023

The emergence of Industry 4.0 and 5.0 technologies has enabled the digital transformation of various processes and the integration of sensors with the internet. Despite these strides, many industrial sectors still rely on visual inspection of physical processes, especially those employing analog gauges. This method of monitoring introduces the risk of human errors and inefficiencies. Automating these processes has the potential, not only to boost productivity for companies, but also potentially reduce risks for workers. Therefore, this paper proposes an end-to-end solution to digitize analog gauges and monitor them using computer vision through integrating them into an IoT architecture, to tackle these problems. Our prototype device has been designed to capture images of gauges and transmit them to a remote server, where computer vision algorithms analyze the images and obtain gauge readings. These algorithms achieved adequate robustness and accuracy for industrial environments, with an average relative error of 0.95%. In addition, the gauge data were seamlessly integrated into an IoT platform leveraging computer vision and cloud computing technologies. This integration empowers users to create custom dashboards for real-time gauge monitoring, while also enabling them to set thresholds, alarms, and warnings, as needed. The proposed solution was tested and validated in a real-world industrial scenario, demonstrating the solution’s potential to be implemented in a large-scale setting to serve workers, reduce costs, and increase productivity.

“…We previously conducted research in developing and installing an Internet of Things (IoT)-based platform, called ParcEMon [ 14 ], to capture live data from a delivery van, so as to analyse parcel-level emissions. During this research, multiple challenges were faced, in retrieving continuous data from the delivery van by using a gas analyser comprising different gas sensors, so as to measure the concentration of carbon monoxide (CO), carbon dioxide (CO

), hydrocarbons (HC), oxygen (O

), and nitrogen oxide (NO

) gases, as shown in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring

Yavari,

Mirza,

Bagha

et al. 2023

Self Cite

Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors. However, connecting, maintaining, and operating gas sensors on moving vehicles in different road and weather conditions is a large and costly challenge. This paper presents the development and evaluation of a reliable and accurate sensing technique for GHG emissions collection (or monitoring) in real-time, employing the Internet of Things (IoT) and Artificial Intelligence (AI) to eliminate or reduce the usage of gas sensors, using reliable and cost-effective solutions.