This paper presents the development and characterization of a system able to estimate the 2D relative position of nodes in a wireless network, based on the measurement of the distances between the ...nodes. To this end, this paper approaches the problem from two perspectives: implementation and theory. Combining a classical non-linear least square approach and a new convention on the arrangement of the nodes, a 2D relative positioning system was developed. Moreover, a numerical and theoretical study of the proposed system using the Cramér Rao Lower Bound (CRLB), proves that the estimator is efficient. The system uses Ultra Wide Band (UWB) ranging technology and the Bluetooth Low Energy protocol to acquire data. A Robot Operating System (ROS) library able to acquire BLE data from UWB devices was also developed. This library is open-source and available on github. The system was tested both in dynamic and static scenarios demonstrating the capability of estimating the relative position of a network comprised of 4 nodes with an update rate of 10 updates per second. The accuracy is in the order of 3 cm and 8 cm, in static and dynamic conditions respectively. A potential application of the developed system is for robot localization. In fact, autonomous robots, over the last years, have reached remarkable capabilities of localization both in indoor and outdoor scenarios using mainly GPS and vision. The developed relative localization system could address the limitations of these technologies and simplify robot cooperative tasks. The provided results and analysis show the feasibility of applying the proposed system for multi-robot cooperative localization and formation control scenarios.
A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and ...physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people’s quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context’s needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions.
•Unobtrusive systems are useful for monitoring elderly behaviour and detect changes.•Wearable devices for BLE indoor positioning and body motility are energy greedy.•Digest mode for data sending to the Shared Repository is the most preferable way.•Linked Open Data to share results is fundamental in a Smart City perspective.•Frailty/MCI risk detection based on high-level geriatric (sub-)factors is effective.
Bluetooth Low-Energy (BLE) beacons-based indoor positioning is a promising method for indoor positioning, especially in applications of position-based services (PbS). It has low deployment cost and ...it is suitable for a wide range of mobile devices. Existing BLE beacon-based positioning methods can be categorized as range-based methods and fingerprinting-based methods. For range-based methods, the positions of the beacons should be known before positioning. For fingerprinting-based methods, a pre-requisite is the reference fingerprinting map (RFM). Many existing methods focus on how to perform the positioning assuming the beacon positions or RFM are known. However, in practical applications, determining the beacon positions or RFM in the indoor environment is normally a difficult task. This paper proposed an efficient and graph optimization-based way for estimating the beacon positions and the RFM, which combines the range-based method and the fingerprinting-based method. The method exists without need for any dedicated surveying instruments. A user equipped with a BLE-enabled mobile device walks in the region collecting inertial readings and BLE received signal strength indication (RSSI) readings. The inertial measurements are processed through the pedestrian dead reckoning (PDR) method to generate the constraints at adjacent poses. In addition, the BLE fingerprints are adopted to generate constraints between poses (with similar fingerprints) and the RSSIs are adopted to generate distance constraints between the poses and the beacon positions (according to a pre-defined path-loss model). The constraints are then adopted to form a cost function with a least square structure. By minimizing the cost function, the optimal user poses at different times and the beacon positions are estimated. In addition, the RFM can be generated through the pose estimations. Experiments are carried out, which validates that the proposed method for estimating the pre-requisites (including beacon positions and the RFM). These estimated pre-requisites are of sufficient quality for both range-based and fingerprinting-based positioning.
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•End-to-end IoT communication infrastructure for Reverse Supply Chain Management.•RFID, BLE and MQTT standards cooperation for inventory-management.•LoRaWAN-based context information ...network for facility-monitoring.•Deployment of a heterogeneous IoT network solution for WEEE management.•A case study based on the recovery of computer components was deployed and tested.
The recent increase in the number of products returned from customers to retailers, supported by the adoption of environment-friendly policies, has led to a growing need to manage backward materials and information flows in the supply chain (SC) domain. Although numerous authors are contributing towards circular economy (CE) with end-of-life (EoL) approaches minimizing the negative impact of Waste Electric and Electronic Equipment (WEEE), the information infrastructure behind SC calls for novel approaches based on Information and Communication Technologies (ICT). In fact, this is one of the major challenges for the so-called Industry 4.0, where wireless technologies governed by the Internet of Things (IoT) are expected to transform the industry as currently conceived. The present work proposes an end-to-end solution for Reverse Supply Chain Management (R-SCM) based on cooperation between different IoT communication standards, enabling cloud-based inventory monitoring of WEEE through embedded sensors. A case study was deployed using IoT devices and sensors, carrying out a set of experimental tests focused on wireless communications to evaluate its performance. The network configuration adopted overcomes the near real-time challenge and provides sufficient coverage to interconnect industrial areas such as warehouses or shop floors. The results point to different communication bottlenecks that need to be addressed in order to enhance the reliability of large-scale Industrial IoT (IIoT) networks.
As the elderly population continues to grow, timely health monitoring and emergency response for the elderly become particularly crucial. However, traditional wireless sensor networks face challenges ...such as transmission delays, high energy consumption, network congestion, and packet collisions during emergencies, which impact the health and safety of the elderly. To address these issues, this paper proposes a novel multi-layer topology control mechanism, called Improving Emergent Events Transmission (IEET), which integrates tree and mesh topologies while applying channel selection and subrated connection strategies. The tree topology effectively constructs a distinct path for emergent transmission, filtering out channels susceptible to contentions and collisions. It employs a subrated connection scheme to adjust the connection interval for transmitting emergency messages, thus reducing transmission delay and energy consumption. Additionally, the mesh topology addresses traffic congestion near the gateway, reducing packet collisions and improving the reliability of packet transmission. As a result, emergent information can be swiftly transmitted to the gateway, mitigating the impact of emergencies on the elderly. Simulation results demonstrate that the proposed IEET outperforms existing mechanisms in terms of average packet latency, network lifetime, and energy consumption for sensors in Bluetooth Low Energy (BLE) networks.
Bluetooth Low Energy (BLE) Mesh Networks enable flexible and reliable communications for low-power Internet of Things (IoT) devices. Most BLE-based mesh protocols are implemented as overlays on top ...of the standard Bluetooth star topologies while using piconets and scatternets. Nonetheless, mesh topology support has increased the vulnerability of BLE to security threats, since a larger number of devices can participate in a BLE Mesh network. To address these concerns, BLE version 5 enhanced existing BLE security features to deal with various authenticity, integrity, and confidentiality issues. However, there is still a lack of detailed studies related to these new security features. This survey examines the most recent BLE-based mesh network protocols and related security issues. In the first part, the latest BLE-based mesh communication protocols are discussed. The analysis shows that the implementation of BLE pure mesh protocols remains an open research issue. Moreover, there is a lack of auto-configuration mechanisms in order to support bootstrapping of BLE pure mesh networks. In the second part, recent BLE-related security issues and vulnerabilities are highlighted. Strong Intrusion Detection Systems (IDS) are essential for detecting security breaches in order to protect against zero-day exploits. Nonetheless, viable IDS solutions for BLE Mesh networks remain a nascent research area. Consequently, a comparative survey of IDS approaches for related low-power wireless protocols was used to map out potential approaches for enhancing IDS solutions for BLE Mesh networks.
As an emerging technology with exceptional low energy consumption and low-latency data transmissions, Bluetooth Low Energy (BLE) has gained significant momentum in various application domains, such ...as Indoor Positioning, Home Automation, and Wireless Personal Area Network (WPAN) communications. With various novel protocol stack features, BLE is finding use on resource-constrained sensor nodes as well as more powerful gateway devices. Particularly proximity detection using BLE beacons has been a popular usage scenario ever since the release of Bluetooth 4.0, primarily due to the beacons’ energy efficiency and ease of deployment. However, with the rapid rise of the Internet of Things (IoT), BLE is likely to be a significant component in many other applications with widely varying performance and Quality-of-Service (QoS) requirements and there is a need for a consolidated view of the role that BLE will play in applications beyond beaconing. This paper comprehensively surveys state-of-the-art applications built with BLE, obstacles to adoption of BLE in new application areas, and current solutions from academia and industry that further expand the capabilities of BLE.
The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and ...performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., P¯=5.84 mW) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital ...signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users' vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning⁻based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user's sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to ...the museum. In this article, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, a received signal strength-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the cloud, to improve localization, precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.
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