Asset Tracking Research Articles

PLANIMETRIC RAIL POSITIONING USING UAV PHOTOGRAMMETRY: TOWARDS AUTOMATED AND SAFE RAILWAY INFRASTRUCTURE MONITORING

Abstract. The maintenance of railway infrastructure requires detailed inspection of track assets including the rails, sleepers, fasteners, and tie plates. Current methods using total stations and measurement trains are costly and the subsequent data processing is often manual. A potent alternative is the use of Unmanned Aerial Vehicles (UAV) to investigate track asset deviations. However, the potential degree of automation and the overall accuracy is still very much the subject of ongoing research. In this work, a potential pipeline is investigated for the planimetric inspection of rails using UAV photogrammetry. Specifically, state-of-the-art line detectors such as Holistically-Nested Edge Detection, DexiNed, Segment Anything Model, and Mobile Line Segment Detection are combined with logical filtering to assess the localisation and gauge of the rails. The experiments indicate that the accuracy and detection rate are promising. Overall, the proposed method is a promising step towards affordable and safe railway infrastructure inspection.

Radio-Frequency Identification Traceability System Implementation in the Packaging Section of an Industrial Company

In recent years, radio-frequency identification (RFID) has aroused significant interest from industry and academia. This demand comes from the technology’s evolution, marked by a reduction in size, cost, and enhanced efficiency, making it increasingly accessible for diverse applications. This manuscript presents a case study of the implementation of an RFID traceability system in the packaging section of an industrial company that produces test equipment for the automotive wiring industries. The study presents the proposal and execution of a prototype asset-tracking system utilising RFID technology, designed to be adaptable and beneficial for various industrial settings. The experiments were carried out within the company’s shop-floor environment, alongside the existing barcode system, with the primary objective of evaluating and comparing the proposed solution. The test results demonstrate a significant enhancement in production efficiency, with substantial optimization achieved. The time required for asset identification and tracking was significantly reduced, resulting in an average time of approximately 43.62 s and an approximate 3.627% improvement in the time required to read the test sample of assets when compared to the barcode system. This successful implementation highlights the potential of RFID technology in improving operations, reducing working time, and enhancing traceability within industrial production processes.

AI-ERA: Artificial Intelligence-Empowered Resource Allocation for LoRa-Enabled IoT Applications

Adaptive data rate (ADR) is a widely adopted resource assignment approach in long-range wide-area networks (LoRaWANs) for static Internet of Things (IoT) applications such as smart grids and metering. Blind ADR (BADR) has been recommended for mobile IoT applications such as pet and industrial asset tracking. However, ADR and BADR cannot provide appropriate measures to alleviate the massive packet loss problem caused by the unsuitable spreading factors (SFs) assigned to end devices when they are mobile. This paper proposes a novel proactive approach—“artificial intelligence-empowered resource allocation” (AI-ERA)—to address the resource assignment issue in static and mobile IoT applications. The AI-ERA approach consists of two modes, namely offline and online modes. First, a deep neural network (DNN) model is trained with a dataset generated at ns-3 in the offline mode. Second, the proposed AI-ERA approach utilizes the pre-trained DNN model in the online mode to proactively assign an efficient SF for the end device before each uplink packet transmission. The proactive behavior of the AI-ERA improved the packet success ratio by an average of 32% and 28% in static and mobility scenarios compared with the typical LoRaWAN ADR, respectively.

COMPARATIVE ANALYSIS OF REAL-TIME LOAD TRACKING TECHNOLOGIES

Real Time Locating Systems (RTLS) and Radio Frequency Identification (RFID) are key technologies in asset tracking and management. Although they may look similar at first glance, they have different characteristics, advantages and disadvantages that make them suitable for different applications.

Threat Modeling for Communication Security of IoT-Enabled Digital Logistics

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure.

TRACE: Transformer-based continuous tracking framework using IoT and MCS

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target tracking solutions are either energy-inefficient or are only effective for fixed-length trajectories, making them impractical for real-world applications. For robust predictive tracking, with irregular trajectory lengths, energy efficiency and accuracy are vital to ensure system’s longevity and reliability. In this work, using a combination of trajectory prediction and path correction techniques, a novel approach, TRACE, is proposed for continuously tracking a target in an environment. TRACE uses locations offered by IoT/MCS localization systems to make predictions about the target’s future movement. A transformer neural network is implemented to learn mobility patterns to predict the target’s future trajectory. To ensure accurate predictions, a path correction mechanism is devised, by updating the predicted trajectory using polynomial regression. Experiments are conducted using a real-world GeoLife dataset to evaluate the performance of the proposed approach. The results demonstrate that TRACE performs better than existing tracking techniques with an improvement in accuracy of about 50% while using 85% less energy, supporting the potential of the proposed approach for enhancing target tracking in IoT/MCS applications.

Specific object finding in point clouds based on semantic segmentation and iterative closest point

This paper proposes an efficient and accurate process to find instances of a specific object in a point cloud scene where the object is unique, a member of a trained class, and the unique object's class is known. Object finding is essential for multiple applications like object detection, pose estimation, and asset tracking. Current template matching and object detection methods are slow or approximate feature matching to match objects, which generalize classes of objects and do not differentiate between specific objects. This paper proposes a specific-object finding methodology based on existing point cloud segmentation, fully convolutional geometric features, and a color-based iterative closest point algorithm. A point cloud template and scene are used, the latter is segmented, the resulting points matching the template's label are isolated, generating candidates. The candidate's geometric features are matched and compared with the point cloud template. The methodology results generate high accuracy for specific-object matching.

A survey of indoor positioning systems based on a six-layer model

Indoor positioning has attracted considerable interest in both the industry and academic communities because of its wide range of applications, such as asset tracking, healthcare and context-aware services like targeted advertisements. While there are many indoor localisation methods, each has its advantages and disadvantages, taking into consideration various factors such as the effect of the indoor environment, ease of implementation, computational cost, positioning accuracy, etc. In other words, no single solution can cater for all different situations. Although many survey papers have been published on indoor positioning, new techniques and methods are proposed every year, so it is important to stay abreast of its latest developments. In addition, each survey has its own classification for indoor positioning systems without a common scheme. Inspired by the well-known OSI model and TCP/IP model, it would be desirable to develop a systematic framework for studying indoor positioning systems. In this paper, we make this new contribution by introducing a systemic survey framework based on a six-layer model to give a comprehensive survey of indoor positioning systems, namely: device layer, communication layer, network layer, data layer, method layer and application layer. Complementing the previous survey papers, this paper provides a survey of the latest research works on indoor positioning based on the six-layer model. Our emphasis is on systematic categorisation, machine learning-based enhancements, collaborative localisation and COVID-19-related applications. The six-layer model should provide a useful framework and new insights for the research community.

Co-Circle: Energy-Efficient Collaborative Neighbor Discovery for IoT Applications

Device discovery or neighbor discovery, which enables devices to detect other devices in the vicinity, plays an important role in many IoT applications as it is a prerequisite for wireless communication and interaction with each other. In this paper, we study collaborative neighbor discovery, where multiple sensors connected to a gateway collaborate to discover another mobile device. Such a scenario can be found in many applications such as automotive keyless access system, smart home monitoring, asset tracking, and safety monitoring. We first build an analytical model called Co-Circle for collaborative neighbor discovery. Based on Co-Circle, we derive the lower bound of the expected discovery latency. Then, we show how the lower bound of the expected discovery latency varies with the energy budget (or duty cycle). Finally, we propose an optimal collaboration scheme that can achieve the lower bound of the expected discovery latency. Experimental results verify the performance of proposed collaboration scheme.

Phase-Based Ranging in Narrowband Systems With Missing/Interfered Tones

The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such applications. Phase-based ranging has recently gained momentum due to its high accuracy in multipath environment compared to traditional schemes such as ranging based on received signal strength. The phase-based ranging requires tone exchange on multiple frequencies on a uniformly sampled frequency grid. Such tone exchange may not be possible due to some missing tones, e.g., reserved advertisement channels. Furthermore, the IQ values at a given tone may be distorted by interference. In this paper, we proposed two phase-based ranging schemes which deal with the missing/interfered tones. We compare the performance and complexity of the proposed schemes using simulations, complexity analysis and measurement setups. In particular, we show that for small number of missing/interfered tones, the proposed system based on employing a trained neural network (NN) performs very close to a reference ranging system where there is no missing/interference tones. Interestingly, this high performance is at the cost of negligible additional computational complexity and up to 60.5 Kbytes of additional required memory compared to the reference system, making it an attractive solution for ranging using hardware-limited radios such as BLE.

Robust and fast algorithm design for efficient Wi-Fi fingerprinting based indoor positioning systems

Indoor positioning systems (IPS) based on Wi-Fi fingerprinting have gained significant attention due to their potential for providing location-based services. Large scale IPS deployments require implementation of robust, accurate, and fast algorithms. Data analytics assisted algorithms provide positioning accuracy improvements, however their integration within real-time and scalable solutions significantly depends on the computational complexity. Therefore, we propose robust and computationally efficient algorithms for performance enhancements in Wi-Fi fingerprinting-based IPS. A robust radio map algorithm based on enhanced statistical cluster initialization was designed for efficient indoor environment characterization. The proposed data filtering algorithm leveraged smart clustering to mitigate real-time data variations. The designed area classification algorithm was based on smart dual-band data aggregation. We evaluated the performance of proposed algorithms based on accuracy and computation time. The performance evaluations signified accuracy and computational enhancement, in comparison to related benchmark techniques. The data filtering and area classification algorithms required 40% less computation time. Simultaneously, 14.5% and 36% accuracy improvements were recorded for the area classification and radio map algorithms respectively. The proposed algorithms have the potential to significantly enhance IPS performance in a variety of real-time applications, including indoor navigation and asset tracking.

Asset Tracking and Management System

Abstract: This abstract presents an overview of an IoT-based asset tracking and management system. The system utilizes Internet of Things (IoT) technologies to enable real-time monitoring and tracking of assets in diverse industries. Today, the growth of technology is rapid and provides all necessary and effective solutions for their requirements. One of the most important areas of concern is security. In this scenario, IoT asset tracking is developed to increase the safety of any valuable items. The Internet of Things technology (IoT) can track and monitor an asset in the outdoor and indoor environment. This empowers business and end-users with the information and opportunity to run their operations effectively and make educated decisions respectively. The management of assets is a critical process for businesses to ensure that they are operating effectively. The system provides organizations with valuable insights into the location and utilization of their assets. It highlights the advantages of adopting this technology such as improved asset visibility and enhanced security. Furthermore, the abstract explores potential applications of IoT-based asset tracking in sectors such as logistics, transportation, and inventory management. Overall, this abstract demonstrates the significance of IoT in revolutionizing asset tracking and management processes ultimately leading to cost savings

A paradigm shift in efficiency and service for commercial buildings

Best-in-class facilities management (FM) is all about high-quality service at a speed faster than ever before. This paper uses case studies to examine in detail the following technologies: indoor mapping and asset tracking, digital kiosks, hotelling and workspace surveying, elevator maintenance auditing, water leak detection, NanoSeptic button covers, 3D virtual tours of buildings, artificial intelligence (AI) for preventive maintenance, emergency light testing, construction management platform, lease abstracting data and analytics and autonomous vacuum systems. Each case study provides a description of the technology, the scope of implementation and the results of each endeavour. The reader will understand ten new technologies and be able to navigate the implementation details to set themselves up for the best chance to succeed.

Scenarios for digital twin deployment in healthcare facilities management

PurposeThis paper aims to focus on identifying key health-care issues amenable to digital twin (DT) approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare facilities management (FM) using four different scenarios. The scenario planning focused on monitoring and controlling the heating, ventilation, and air-conditioning system in real-time; monitoring indoor air quality (IAQ) to monitor the performance of medical equipment; monitoring and tracking pulsed light for SARS-Cov-2; and monitoring the performance of medical equipment affected by radio frequency interference (RFI).Design/methodology/approachThe importance of a healthcare facility, its systems and equipment necessitates an effective FM practice. However, the FM practices adopted have several areas for improvement, including the lack of effective real-time updates on performance status, asset tracking, bi-directional coordination of changes in the physical facilities and the computational resources that support and monitor them. Consequently, there is a need for more intelligent and holistic FM systems. We propose a DT which possesses the key features, such as real-time updates and bi-directional coordination, which can address the shortcomings in healthcare FM. DT represents a virtual model of a physical component and replicates the physical data and behavior in all instances. The replication is attained using sensors to obtain data from the physical component and replicating the physical component's behavior through data analysis and simulation. This paper focused on identifying key healthcare issues amenable to DT approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare FM using four different scenarios.FindingsThe scenarios were validated by industry experts and concluded that the scenarios offer significant potential benefits for the deployment of DT in healthcare FM such as monitoring facilities’ performance in real-time and improving visualization by integrating the 3D model.Research limitations/implicationsIn addition to inadequate literature addressing healthcare FM, the study was also limited to one of the healthcare facilities of a large public university, and the scope of the study was limited to IAQ including pressure, relative humidity, carbon dioxide and temperature. Additionally, the study showed the potential benefits of DT application in healthcare FM using various scenarios that DT experts validated.Practical implicationsThe study shows the practical implication using the various validated scenarios and identified enabling technologies. The combination and implementation of those mentioned above would create a system that can effectively help manage facilities and improve facilities' performances.Social implicationsThe only identifiable social solution is that the proposed system in this study can manually be overridden to prevent absolute autonomous control of the smart system in cases when needed.Originality/valueTo the best of the authors’ knowledge, this is the only study that has addressed healthcare FM using the DT approach. This research is an excerpt from an ongoing dissertation.

Faceting the post-disaster built heritage reconstruction process within the digital twin framework for Notre-Dame de Paris

April 15th, 2019: Notre-Dame Cathedral in Paris was burning, the spire collapsed on the nave, vaults crumbled and most of the timber roof was gone. In the post-disaster context, the authenticity and the monitoring of the archaeological remains are crucial for their potential reuse during reconstruction. This paper analyzes the collapsed transverse arch from the nave of Notre-Dame as a case study of reconstruction, using the digital twin framework. We propose four facets for the digital twin experiment—physical anastylosis, reverse engineering, spatio-temporal tracking of assets, and operational research—that are described in detail, while being assembled to support a hybrid reconstruction hypothesis. The digital twin can realize the parallel unfolding of physical-native and digital-native processes, while acquiring and storing heterogeneous information as semantically structured data. The results demonstrate that the proposed modeling method facilitates the formalization and validation of the reconstruction problem and increases solutions performances. As result, we present a digital twin framework application ranging from acquisition to data processing that informs a successful hybrid reconstruction hypothesis.

Internet of Things (IoT) in Supply Chain Management: Challenges, Opportunities, and Best Practices

The advent of the Internet of Things (IoT) has ushered in a transformative era in supply chain management, revolutionizing the way organizations monitor, analyze, and optimize their operations. This comprehensive survey paper explores the multifaceted landscape of IoT applications in supply chain management, shedding light on the challenges, opportunities, and best practices that define this technological paradigm shift. The paper delves into the fundamental principles of IoT, elucidating how sensor-laden devices, real-time data streams, and advanced analytics empower organizations with unprecedented visibility and control across their supply chains. It systematically examines IoT applications in key supply chain domains, including inventory management, asset tracking, cold chain monitoring, predictive maintenance, route optimization, and waste reduction. Each application is scrutinized for its role in enhancing efficiency, reducing costs, ensuring product quality, and advancing sustainability. Furthermore, this paper addresses the challenges inherent in implementing IoT within supply chains, such as data security, interoperability, scalability, and regulatory compliance. It underscores the importance of change management and workforce development in harnessing the full potential of IoT and presents a roadmap for best practices to overcome these obstacles. The paper culminates in a forward-looking exploration of future trends and innovations in the IoT-driven supply chain landscape. By offering a comprehensive overview of IoT's role in supply chain management, this paper equips practitioners, researchers, and decision-makers with a holistic understanding of the transformative power of IoT, empowering them to navigate the complexities, seize opportunities, and implement best practices that will define the future of supply chain management.

Flexible Hybrid Electronics via Near‐Infrared Radiation‐Assisted Soldering of Surface Mount Devices on Screen Printed Circuits

AbstractThe development of flexible hybrid electronics (FHEs) with high‐throughput integration of electrical components onto digitally printed circuits has a wide range of applications, such as asset tracking, wearable electronics, and structural health monitoring. However, one of the major challenges with FHEs is the process of soldering the electrical components onto a printed circuit while having minimal thermal damage to the printed traces and their temperature‐sensitive polymeric substrates. Here, the possibility of utilizing near‐infrared (NIR) technology as a nondestructive photonic approach for rapid soldering and mounting electrical components onto printed circuits while keeping the polymer substrate at a relatively low temperature during the soldering process is investigated. Results of this systematic study show that FHEs prepared with the optimized NIR processing conditions produce the desired reflow of solder with effective electrical connection and metallic bonding of electrical components onto the conductive traces with excellent mechanical stability (no failure even after 1000 cycles of bending). Furthermore, using this technique and as a proof of concept, the fabrication of a wearable FHE device that provides a remote assessment of the wound exudate absorption in dressings and notifies caregivers of the appropriate time to change the dressing is demonstrated.

An asset-backed decentralised finance instrument for food supply chains – A case study from the livestock export industry

Decentralised finance (DeFi) has emerged as a blockchain-enabled innovation in supply chains that makes use of supporting infrastructure to transform financial services by reducing or removing the need for intermediaries. While an increasing number of pilot projects have been exploring DeFi applications for use by small and medium enterprises (SMEs) in various finance scenarios, there is not yet a real-world adoption of DeFi beyond these projects to unlock their potential for progressing the digitisation of supply chain finance (SCF). This paper reports on a university-industry collaborative study to leverage the use of blockchain-based DeFi technology in two ways: (i) to benefit SMEs by reducing their cost of finance and increasing ease of access to funding, and; (ii) to address the prevalent asymmetric information risks and costs in a supply chain finance context. The research informed the design and development of a DeFi architecture for asset tracking in agricultural supply chains implemented and evaluated in a real-world pilot adoption with a producer in the Australian livestock industry. The use case shows that there are numerous risks pertaining to the adoption of DeFi for SCF. In response, the paper suggests new ways for supply chain SMEs to secure benefits from their investment in DeFi based on the key findings derived from evaluating our use cases.

IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

The assets in Industry 4.0 are categorized into physical, virtual, and human. The innovation and popularization of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc., for asset identification and tracking. The generated data from the Industrial Internet of Things (IIoT) ease information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of the manufacturing equipment. Predictive maintenance enables the business owner to decide, such as repairing or replacing the component before an actual failure that affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimize the task distributions and predictive maintenance model. This article presents the genetic algorithm (GA)-based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost, and energy performance of GA along with MinMin, MaxMin, FCFS, and RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time data sets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost, and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.

Adoption of Blockchain Technology in Education: Application & its Challenges

Abstract: Blockchain Technology has other applications outside of Bitcoin. This technology has the potential to be used in a variety of industries, including education, finance, insurance, healthcare etc. This article not only provides a broad overview of Blockchain Technology, but also provides descriptions of how it could be used in educational institutions. School records management, such as validation and transfer of student transcripts, degrees, test scores and records, including those related to college admissions; educator credentialing/certification/re-certification; management and tracking of school assets, such as buses, library books; management of student privacy and parental permissions.