Underground mines require continuous airflow monitoring, yet cabling or frequent battery replacement for distributed sensors is costly and hazardous. In this study, we present a piezoelectric wind energy harvester (PWEH) that also functions as a self-powered sensor for monitoring ventilation airflow conditions in underground mines. A shallow groove machined into the windward face of a D‑shaped bluff body boosts harvested power by 29.3% at a wind speed of 4.5 m/s compared with an ungrooved benchmark. The resulting voltage waveform supports two embedded sensing functions without external power: (i) ventilation fault diagnosis based on a stacking ensemble that integrates one-dimensional convolutional neural network (1D CNN), random forest (RF) and transfer-learned AlexNet classifier, enhanced by manifold‑based data augmentation, to identify 16 fault scenarios in a laboratory mine ventilation layout with up to 97.4% accuracy; and (ii) wind speed estimation using Random Forest that delivers a median mean absolute error below 0.12 m/s and worst‑case error under 0.5 m/s across 1.5–5.5 m/s airflow speeds, matching the performance of commercial anemometers. Laboratory trials in a scaled mine ventilation layout validate these capabilities across 16 duct networks with fan speeds ranging from 200–700 rpm, while energy measurements show the device produces a root mean square power output of 5.2 μW at a 4.5 m/s wind speed, which is sufficient to run a Bluetooth Low Energy-based SensorTag for fog data transmission every 50 seconds. By combining wind energy harvesting and airflow sensing in a single device, the proposed PWEH offers a battery-free and low-maintenance path to safer and smarter Mine Internet of Things deployments.

In indoor wireless positioning systems, non-line-of-sight (NLOS) propagation caused by base station deployment constraints and environmental structures poses a major challenge to positioning accuracy. Mitigating the adverse effects of NLOS propagation without increasing deployment cost remains a fundamental challenge in indoor wireless positioning. To address this challenge, we propose a novel correction framework that utilizing ranging information and structural constraints to construct virtual line-of-sight (LOS) base stations as substitutes for NLOS measurements, thereby enabling effective utilization of NLOS signals. The proposed method models the structural information of the indoor environment in a planar form and, in combination with a location prediction algorithm, enables accurate identification of signal types in dynamic scenarios. Furthermore, an NLOS reconstruction algorithm is developed to infer feasible propagation paths of identified NLOS signals, allowing reliable virtual LOS base stations to be generated for subsequent localization. The entire framework operates rapidly without requiring any prior data collection, and improves positioning stability and system availability using only standard ranging measurements and structural constraints at a low computational cost. To validate the proposed approach, multiple low-cost ultra-wideband (UWB) base stations were deployed in a corridor environment, and pedestrian motion data were collected under dense NLOS conditions. Experimental results demonstrate a substantial improvement in positioning performance: the root mean square error (RMSE) of UWB positioning is reduced from over 4 meters to below 0.4 meters, confirming the effectiveness of the proposed solution.

Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa’s low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently utilize the limited available network bandwidth in these low-data-rate networks due to excessive control overhead, acknowledgments, and redundant retransmissions. In this paper, we introduce a novel position- and energy-aware routing strategy tailored for subterranean LoRa mesh networks aimed at enhancing maximum throughput and power efficiency while also maintaining high packet delivery ratios. Our mechanism begins with a lightweight position learning phase, during which LoRa repeaters ascertain their relative positions and gather routing information. Afterwards, the network becomes fully operational with adaptive routing, leveraging standby LoRa repeaters for recovery from packet collisions and losses, and energy-aware route switching to balance battery depletion across repeaters. The simulation results on a representative subterranean network demonstrate a 185% increase in maximum throughput and a 75% reduction in energy consumption compared to a previously optimized flooding-based approach for high traffic.

Localizing text under low-light conditions has gained attention, with typical approaches relying on two stage cascading modules that combine low-light enhancement and text localization. However, these often require additional enhancement modules and cause inefficiency in joint optimization. In this work, we address the challenge by adopting a novel approach: tailoring the detector for low light conditions through knowledge distillation from normal light conditions, without relying on any enhancement module. First, we design a Graph Topological Aggregation (GTA) model that utilizes the message passing mechanism of graph neural networks to structurally represent text topology and facilitate structured feature expression in knowledge transfer. We then introduce two specially designed knowledge transfer constraints aimed at enhancing the learning of text's multi-scale features and topological knowledge. Finally,we propose a Stage-wise Bilevel Knowledge Transfer learning strategy that designates the low-light learning process as the upper-level task, while treating normal light learning as the lower-level task, effectively addressing the coupling issues and sequential dependencies prevalent during the distillation process. Extensive experiments underscore the approach's superiority.

As industries increasingly integrate Internet of Things (IoT) technologies, standardisation is crucial for ensuring effective implementation. This study addresses gaps in the application of the Reference Architectural Model for Industry 4.0 (RAMI 4.0), a key standard for industrial standardisation, and examines its role in digitising the industrial sector. Despite the recognised importance of RAMI 4.0, unclear application procedures often hinder its adoption and effectiveness. This study conducts a thorough literature review to explore RAMI 4.0’s applications in various industrial contexts, establishing a foundation for understanding standards, IoT, and Industry 4.0 while clarifying how RAMI 4.0 is a framework for standardisation. The study focuses on the impact of IoT and Industrial IoT (IIoT) technologies on manufacturing practices. The analysis leads to a detailed procedural framework for applying RAMI 4.0 across various industries, with an emphasis on monitoring, system design, security, digitalisation, and IoT implementation. This study proposes a guideline to standardise smart manufacturing processes and enhance the integration of RAMI 4.0 principles. The findings outline methodologies to monitor IoT systems, approaches to improve system transparency, techniques to ensure security in IoT implementation, and structured steps for system design and digitalisation. Offering a structured approach to standardisation, the framework fosters a cohesive IIoT ecosystem and advances smart manufacturing practices. The methodology resolves the lack of uniformity and challenges in synthesising existing studies, improving the understanding and effectiveness of RAMI 4.0 applications across industries.

Human Activity Recognition is an important task in many human-computer collaborative scenarios, with various practical applications. Although uni-modal approaches have been extensively studied, they suffer from data quality issues and require modality-specific feature engineering, making them neither robust nor effective enough for real-world deployment. By utilizing various sensors, Multi-modal Human Activity Recognition can leverage complementary information to build models that generalize well. While deep learning methods have shown promising results, their potential in extracting salient multi-modal spatial-temporal features and better fusing complementary information has not been fully explored. Additionally, reducing the complexity of the multi-modal approach for edge deployment is another unresolved issue. To address these issues, a Knowledge Distillation-based Multi-modal Mid-Fusion approach, DMFT, is proposed to facilitate informative feature extraction and fusion for efficiently solving the Multi-modal Human Activity Recognition task. DMFT first encodes the multi-modal input data into a unified representation. The DMFT teacher model then applies an attentive multi-modal spatial-temporal transformer module that extracts the salient spatial-temporal features. A temporal mid-fusion module is also proposed to further fuse the temporal features. Subsequently, the Knowledge Distillation method is applied to transfer the learned representation from the teacher model to a simpler DMFT student model, which consists of a lite version of the multi-modal spatial-temporal transformer module, to produce the results. Evaluation of DMFT was conducted on two public multi-modal human activity recognition datasets alongside various state-of-the-art approaches. The experimental results demonstrate that the model achieves competitive performance in terms of effectiveness, scalability, and robustness.

Real-life graphs often exhibit intricate dynamics that evolve continuously over time. To effectively represent continuous-time dynamic graphs (CTDGs), various temporal graph neural networks (TGNNs) have been developed to model their dynamics and topological structures in Euclidean space. Despite their notable achievements, the performance of Euclidean-based TGNNs is limited and bounded by the representation capabilities of Euclidean geometry, particularly for complex graphs with hierarchical and power-law structures. This is because Euclidean space does not have enough room (its volume grows polynomially with respect to radius) to learn hierarchical structures that expand exponentially. As a result, this leads to high-distortion embeddings and suboptimal temporal graph representations. To break the limitations and enhance the representation capabilities of TGNNs, in this article, we propose a scalable and effective TGNN with hyperbolic geometries for CTDG representation (called ${\mathrm { STGN}}^{h}$ ). It captures evolving behaviors and stores hierarchical structures simultaneously by integrating a memory-based module and a structure-based module into a unified framework, which can scale to billion-scale graphs. Concretely, a simple hyperbolic update gate (HuG) is designed as the memory-based module to store temporal dynamics efficiently; for the structure-based module, we propose an effective hyperbolic temporal Transformer (HyT) model to capture complex graph structures and generate up-to-date node embeddings. Extensive experimental results on a variety of medium-scale and billion-scale graphs demonstrate the superiority of the proposed ${\mathrm { STGN}}^{h}$ for CTDG representation, as it significantly outperforms baselines in various downstream tasks.

Tunnel-like environments are often extremely long and large-scale, particularly narrow and confined. This paper investigates the problem of autonomously guiding a robot through a tunnel-like environment. To address this challenge, we initially create a skeleton-based reference curve to compute the normal and tangential direction of the tunnel-like environment. Then we create a barrier function to delineate the impenetrable nature of the environment’s boundary. Subsequently, we employ the principle of vector field to design a novel navigation law that guarantees the continuous advancement of the robot within the tunnel meanwhile simultaneously maintaining a predefined safety margin from the bounds of the environment. Conducted simulation experiments validate the effectiveness of the proposed vector field-based strategy for safely navigating a robot through tunnel-like environments.

Accurate time synchronization is crucial for multisensor fusion, which is widely used in mobile robotics, autonomous driving, and virtual reality. Despite many advancements, precise multi-sensor synchronization is still challenging due to the sensors’ internal characteristics, data filtering, disjointed clock reference, and transmission delay caused by operation system scheduling. This paper proposes a novel hardware-based synchronization solution to achieve synchronization in microsecond-level precision. By introducing a Sensor Adaptor board that provides a unified clock reference, the proposed hardware architecture enables high-precision synchronization across multiple sensors. Furthermore, we develop a method for Visual-Inertial time synchronization that actively controls the exposure duration using an ambient light sensor. By managing the IMU clock signal and exposure trigger, we align the camera’s sampling moment with the authentic IMU sampling time and significantly reduce the time discrepancy in the Visual-Inertial system. Experiments are conducted to evaluate the efficiency of the proposed method and system, including comparisons with previous work. The results indicate that our method can achieve precise time synchronization and be successfully implemented in multi-sensor systems.

Although LoRa (Long Range) wireless communication technology is most commonly used in the form of LoRaWAN, a single-hop Low Power Wide Area Network (LPWAN) protocol, recent advancements have seen the emergence of multi-hop and mesh LoRa networks. Among these, research on linear LoRa mesh networks for subterranean environments, where traditional LoRaWAN networks face challenges due to limited telecommunication infrastructure, insufficient range, and environmental constraints, is notable. In a linear LoRa mesh network, LoRa repeaters are arranged in a line to provide extended coverage in lengthy environments such as underground mines, pipelines, and tunnels. In this paper, we present a comprehensive analysis of the scalability of these networks, based on a series of simulation experiments conducted using LoRaMeshSim, the first-ever LoRa mesh network simulator, developed by us. We propose optimal selections for LoRa configurations and network properties, including the optimal repeater density, the optimal waiting period in our presented repeater algorithm featuring carrier sensing, and a strategic method of utilizing two frequency channels to enhance network performance. Employing our proposed optimizations, we demonstrate how linear LoRa mesh networks can effectively scale up to support numerous repeaters, end devices, and high-traffic loads.

Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detection, LLE is primarily designed for human vision rather than machine vision and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in the dark that circumvents the need for LLE. We introduce a constrained learning module as an auxiliary mechanism during the training stage of the text detector. This module is designed to guide the text detector in preserving textual spatial features amidst feature map resizing, thus minimizing the loss of spatial information in texts under low-light visual degradations. Specifically, we incorporate spatial reconstruction and spatial semantic constraints within this module to ensure the text detector acquires essential positional and contextual range knowledge. Our approach enhances the original text detector's ability to identify text's local topological features using a dynamic snake feature pyramid network and adopts a bottom-up contour shaping strategy with a novel rectangular accumulation technique for accurate delineation of streamlined text features. In addition, we present a comprehensive low-light dataset for arbitrary-shaped text, encompassing diverse scenes and languages. Notably, our method achieves state-of-the-art results on this low-light dataset and exhibits comparable performance on standard normal light datasets. The code and dataset will be released.

Gas sensing plays a key role in detecting explosive/toxic gases and monitoring environmental pollution. Existing approaches usually require expensive hardware or high maintenance cost, and are thus ill-suited for large-scale long-term deployment. In this paper, we propose Gastag, a gas sensing paradigm based on passive tags. The heart of Gastag design is embedding a small piece of gas-sensitive material to a cheap RFID tag. When gas concentration varies, the conductivity of gas-sensitive materials changes, impacting the impedance of the tag and accordingly the received signal. To increase the sensing sensitivity and gas concentration range capable of sensing, we carefully select multiple materials and synthesize a new material that exhibits high sensitivity and high surface-to-weight ratio. To enable a long working range, we redesigned the tag antenna and carefully determined the location to place the gas-sensitive material in order to achieve impedance matching. Comprehensive experiments demonstrate the effectiveness of the proposed system. Gastag can achieve a median error of 6.7 ppm for CH4 concentration measurements, 12.6 ppm for CO2 concentration measurements, and 3 ppm for CO concentration measurements, outperforming a lot of commodity gas sensors on the market. The working range is successfully increased to 8.5 m, enabling the coverage of many tags with a single reader, laying the foundation for large-scale deployment.

Current studies often decompose multiview registration into several individual tasks, ignoring the correlation between each stage and making certain assumptions on noise distribution without knowledge from previous stages. These issues bring difficulties in generalization to real cases. In this paper, we propose an end-to-end feature-based multiview registration model that takes a set of raw 3D point cloud fragments as input and outputs the global transformation. Unlike previous works, our method allows the exchange of information between stages. We firstly estimate pairwise registration by a attention-based model to assist feature learning. In the next stage, we utilize iteratively reweighted least squares (IRLS) algorithm to refine and obtain the global transformation. In each iteration, instead of making assumptions on noises, we directly construct a model to infer the outliers from pairwise registration so that such an inference can help synchronization produce more reliable results. To follow the process in IRLS algorithm, we propose a simple yet effective refinement module to boost feature-based pairwise estimations in an iterative manner, which can be seamlessly integrated into the IRLS procedure. Extensive experiments conducted on benchmark datasets show that the results of our proposed method outperformed existing methods.

Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors. An individual’s inherent uncertainty, e.g., change of destination, can be masked by complex patterns resulting from the movements of interacting pedestrians. However, latent variable-based generative models often entangle such uncertainty with complexity, leading to limited either latent expressivity or predictive diversity. In this work, we propose to separately model these two factors by implicitly deriving a flexible latent representation to capture intricate pedestrian movements, while integrating predictive uncertainty of individuals with explicit bivariate Gaussian mixture densities over their future locations. More specifically, we present a model-agnostic uncertainty-aware pedestrian trajectory prediction framework, parameterizing sufficient statistics for the mixture of Gaussians that jointly comprise the multi-modal trajectories. We further estimate these parameters of interest by approximating a denoising process that progressively recovers pedestrian movements from noise. Unlike previous studies, we translate the predictive stochasticity to explicit distributions, allowing it to readily generate plausible future trajectories indicating individuals’ self-uncertainty. Moreover, our framework is compatible with different neural net architectures. We empirically show the performance gains over state-of-the-art even with lighter backbones, across most scenes on two public benchmarks.

Trajectory prediction is fundamental to various intelligent technologies, such as autonomous driving and robotics. The motion prediction of pedestrians and vehicles helps emergency braking, reduces collisions, and improves traffic safety. Current trajectory prediction research faces problems of complex social interactions, high dynamics and multi-modality. Especially, it still has limitations in long-time prediction. We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction. We combine Graph Convolutional Networks and Transformer Networks by generating stable resolution pseudo-images from Spatio-temporal graphs through a designed stacking and interception method. Furthermore, we design the attention-aware module to handle social interaction information in scenarios involving mixed pedestrian-vehicle traffic. Thus, we maintain the advantages of the Graph and Transformer, i.e., the ability to aggregate information over an arbitrary number of neighbors and the ability to perform complex time-dependent data processing. We conduct experiments on datasets involving pedestrian, vehicle, and mixed trajectories, respectively. Our results demonstrate that our model minimizes displacement errors across various metrics and significantly reduces the likelihood of collisions. It is worth noting that our model effectively reduces the final displacement error, illustrating the ability of our model to predict for a long time.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.

Internet of Things (IoT) technology has been widely used for real-time monitoring of the environment, safety and production in underground mines. This paper presents the basic structure of a Mine Internet of Things (MIoT) system based on a widely used three-layer IoT architecture, classifies types of sensors commonly used in underground mines by specific application, and introduces available wired and wireless communication technologies and network topologies that can be applied in underground mines. This paper provides a comprehensive review of recent developments in IoT applications in underground mines to monitor various environmental parameters, including mine gas and dust concentrations, temperature, humidity and airflow, groundwater, ground support and seismic activity. MIoT applications for fire and hazard detection, personnel and equipment positioning, and production safety management have also been investigated. This paper highlights key challenges for the broad application of IoT technology in underground mines such as operation disruption, additional investment, limited battery life, poor quality of underground communication, and difficulty in data management. Further research on novel advanced techniques, such as self-powered sensors, MIoT standardization and underground wireless communication technologies, is essential to improve the applicability and effectiveness of IoT applications in underground mines.

Abstract Given a user dataset $$\varvec{U}$$ U and an object dataset $$\varvec{I}$$ I , a kNN join query in high-dimensional space returns the $$\varvec{k}$$ k nearest neighbors of each object in dataset $$\varvec{U}$$ U from the object dataset $$\varvec{I}$$ I . The kNN join is a basic and necessary operation in many applications, such as databases, data mining, computer vision, multi-media, machine learning, recommendation systems, and many more. In the real world, datasets frequently update dynamically as objects are added or removed. In this paper, we propose novel methods of continuous kNN join over dynamic high-dimensional data. We firstly propose the HDR $$^+$$ + Tree, which supports more efficient insertion, deletion, and batch update. Further observed that the existing methods rely on globally correlated datasets for effective dimensionality reduction, we then propose the HDR Forest. It clusters the dataset and constructs multiple HDR Trees to capture local correlations among the data. As a result, our HDR Forest is able to process non-globally correlated datasets efficiently. Two novel optimisations are applied to the proposed HDR Forest, including the precomputation of the PCA states of data items and pruning-based kNN recomputation during item deletion. For the completeness of the work, we also present the proof of computing distances in reduced dimensions of PCA in HDR Tree. Extensive experiments on real-world datasets show that the proposed methods and optimisations outperform the baseline algorithms of naive RkNN join and HDR Tree.

Human activity recognition has become an important challenge yet to resolve while also having promising benefits in various applications for years. Existing approaches have made great progress by applying deep-learning and attention-based methods. However, the deep learning-based approaches may not fully exploit the features to resolve multimodal human activity recognition tasks. Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. We first design a unified representation learning layer to encode the multimodal data, which preprocesses the data in a generalized and efficient way. Then we develop a multimodal spatial-temporal transformer module that applies the attention mechanism to extract the salient spatial-temporal features. Finally, we use a multi-agent training module to collaboratively select the informative modalities and predict the activity labels. We have extensively conducted experiments to evaluate MATN's performance on two public multimodal human activity recognition datasets. The results show that our model has achieved competitive performance compared to the state-of-the-art approaches, which also demonstrates scalability, effectiveness, and robustness.

Pedestrian trajectory prediction is essential for many modern applications, such as abnormal motion analysis and collision avoidance for improved traffic safety. Previous studies still face challenges in embracing high social interaction, dynamics, and multi-modality for achieving high accuracy with long-time predictions. We propose Social Graph Transformer Networks for multi-modal prediction of pedestrian trajectories, where we combine Graph Convolutional Network and Transformer Network by generating stable resolution pseudo-images from Spatio-temporal graphs through a designed stacking and interception method. Specifically, we adopt adjacency matrices to obtain Spatio-temporal features and Transformer for long-time trajectory predictions. As such, we retrain the advantages of both, i.e., the ability to aggregate information over an arbitrary number of neighbors and to conduct complex time-dependent data processing. Our experimental results show that our model reduces the final displacement error and achieves state-of-the-art in multiple metrics. The module's effectiveness is demonstrated through ablation experiments.

Abstract The working face is the most dangerous place of coal mines, and it is difficult (even impossible) to replenish energy by replacing batteries of sensor nodes in the working faces. This paper presents a wireless charging method for this scenario that is composed of two sub methods called mining charging and maintaining charging, respectively. Mining charging provides opportunistic charging services during the process of coal cutting through two airborne Mobile Chargers (MCs) installed on the two ends of the shearer and two portable MCs carried by shearer drivers. Maintaining charging provides opportunistic charging services for nodes in the charging radius when scraper conveyor repairmen and hydraulic support repairmen check or repair equipment, with each repairman carrying one portable MC. Simulation results show that both mining charging and maintaining charging can give energy replenishment for nodes in coal faces. When charging power of MCs is greater than or equal to 5.2 W, the first row of nodes can work sustainably. If the energy requirements of the second row of nodes are also met, the charging power of MCs cannot be less than 12 W.

The feature analysis of point clouds, a popular representation of three-dimensional (3D) objects, is rising as a hot research topic nowadays. Point cloud data bear a sparse and unordered nature, making many commonly used feature extraction methods, for example, Convolutional Neural Networks (CNNs) inapplicable, while previous models suitable for the task are usually complex. We aim to reduce model complexity by reducing the number of parameters while achieving better (or at least comparable) performance. We propose an Interpolation Graph Convolutional Network (IGCN) for extracting features of point clouds. IGCN uses the point cloud graph structure and a specially designed Interpolation Convolution Kernel to mimic the operations of CNN for feature extraction. On the basis of weight postfusion and multilevel-resolution aggregation, IGCN not only reduces the cost of calculating the interpolation operation but also improves the model's performance. We validate the performance of IGCN on both point cloud classification and segmentation tasks and explore the contribution of each module of our model through ablation experiments. Furthermore, we embed the IGCN point cloud feature extraction module as a plug-and-play module into other frameworks and perform point cloud registration experiments.

Abstract It is of great importance to power the nodes of the rechargeable wireless sensor network to detect events continuously in the area of interest. This paper proposes a joint data transmission and on‐demand charging algorithm based on the space correlation. The new algorithm optimises the event detection, data forwarding and node charging jointly to improve the charging efficiency. First, the active nodes participating in the event detection are selected using an improved iterative node selection method to reduce the number of nodes working concurrently. Then, the greedy data transmission scheme based on grid partition is proposed to transmit the observed data to the sink node. Finally, the nodes in the networks are charged using the on‐demand charging method based on grid partition, which greatly decreases the charging frequency and energy loss of the mobile charger. The simulation results demonstrate that the proposed method has superior performance in the distance travelled by the mobile charger, the energy utilisation, the average energy consumption of the mobile charger and the node charging latency.

In this paper, we present an improved belief propagation list (BPL) decoding algorithm for polar codes. Rather than getting L factor graphs (FGs) at random and cyclic shift permutation, we use the upper bounds on the block error propability of polar codes with different FGs as the metric to choose the best L FGs. By observing the bounds of different FGs, we propose a heuristic method to reduce search complexity. Simulation results show that there is only a gap of 0.2 dB between the frame error rate (FER) performance of the improved BPL decoder using RM16-GA construction and that of length-1024 5G polar code decoded by SCL with the same list size of 32 at FER =10-4. Moreover, with the proposed FG selection method, BPL decoding can reduce clock cycles by 97.74% compared with the SCL decoding.

Most nodes of Mine Internet of Things (Mine IoT) are powered by batteries, and wireless charging using mobile chargers (MCs) is an effective way to make nodes work sustainably. A novel hybrid charging method combining the controlled and opportunistic MCs (C&O charging) is proposed in this study. Workers (such as the repairmen and gas inspectors) carrying portable chargers are proposed to be opportunistic MCs to provide an incidental charging service for the surrounding rechargeable Mine IoT nodes while doing its own work to reduce the payload of controlled MCs. The hybrid charging model based on the incidental charging ability of the opportunistic MC is constructed and the scheduling strategy of the controlled MC and the queueing management scheme of the charging request are also proposed. The simulation results indicate that the power demands of the majority of the nodes in the maintenance areas can be met or partially met by opportunistic MCs and the charging time of C&O charging is greatly decreased compared to that of only using controlled MCs.

The nodes of the mine Internet of things (MIoT) are powered by batteries, for which the wireless charging is essential for their continuous and stable operation. This study proposed a method of directional mobile charging for the MIoT based on smart antenna, in which the mobile chargers charge the nodes need power not only when stationary, but also when moving. The theoretical calculation method of the charged energy is studied and established its approximate calculation algorithm, which can greatly reduce the computational complexity, based on the discretised model of effective charging distance of smart antenna. Then, the method for estimating the residual energy of the nodes was designed, and the upper bound of the transmitting power of the mobile charger was determined. The results of the simulation experiments indicated that this method has high charging efficiency and can meet the power demand of MIoT nodes.

It is well known that atmospheric pressure decreases when altitude increases. Models have been created to relate altitude or height to pressure. A barometer can measure the air pressure and then the altitude can be calculated. Before the era of GNSS, barometers were widely used to determine heights outdoors. The invention of GNSS was a revolution in positioning and navigation. However, it does not work in an indoor environment. Alternative technologies have been developed such as Wi-Fi fingerprinting mainly for 2D positioning and navigation. In some of the applications, 3D or 2.5D (the level of the building) is required. Using barometers is a possible solution and some new mobile phones have a built in pressure sensor. But there are many issues that should be considered. Is height determined from barometric pressure accurate enough? Is there a latency problem? Does the air conditioning in an almost sealed building significantly affect height readings? This paper discusses the necessary considerations to use barometers for indoor applications based on experiments. Possible solutions are suggested.

There are over 1.2 million Australians registered as having vision impairment. In most cases, vision impairment severely affects the mobility and orientation of the person, resulting in loss of independence and feelings of isolation. GPS technology and its applications have now become omnipresent and are used daily to improve and facilitate the lives of many. Although a number of products specifically designed for the Blind and Vision Impaired (BVI) and relying on GPS technology have been launched, this domain is still a niche and ongoing R&D is needed to bring all the benefits of GPS in terms of information and mobility to the BVI. The limitations of GPS indoors and in urban canyons have led to the development of new systems and signals that bridge the gap and provide positioning in those environments. Although still in their infancy, there is no doubt indoor positioning technologies will one day become as pervasive as GPS. It is therefore important to design those technologies with the BVI in mind, to make them accessible from scratch. This paper will present an indoor positioning system that has been designed in that way, examining the requirements of the BVI in terms of accuracy, reliability and interface design. The system runs locally on a mid-range smartphone and relies at its core on a Kalman filter that fuses the information of all the sensors available on the phone (Wi-Fi chipset, accelerometers and magnetic field sensor). Each part of the system is tested separately as well as the final solution quality.

Location fingerprinting in WiFi positioning has been widely used in indoor environments. The key issue of the fingerprinting technology is the fingerprint database. The disadvantages of this technology are the database generation and maintenance requirements. The conventional method to create the database is that people carry out the survey manually (that is what the commercial products are doing). When the environment changes significantly (such as after a building renovation, or moving of furniture), the database has to be rebuilt. This paper proposes a new method to build and maintain the database in an efficient manner. This method only requires persons to carry a specific device which consists of a Radio Frequency Identification (RFID) reader and WiFi scanner to log the coordinates and the WiFi signal strengths. The coordinates are provided by some pre-deployed medium range (1–2 meters) RFID tags in the building with a location technique based on ‘cell ID’. As the persons conducting the survey are moving around the area of interest for purposes other than the fingerprint survey (such as a security guard who regularly patrols the whole building anyway), so the fingerprint database can be generated “automatically”. Also, the database can be refined as the data are being accumulated. When the environment changes, it can be detected by the self-refining database. A preliminary test was carried out for the proposed method. The results show that it works well.

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

Despite over a decade of intensive research and development, the problem of delivering an effective indoor navigation system to the blind and vision impaired (BVI) remains largely unsolved. In an attempt to strengthen and improve future research efforts, we define a set of criteria for evaluating the success of a potential navigation device. In order to give complete coverage, the Requirements Analysis has been broken down into the subcategories of positioning accuracy, robustness, seamlessness of integration with varying environments and the nature of information that is outputted to a BVI user. We then apply this framework to a number of existing navigation solutions for the BVI, drawing upon the notable achievements that have been made thus far and also the crucial issues that remain unresolved or are yet to receive attention. It was found that these key issues, which existing designs fail to overcome, can be attributed to the need for a new focus and user centred design attitude - one which incorporates universal design, recognises the uniqueness of its audience and understands the challenges associated with the systems/devices intended environment.

It is well known that a global positioning system (GPS) receiver needs to ‘see’ at least four satellites to provide a three-dimensional fix solution. But in difficult environments such as an urban canyon, the number of ‘visible’ satellites is often not enough. Wireless fidelity (WiFi) signals have been utilised for positioning mainly based on the fingerprinting technology. However, the accuracy of WiFi positioning outdoors is from several tens of metres to more than one hundred metres. This study proposes a new methodology to integrate WiFi positioning technology and GPS to improve positioning accuracy in urban canyons. When only two GPS satellites are visible, the pseudorange observations can be used to generate a time difference of arrival (TDOA) measurement. The TDOA generates a hyperboloid surface which can be intersected with the surface of the Earth and shows the possible location of the user on that line of position. Integrating this method with the WiFi fingerprinting technology can increase the positioning performance significantly. The test results show that the positioning accuracy can be improved by more than 50% if the new method can be applied.

The purpose of this paper is to describe a campus-wide indoor and outdoor positioning system developed at the School of Surveying and Spatial Information Systems at the University of New South Wales, Sydney, Australia. The system uses a Wi-Fi positioning technique known as `fingerprinting' to locate users indoors, and GPS to locate users outdoors. Fingerprinting first requires the building up of a database of signal strengths from different Wi-Fi access points taken at different points across the area of interest. Then, the user scans the signal strengths in the wireless network and sends the data to the database, which will find the closest match, and return the likeliest location of the user. Our work investigates different approaches to database generation and fingerprint matching, and their impact on system performance. We find that reducing the time for the survey phase by half does not impact significantly on the final accuracy of the system. The system is also tested outdoors to investigate whether Wi-Fi can challenge GPS for outdoor positioning, and we find that Wi-Fi can match, and sometimes outperform GPS in terms of accuracy.

Millions of people travel between countries and regions on a daily basis being exposed to unfamiliar environments. Knowing where you are and how to get to places within a restricted time can make people's lives easier. Today, WiFi access points (APs) are common everywhere, especially on university campuses, in hotels, hospitals, shopping centres, and city central business districts. This project, dubbed WiPos (WiFi-Positioning), had the objective to develop a positioning system using WiFi APs deployed across a university to locate one's position in a building under conditions where GPS could not be used. When relatively accurate user position is available, location based services (LBS) can be provided to users, including timetable of a lecture room, location of the nearest vending machine, and so on. This project involved developing a server and a client (running on the Android platform) to handle positioning and LBS transactions. Testing has shown that the university's WiFi network is sufficient to provide `room to room' accuracy.

Fingerprinting is a technique that records vectors of received power from several transmitters, and later matches these to a new measurement to position the new user. This paper examines data from locations at very short ranges from each other in order to observe the nature of the relationship between short-range fingerprints. We find that even at short distances, variation due to fading dominates and a suitable signal model has yet to be produced. We also conclude that fingerprints should be averaged over a range near the point of interest.

802.11 positioning systems are established as a low-cost solution to positioning within context aware computing. Prior research has mostly focused on either indoor positioning within commercial environments, where access points are prevalent, or outdoors positioning over large areas, using discovered networks to augment GPS. In this paper we test 802.11 positioning in a medium sized domestic house with only a small number of access points, examining its suitability in a domestic context aware computing system. We use a signal strength fingerprint map approach, in which empirical signal strength data is gathered over the area prior to use. Estimation then involves comparing the online input to the map, and selecting the best position, and direction, based on similarity. We compare different strategies for gathering signal strength, and implementations of the Nearest Neighbor and Bayesian methods. Our results demonstrate that with good placement, only two access points are sufficient to estimate position with an error of less than 4 meters 90% of the time.