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Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [ABSTRACT FROM AUTHOR]

Summary: The frequent geographical changes of mobile nodes in Internet of Things (IoT) systems affect communication, activities, and behaviors. In such scenarios, it is crucial to establish a system model capable of evaluating quality of service (QoS) measures. However, the existing formal modeling techniques pose complexities in modeling mobility. To deal with these challenges, this study aims to propose a model that simplifies the process of modeling mobility within IoT systems. This paper presents a method for modeling mobility within IoT systems by leveraging a widely recognized extension of stochastic Petri nets known as stochastic reward nets (SRNs). The proposed method enhances the SRN model by incorporating the location concept, resulting in a novel extension called mobile SRN (MSRN). In this work, a case study utilizes the MSRN to evaluate the suggested features, examining various scenarios and investigating the impact of factors such as environmental conditions, sensor sampling rate, and the permissible distance of the node from the sink. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

Edge–cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network's edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge–cloud-assisted IoT, named TL-ABKS, using edge–cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient. [ABSTRACT FROM AUTHOR]

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity. [ABSTRACT FROM AUTHOR]

Digital agriculture supports farmers' decision making to improve productivity and profitability. However, adoption of digital technology is uneven. Through interviews with 21 medium acreage almond growers and crop consultants in California's Central Valley, we examine barriers to adoption through the lens of technology acceptance models, Technology Acceptance Model (TAM-3) and Unified Theory of Acceptance and Use of Technology (UTAUT-2). Not surprisingly, farmers are willing to adopt technology when profitability and ease of use are shown, with economic returns (either anticipated or demonstrated) from the technology investment serving as the primary factor influencing adoption. Trust operates as a moderating factor to the desire for economic returns that influences adoption. There may be trust, or lack of trust, in technology performance or in the advisors who recommend it. Producer trust is affected by expectations of technology relevance and usefulness, and it is influenced by prior experience. Concerns about data management (e.g., governance, quality, privacy, security) take a back seat to more practical issues such as profitability, leaving producers in an imbalanced position with tech companies who have an interest in their agricultural data. We assert that producer acceptance of data management practices (despite their uncertainty in how to utilize the data being generated) implies that there is a basic level of trust in tech companies' data management practices that is consistent with models of moralistic trust behaviors for precision ag adoption. Our findings contribute to the growing research on digital agriculture that debates the benefits and downsides of digital agriculture. [ABSTRACT FROM AUTHOR]

Background: In response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services. Methods and Findings: To address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children's hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected. Conclusion: In conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), mobile edge computing based on Low Earth Orbit (LEO) satellites has attracted extensive attention. LEO satellites can break through the geographical restrictions, which have wide coverage and flexible deployment. They are the choice of future mobile communications. In this article, we consider the three-tier architecture of LEO satellites, relay nodes, and IoT devices with sensors. In this architecture, the IoT devices can choose devices with better communication conditions or base stations as relay nodes to assist task and data scheduling, which can effectively reduce system energy consumption and delay in the process of task and data scheduling. Moreover, we study computational resource allocation strategies and data scheduling strategies to minimize system energy consumption. Based on Lyapunov technology, we transform the problem into three sub-problems, namely local scheduling, data scheduling from IoT devices to relay devices and data scheduling from relay devices to LEO satellite. To solve three sub-problems, we design the sensory data scheduling algorithm (SDSA). Finally, we carry out simulation experiments to verify the performance of the algorithm. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new well‐structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost‐effective activities in the intelligent transportation systems. Federated Learning (FL) enhances privacy and security in intelligent transportation systems and the Internet of Vehicles (IoV), using advanced prediction methods. Integrating blockchain with IoT, particularly in FL for transportation systems and IoV, bolsters security and data integrity. This approach keeps data local while only sharing model updates, enhancing privacy. Blockchain's transparency aids in efficient IoT collaboration, crucial for accountability. Its consensus algorithms further ensure network integrity, validating transactions and updates across devices, protecting against attacks, and fostering a transparent, collaborative environment. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoVs. This review presents a novel categorization examining three main types of blockchain‐based FL approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Cloud (V2C). It highlights FL applications in cyber‐attack detection, data sharing, traffic prediction, and privacy, considering Quality of Service factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV. [ABSTRACT FROM AUTHOR]

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions. [ABSTRACT FROM AUTHOR]

In recent years, our time spent indoors has risen to around 90% and to maintain an occupant's comfort and well-being, Indoor Environmental Quality (IEQ) is monitored. Concerned with inhabitant's satisfaction and health, the adoption of smart solutions for IEQ monitoring and improvement has expanded. The solution this study explores is an occupant-centric approach involving the implementation of an Internet of Things (IoT) IEQ sensing network in a prominent office skyscraper in Hong Kong. Over the course of 15 months, real-time IEQ data were collected from 12 locations within the building. The data were collected at 1-min time intervals and consisted of readings of indoor air temperature, radiant temperature, relative humidity, air velocity, carbon dioxide (CO2), particulate matter (PM10 and PM2.5), horizontal illuminance levels, and sound pressure levels, which served as the basis of the assessment made about the qualities of thermal comfort, indoor air quality (IAQ), aural comfort, and visual comfort. Compared to traditional periodic surveys, this IoT-based sensing network captured instantaneous environmental variations, providing valuable insights into the indoor environment's spatial characterization and temporal dynamics. This smart solution also assisted facility management in terms of identifying sources of discomfort and developing effective mitigation strategies accordingly. This study presents an occupant-centric approach to improve occupant comfort and energy efficiency within office buildings. By customizing the built environment to enhance occupants' well-being, comfort, and productivity, an emphasis is placed on a more personalized and occupant-focused design strategy. This approach integrates technical design with human experience, highlighting the importance of real-time physical and subjective surveys for achieving optimal results. [ABSTRACT FROM AUTHOR]

In the construction of new power systems, the traditional network security protection mainly based on boundary protection belongs to static defense and still relies mainly on manual processing in vulnerability repair, threat response, etc. It is difficult to adapt to the security protection needs in large-scale distributed new energy, third-party aggregation platforms, and flexible interaction scenarios with power grid enterprise systems. It is necessary to conduct research on dynamic security protection models for IoT and other Blockchain-based IoT architectures. This article proposes a network security comprehensive protection model P2AEDR based on different interaction modes of cloud–edge interaction and cloud–cloud interaction. Through continuous trust evaluation, dynamic access control, and other technologies, it strengthens the internal defense capabilities of power grid business, shifting from static protection as the core mode to a real-time intelligent perception and automated response mode, and ultimately achieving the goal of dynamic defense, meeting the security protection needs of large-scale controlled terminal access and third-party aggregation platforms. Meanwhile, this article proposes a dynamic trust evaluation algorithm based on deep learning, which protects the secure access and use of various resources in a more refined learning approach based on the interaction information monitored in the system. Through experimental verification of the dynamic trust evaluation algorithm, it is shown that the proposed model has good trust evaluation performance. Therefore, this research is beneficial for trustworthy Power IoT and other Blockchain-based IoT architectures. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as an indispensable technology enabling the efficient management of energy consumption and deployment of renewable energy solutions within urban energy systems. While urban energy systems can be made sustainable in both smart cities (SC) and traditional cities, the former provide a more advantageous environment for achieving sustainability thanks to advanced technology integration, data-driven decision-making, integration of renewable energy, as well as policy and infrastructure support. Across the globe, cities are evolving into SCs through the implementation of sustainable urban energy systems and adaptation to the latest IoT technologies. For them, advanced cutting-edge technologies such as digital twins, artificial intelligence (AI), Big Data, and IoT are all positioned to underpin the civic platform for new forms of a city that is simultaneously sustainable and prosperous. The COVID-19 pandemic brought about significant challenges, emphasizing the need for resilient and sustainable urban environments and demonstrating that IoT became a must for the modern cities in energy generation, transmission, and the efficient use of energy sources by cities. This bibliometric review focuses on the new role of IoT in the development of post-COVID sustainable urban energy systems. It demonstrates how IoT can help to efficiently manage energy consumption and to deploy novel renewable energy solutions in the cities using the experience from the two years of the COVID-19 pandemic. The article explores the role of IoT in the post-COVID urban energy development highlighting how the technology can contribute to sustainable urbanization. It employs the extended literature review including both 151 publications retrieved from the Web of Science (WoS) database selected based on the relevant keywords and the analytical tools based on Google Trends as well as the enhanced network dynamics VOSviewer v.1.6.15 software (the latter being frequently used for identifying the dominant trends in intersectoral bibliometric research). The findings stemming from this research can contribute to the clustering of the discussion of the strategies required for boosting SCs’ development and growth. Therefore, the results and outcomes can provide many useful insights for academics and stakeholders alike on the role of the IoT in the development of sustainable urban energy in post-pandemic cities. [ABSTRACT FROM AUTHOR]

Several technologies are implemented for serving industrial information and controlling the process in which the increased complexity and allied fault failed to achieve a reliable transmission to activate the timely network. These data must be transferred from one node to another node without any fault or delay in their nodes. To resolve this problem, an algorithm named SAOA is proposed to communicate the messages from various services without any delay in their nodes. IoT with mobile sink node is simulated in the network based on the MoO4RPL objective and the phases involved in this strategy are topology, route discovery, communication, and route maintenance phase. The topology network is generated for network topology and the rank is computed based on the energy, trust, delay, fault tolerance, and link quality in the multi-objective route discovery phase. In the communication phase, the optimal route is selected using proposed SAOA with fitness function factors. At last, the route maintenance phase is performed, and the proposed SAOA has achieved a minimum distance of 53.807 m, maximum energy of 0.415 J, maximum link quality of 2.163, and maximum trust of 0.701, respectively. In the future, another algorithm is implemented in IoT applications with minimum modifications to evaluate the various metric values. [ABSTRACT FROM AUTHOR]

The architecture, engineering, and construction (AEC) industry in the US faces increasing labor shortages while accumulating downward productivity trends. Previous research efforts have shown that lack of timely access to information is one of the main factors hindering construction workers' productivity, and prompt information extraction (IE) from BIM sources can help address this issue. This study develops a BIM-web interoperable system, featuring a voice-based intelligent virtual agent (VIVA), to provide workers with information on-demand when conducting their activities on the jobsite. VIVA integrates construction digital sources with web platforms to enable real-time data communication between the users, workers in this case, and BIM platforms. The voice interaction feature of VIVA provides an intuitive and safe tool that can easily be used by workers with varying ranges of experience, with minimal need for training. The responsiveness and accuracy of VIVA is evaluated through two proof-of-concept case studies. Results indicate that the BIM-VIVA link and underlying natural language understanding (NLU) algorithms in the Google Actions platform accurately understand user queries, with a performance of 97.50% correct responses. As a result, VIVA achieves superior performance compared with previous research works based on NLU algorithms with text-based queries. The developments made in this research not only contribute to improving the safety and productivity of construction workers but also can open the door for easier onboarding of less-experienced workers, thus addressing the industry's severe skilled labor shortage issue. Practical Applications: This study introduces an innovative web-based system featuring VIVA to address the pressing labor shortage and diminishing productivity in the AEC industry. VIVA seamlessly integrates construction digital sources with web platforms, enabling real-time communication with construction workers and facilitating their on-demand information retrieval. The intuitive voice interaction feature of VIVA offers a safe and easily navigable tool that demands minimal training for workers with diverse backgrounds and experience levels. This feature also makes VIVA a practical tool for hands-free communication and information retrieval on jobsites, which minimizes the imposed distraction inherent in technological solutions. Construction workers can use voice commands to access project plans, schedules, and safety protocols, allowing them to retrieve critical information without having to stop work or use their hands. This intuitive interface reduces the need for complex user interfaces or manual input, making it easier for workers to interact with technology while focusing on their tasks. Hence, VIVA promises to improve productivity and safety of diverse group of workers without causing distraction or safety issues itself, which is a common concern when implementing technology to assist workers in hazardous and ever-changing construction jobsites. Beyond enhancing safety and productivity, widespread use of VIVA can help address the AEC sector's severe skilled labor shortage by providing an accessible means to onboard less-experienced workers. As such, VIVA provides opportunities to augment efficiency for seasonal/nonnative workers, thus facilitating the integration of a broader workforce and addressing the industry's pressing labor challenges. [ABSTRACT FROM AUTHOR]

Productivity assessment plays a key role in successful earthwork projects and is primarily achieved by monitoring key construction equipment such as excavators and dump trucks. Vision-based methods are widely adopted to analyze the productivity of construction equipment in earthwork projects. These methods are inexpensive and easy to implement and maintain. However, previous studies on vision-based productivity analysis of earthmoving equipment have predominantly demonstrated its effectiveness under favorable conditions characterized by stable lighting, areas devoid of shadows, and high-visibility environments free from significant occlusion by other objects or terrain. There is a significant illumination difference and limited visibility under harsh low-visibility conditions at earthwork construction sites, which makes it difficult to achieve reliable identification accuracy using these already developed methods. To address this problem, this study proposes an automatic vision-based dump truck productivity measurement method based on a deep learning illumination enhancement algorithm combined with transfer learning for low-visibility, harsh conditions at earthwork construction sites. This method uses an internet of things (IoT) system equipped with a camera to capture the image and a deep learning illumination enhancement algorithm, trainable deep hybrid network (TDHN), to enhance the image quality under low-light conditions at earthwork sites. Then, a deep convolutional neural network image recognition algorithm, ResNet50, was combined with a transfer learning technique to extract information from the image. Through the IoT, this processed information is sent to the earthwork platform to perform productivity analysis and make timely decisions regarding equipment allocation schemes. To validate the effectiveness of the proposed methodology, it was implemented in a real-time earthwork project. This study results show that image recognition accuracy of 99.6%, 95.67%, and 94.77% under normal lighting, low lighting, and extremely low lighting conditions, respectively. The dump truck recognition accuracy increased by 1.10%, 3.62%, and 21.19%, leading to a significant improvement in productivity measurement of 1.08%, 3.54%, and 20.71% for the above-mentioned lighting conditions, respectively. [ABSTRACT FROM AUTHOR]