Transport is Australia's third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions.
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http://dx.doi.org/10.3390/s22197380 | DOI Listing |
Sensors (Basel)
January 2025
Institute of Theoretical & Applied Informatics, Polish Academy of Sciences (IITiS-PAN), 44-100 Gliwice, Poland.
Edge computing systems must offer low latency at low cost and low power consumption for sensors and other applications, including the IoT, smart vehicles, smart homes, and 6G. Thus, substantial research has been conducted to identify optimum task allocation schemes in this context using non-linear optimization, machine learning, and market-based algorithms. Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency.
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January 2025
School of Cyber Science and Engineering, Liaoning University, Shenyang 110036, China.
Recently, there has been a growing interest in underground construction safety, during activities such as subway construction, underground mining, and tunnel excavation. While Internet of Things (IoT) sensors help to monitor these conditions, large-scale deployment is limited by high power needs and complex tunnel layouts, making real-time response a critical challenge. A delay-sensitive multi-sensor multi-base-station routing scheduling method is proposed for the IoT in underground mining.
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January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
With the rapid development of IoT technology, sensors are widely used for monitoring environmental parameters. The data collected by sensors needs to be stored, and distributed storage systems provide an excellent platform to handle this vast amount of data. To enhance data reliability and reduce storage costs, erasure coding technology can be employed within distributed storage systems.
View Article and Find Full Text PDFHeliyon
January 2025
Universitat de Lleida, Carrer Jaume II, 69, Lleida 25001, Spain.
This study conducts a bibliometric analysis of 252 scientific publications from 2001 to 2023, exploring the evolution and emerging trends in agricultural data spaces. Analyzing articles from the Web of Science and Scopus databases, we address six research questions: the current and interconnected key topics in agricultural data spaces (RQ1), the evolution of research themes over time (RQ2), emerging trends in the field (RQ3), the identification of leading researchers (RQ4), and the primary funding sources for this research area (RQ5), the relationship among research data and small farmers (RQ6). Our findings reveal a shift from traditional to innovative research themes, such as the increasing focus on the Internet of Things (IoT), Blockchain, and Digital Storage.
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