This article proposes an IoT system that ensures the integrity and monitoring of railway circulating material, where the network is self reconfigurable and its devices are not physically connected or ...used with redundancy. The system is based on a wireless sensor network (WSN) that facilitates communication among devices and transmits data from train sensors to a gateway while considering power consumption. Bluetooth low energy (BLE) mesh features enable the relay of data between devices and the network reconfiguration. If network reconnection is not possible, the device can use LoRa communication to report its position directly to the control center. Different disposition conditions were applied to the WSN, and the results indicate that this system is viable in terms of network configuration and self regeneration. Furthermore, it has a current consumption of less than 1 mA during its low power mode,with the integrity of a 50 node model train can be reported in an average time of 2.63 s.
In late 2019, a new genre of coronavirus (COVID-19) was first identified in humans in Wuhan, China. In addition to this, COVID-19 spreads through droplets, so quarantine is necessary to halt the ...spread and to recover physically. This modern urgency creates a critical challenge for the latest technologies to detect and monitor potential patients of this new disease. In this vein, the Internet of Things (IoT) contributes to solving such problems. This paper proposed a wearable device that utilizes real-time monitoring to detect body temperature and ambient conditions. Moreover, the system automatically alerts the concerned person using this device. The alert is transmitted when the body exceeds the allowed temperature threshold. To achieve this, we developed an algorithm that detects physical exercise named “Continuous Displacement Algorithm” based on an accelerometer to see whether a potential temperature rise can be attributed to physical activity. The people responsible for the person in quarantine can then connect via nRF Connect or a similar central application to acquire an accurate picture of the person’s condition. This experiment included an Arduino Nano BLE 33 Sense which contains several other sensors like a 9-axis IMU, several types of temperature, and ambient and other sensors equipped. This device successfully managed to measure wrist temperature at all states, ranging from 32 °C initially to 39 °C, providing better battery autonomy than other similar devices, lasting over 12 h, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption. Furthermore, a 1D Convolutional Neural Network (CNN) was employed to classify whether the user is feverish while considering the physical activity status. The results obtained from the 1D CNN illustrated the manner in which it can be leveraged to acquire insight regarding the health of the users in the setting of the COVID-19 pandemic.
We investigate Apple’s Bluetooth Low Energy (BLE) Continuity protocol, designed to support interoperability and communication between iOS and macOS devices, and show that the price for this seamless ...experience is leakage of identifying information and behavioral data to passive adversaries. First, we reverse engineer numerous Continuity protocol message types and identify data fields that are transmitted unencrypted. We show that Continuity messages are broadcast over BLE in response to actions such as locking and unlocking a device’s screen, copying and pasting information, making and accepting phone calls, and tapping the screen while it is unlocked. Laboratory experiments reveal a significant flaw in the most recent versions of macOS that defeats BLE Media Access Control (MAC) address randomization entirely by causing the public MAC address to be broadcast. We demonstrate that the format and content of Continuity messages can be used to fingerprint the type and Operating System (OS) version of a device, as well as behaviorally profile users. Finally, we show that predictable sequence numbers in these frames can allow an adversary to track Apple devices across space and time, defeating existing anti-tracking techniques such as MAC address randomization.
Bluetooth Mesh (BM) is one of the promising networking technologies for Internet-of-Things (IoT) networks released by Bluetooth SIG in 2017. BM provides a protocol for multi-hop scalable networking ...of IoT devices over the widely-used Bluetooth Low Energy (BLE) technology. However, the capabilities and limits of this technology are not fully studied to determine the IoT applications for which this technology can be used. One of the barriers towards this is the lack of a suitable BM network simulator for performance investigations under various conditions and settings. This paper presents a full-fledged open-source event-driven simulator (BMSim) for the performance evaluation of BM networks. The accuracy of the developed simulator is verified by real experiments. Also, BMSim is used to perform a comprehensive investigation of the performance of the BM protocol in various network conditions and configuration settings. The impact of several configuration parameters on the BM network performance is studied. Since the simulator is capable of simulating dynamic networks and run-time configurations, a BM network with node mobility is also investigated. The results reveal the importance and necessity of proper BM parameter configuration mechanisms to achieve the required quality-of-service and network efficiency.
Magnetic-assisted indoor localization has attracted significant attention because of its commercial and social values. However, it is challenging to construct a robust and accurate system due to the ...severe feature ambiguity caused by different users, mobiles, attitudes, and moving speeds. In order to cope with this issue, we first propose to fuse magnetic with the multi-modal features, including Bluetooth low energy (BLE), and context information of continuous predictions, to improve the feature discrimination with more valuable localization clues. Then, we extract more orientation-insensitive magnetic features and remove the Direct Current (DC) component of the sequence can reduce the feature ambiguity caused by different holding attitudes, devices, and users. After that, we propose an online data augmentation algorithm to automatically generate a sufficient amount of various-speed sequences based on only one dense sampling benchmark sequence, thus reducing the influence of multi-scale sequences caused by different moving speeds. Finally, we propose a multi-branch and attention mechanism-based end-to-end localization model to extract and efficiently fuse the significant features of the multi-modal data for accurate localization. We evaluate the performance of the proposed localization system (DamLoc) in a typical indoor environment based on extensive experiments. Evaluation results showcase that DamLoc more robust for diverse heterogeneous factors, and can support about 63% improvement compared to state-of-the-art methods. It is worth pointing out that the BLE in our work can be replaced with other signals, such as Wireless Fidelity (Wi-Fi), which is more general than other fusion-based localization.
•This paper proposes a novel multi-modal information fusion-based localization model to improve the accuracy.•This paper proposes an online data augmentation-based feature extraction method to reduce the feature ambiguity caused by different moving speeds.•This paper proposes an indoor localization system with a low cost of data collection, more stability for different attitudes and moving speeds, and more accuracy with low computational overload.
Crowd-centric sensing using smart phones enables a diverse range of applications evolving from large outdoor environments (e.g., smart cities) to small-scale indoor environments (e.g., smart homes, ...smart buildings). Tracking users’ patterns in indoor environments is a valuable and challenging aspect that is not yet fully addressed. Active indoor localization systems are generally energy-inefficient and cannot be applied to crowd monitoring applications. This paper focuses on the development of a passive and energy-efficient indoor tracking and pattern recognition technique on top of a managed Bluetooth Low-Energy (BLE) network. Particularly, our system model is based on a passive monitoring of a network of BLE tags, which continuously broadcast their unique identifiers, and the current timestamp. Multiple protocols were implemented to extract moving objects’ locations in indoors. The trajectory building process consists of different phases: 1) data sampling, 2) outlier detection and removal, 3) location estimation with a weighted centroid approach, 4) spatio-temporal map matching, and finally 5) trajectory smoothing. A series of experiments was conducted to demonstrate the efficiency and accuracy of the proposed approach, with respect to active triggering approaches and BLE-based localization systems.
Crowdsourced indoor localization methods have grasped much attention in recent years as a method of reducing the cost of constructing the fingerprint database. In a crowdsourcing environment, ...however, the localization system is vulnerable to malicious attacks, which possibly lead to serious localization errors. In this paper, we conclude the potential attacks during fingerprint database updates and online inference phases and propose a secure indoor crowdsourced localization system, BERT-ADLOC, based on BLE fingerprints. Our system consists of two main parts: adversarial sample discriminator BERT-AD and indoor localization model BERT-LOC. Our proposed BERT-AD recognizes fake fingerprints during the database update phase, while BERT-LOC defends against attacks online, in which valid beacons are moved or malicious beacons are deployed. A tailored BERT model is introduced to extract deep hidden features through the self-attention mechanism. Our experiments show that BERT-ADLOC achieves a good localization performance against adversaries both in the fingerprint database update phase and online inference phase.
Federated learning (FL) is a collaborative learning paradigm where multiple clients are used to build the model without sharing data and preserving privacy. An FL-based linear regression model is ...designed to predict the length of stay for patients at hospitals using the low-power Arduino Nano 33 BLE Sense microcontroller unit (MCU). FL uses a distributed learning technique that allows model building from decentralized data sources. The Arduino Nano 33 BLE Sense is a compact and energy-efficient MCU providing an ideal platform for implementing FL in resource-constrained environments. FL algorithms aggregate model parameters from multiple Arduino clients and collectively train and build a predictive model to estimate the length of stay at the hospital by patients. Experiments were conducted to understand the performance of FL on clients with data of equal and varying sizes and heterogeneous data from multiple sources. The performance of the algorithm is evaluated based on Mean Absolute Error (MAE), Percentage Decrease in Training error (PDTE), and Percentage Difference with Optimal Testing (PDOT) value. Experimental results show that the number of local epochs and FL rounds affects the convergence of clients to the optimal value. The experimental results demonstrate the applicability of FL on low-power MCUs, preserving privacy which is a core requirement for healthcare solutions.
This research work uses a simplified approach to combine location information from a beacon's propagation signal interaction with a mobile device sensor (accelerometer and gyroscope) with local ...building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user's orientation inside unknown buildings and the data stored from different users can provide useful information about users' movements inside a public building. Beacons installed on the building at specific pre-defined positions emit signals that give a geographic position with an associated imprecision, related with Bluetooth's range. This uncertainty is handled by building layout and users' movement in a developed system that maps users' position, gives guidance, and stores user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user's smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.
We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable ...or unwilling to carry a smartphone, or any other "wearable" device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors' coverage of the monitored space and the quality of the location estimates.
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