A Conversion System Based on Acoustic Communication for Using Bluetooth Low Energy Sensors Underwater

In this paper, we propose a system for using a commercially available Bluetooth Low Energy (BLE) sensor underwater. Our system comprises a mounting device located close to the BLE sensor and a part located on the water surface. In the water, the mounting device receives the BLE signals from the sensor and converts them into acoustic waves that are emitted from a speaker. The part on the water surface receives those acoustic waves via a microphone and demodulates the data transmitted by the BLE sensor. The proposed system enables existing BLE sensors to be used underwater. Some of the functions of our proposed system are developed and evaluated using commercially available products, and the basic nerformance of the system is confirmed.

With the increasing demand for location-based services, indoor localization technology based on wireless sensor networks (WSNs) has recently received significant attention. Bluetooth low energy (BLE) has become a research hotspot among the numerous WSNs due to its ubiquitous deployment, low-power consumption, and low cost. Many previous BLE-based indoor localization technologies can only use received signal strength indication (RSSI) for localization in the 2-D plane and achieve coarse accuracy. However, high-accuracy location information is required in an increasing number of applications, e.g., elderly fall detection for medical applications, which urgently requires a new BLE-based localization technology in 3-D space. To this end, we propose Spotlight, an angle-based fine-grained indoor localization system in 3-D space using BLE sensors. The critical idea of the Spotlight is to measure the angle-of-arrival (AoA) and elevation-of-angle (EoA) of the signal from the target to the BLE receivers and utilize them to achieve accurate 3-D localization. Specifically, we develop an offset elimination algorithm and data stitching algorithm, utilizing the second-order cone (SoC) programming to obtain accurate angle estimates. Then, the simulated annealing algorithm (SAA) is used to search for the target location in 3-D space. Finally, we develop the prototype system with commercial off-the-shelf (COTS) BLE sensors and conduct extensive experiment measurements in real-world scenarios. The results demonstrate that the proposed system can achieve a median localization error of 36 cm in the 2-D plane and 48 cm in the 3-D space.

Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

Timing points used in running races and other competition events are generally based on radio-frequency identification (RFID) technology. Athletes’ times are calculated via passive RFID tags and reader kits. Specifically, the reader infrastructure needed is complex and requires the deployment of a mat or ramps which hide the receiver antennae under them. Moreover, with the employed tags, it is not possible to transmit additional and dynamic information such as pulse or oximetry monitoring, alarms, etc. In this paper we present a system based on two low complex schemes allowed in Bluetooth Low Energy (BLE): the non-connectable undirected advertisement process and a modified version of scannable undirected advertisement process using the new capabilities present in Bluetooth 5. After fully describing the system architecture, which allows full real-time position monitoring of the runners using mobile phones on the organizer side and BLE sensors on the participants’ side, we derive the mobility patterns of runners and capacity requirements, which are determinant for evaluating the performance of the proposed system. They have been obtained from the analysis of the real data measured in the last Barcelona Marathon. By means of simulations, we demonstrate that, even under disadvantageous conditions (50% error ratio), both schemes perform reliably and are able to detect the 100% of the participants in all the cases. The cell coverage of the system needs to be adjusted when non-connectable process is considered. Nevertheless, through simulation and experimental, we show that the proposed scheme based on the new events available in Bluetooth 5 is clearly the best implementation alternative for all the cases, no matter the coverage area and the runner speed. The proposal widely exceeds the detection requirements of the real scenario, surpassing the measured peaks of 20 sensors per second incoming in the coverage area, moving at speeds that range from 1.5 m/s to 6.25 m/s. The designed real test-bed shows that the scheme is able to detect 72 sensors below 600 ms, fulfilling comfortably the requirements determined for the intended application. The main disadvantage of this system would be that the sensors are active, but we have proved that its consumption can be so low (9.5 µA) that, with a typical button cell, the sensor battery life would be over 10,000 h of use.

Slowing of alpha rhythm in thinner sniffers : H. Miyake, S. Iijima and Y. Hishikawa (Yokosuka and Osaka, Japan)

This paper describes how the location of a test object or person can be determined at room resolution using Bluetooth Low Energy (BLE). Therefore, the test object or person is equipped with a BLE node and approximately central in each room a low-cost and low-power BLE sensor is placed. No stringent installation requirements or battery replacement is needed for these sensors. They send advertising packets to the mobile node, where the received data is processed quickly, based on the signal strength of the received messages, characterized by the Received Signal Strength Indicator (RSSI). No fingerprinting, propagation characteristics or trilateral statistics are needed to determine in which room the test object or person is located by use of the measured RSSI values. In this paper, the localization is done by a BLE development kit of Bluegiga. Measuring in a real-life environment, we can locate at room resolution a device during more than 90% of the time, without the need for a difficult installation procedure or battery replacements on a regular basis.

In this paper, we consider a protocol architecture that enables IPv6 routing protocol for low power and lossy networks (RPL) to run on top of Bluetooth Low Energy (BLE), aiming to provide a BLE-based multi-hop IoT network. In our approach to RPL over BLE, we propose to use both of advertising and data channels of BLE to create synergistic effects between RPL and BLE to jointly achieve high energy efficiency and reliable multi-hop routing. We design an adaptation layer between BLE and RPL (ALBER) which tightly couples RPL and BLE operations together. Specifically, ALBER provides RPL control message broadcast through BLE, RPL routing metric calculation that reflects BLE link quality, and routing table update that incorporates BLE connection management. We implement ALBER in a Linux kernel to realize RPL over BLE and compare its performance with that of RPL over IEEE 802.15.4 on a multi-hop testbed network. The performance results show that our architecture is not only feasible but also provides almost perfect packet delivery performance (↑100%) and reduces dutycycle up to 32% compared to RPL over IEEE 802.15.4 under varying link dynamics. Our research shows that RPL over BLE is a promising approach which can increase the utility and impactof BLE across different application domains.

In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag's location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.

Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

Recently, indoor location-based services (LBS) have become attractive with the rapid development of indoor location estimation and with the spread of smartphones. An estimation technique using Bluetooth low energy (BLE) tags becomes most attractive and promising among other techniques for estimating location because a majority of smartphones supports BLE. The BLE tags transmit advertisement (ADV) packets periodically, which include a unique ID related to each tag. When a smartphone receives the ADV packet, the smartphone sends a query to the server to retrieve the locationbased information for the ID. To provide such a service continuously, operators have to monitor whether BLE tags work well and repair them in the event of a problem. These operations are difficult because they require physical labor that cannot be automated for a large number of BLE tags spread over a wide area. Therefore, we evaluated the operations of BLE tags quantitatively by the metric of operational costs. We established an environment composed of 54 BLE tags in a four-story building and measured the lifetime of the battery, the service availability ratio, and the operational costs. Through the experiments, we obtained various results regarding the battery management of the BLE tags and the effectiveness of redundant deployment.

Introduction:An important and complicated problem related to the multilateration of Wi-Fi or Bluetooth Low Energy signals as well as Wi-Fi fingerprinting is the procedure of infrastructure adjustment which includes Wi-Fi radio map construction and Wi-Fi or Bluetooth Low Energy radio signal path loss model calibration.Purpose:Developing a method for navigation and Wi-Fi radio map construction, which would provide user’s indoor navigation, Bluetooth Low Energy path loss model calibration and Wi-Fi radio map collaborative semi-automatic construction.Results:The paper presents a collaborative semi-automatic Wi-Fi radio map construction method based on the combination of Bluetooth Low Energy multilateration, Wi-Fi fingerprinting, Wi-Fi radio map collaborative semiautomatic construction procedure and semi-automatic Bluetooth Low Energy path loss model calibration. For the semi-automatic calibration procedure of the Bluetooth Low Energy signal propagation model and for the method of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation, a calibration algorithm and an algorithm of collaborative semi-automatic construction of Wi-Fi radio map and indoor navigation were proposed, respectively. A mobile application has been developed which implements the proposed algorithms in order to test them and analyze the results.Practical relevance:The proposed method allows you to avoid time-consuming procedures of constructing a map of Wi-Fi radio signals and semi-automatic calibration of Bluetooth Low Energy signal propagation in the offline phase.

Wireless Mesh Networks (WMN) are becoming inevitable in this world of high technology as it provides low cost access to broadband services. Moreover, the technologists are doing research to make WMN more reliable and secure. Subsequently, among wireless ad-hoc networking technologies, Bluetooth Low Energy (BLE) is gaining high degree of importance among researchers due to its easy availability in the gadgets and low power consumption. BLE started its journey from version 4.0 and announced the latest version 5 with mesh support capability. BLE being a low power and mesh supported technology is nowadays among the hot research topics for the researchers. Many of the researchers are working on BLE mesh technology to make it more efficient and smart. Apart from other variables of efficiency, like all communication networks, mesh network security is also of a great concern. In view of the aforesaid, this paper provides a comprehensive review on several works associated to the security in WMN and BLE mesh networks and the research related to the BLE security protocols. Moreover, after the detailed research on related works, this paper has discussed the pros and cons of the present developed mesh security mechanisms. Also, at the end after extracting the curx from the present research on WMN and BLE mesh security, this research study has devised some solutions as how to mitigate the BLE mesh network security lapses.

The advent of Bluetooth Low Energy (BLE) enabled Beacons is poised to revolutionize the indoor contextual aware services to the users. Due to the lower energy consumption and higher throughput, BLE could therefore be an integral pillar of an Internet of Things (IoT) Location Based Service (LBS). Tracking a user with high accuracy is known as Micro-Location. This is a requirement of many IoT user-centric applications for indoor environments. Although several technologies have been used for tracking purposes, the accuracy has always been a serious issue. At the same time, each vendor would install different technologies. In this work, we propose to use the cutting edge and commercially available Apple's iBeacon protocol and iBeacon BLE sensors for micro-location. We propose to leverage a control theoretic approach, namely particle filtering, in order to increase the tracking accuracy in an indoor environment. We performed extensive experiments and our results show that the proposed beacon based micro-location system can be used to locate a user in an indoor environment with an error as low as 0.27 meters.

Bluetooth Low Energy (BLE) is currently the dominating wireless network solution for eHealth and sports. However, most BLE sensors require dedicated applications with limited development capabilities. This paper presents a method for rapid development of applications in distributed BLE IoT systems for eHealth and sports. The method is implemented as a JavaScript web framework based on HTML5 canvas, WebSocket and Web Bluetooth APIs. This paper demonstrates how the framework can be applied to develop an application for monitoring physical activity and heart rate. The framework enables software and service operators to iteratively create, tune and deploy filter algorithms in distributed BLE IoT systems, without rebooting nodes or restarting programs using dynamic software updating.

In this paper, we present an innovative back-channel wireless communication concept. The proposed back- channel communication enables ultra-low power (ULP) devices that are neither WiFi (IEEE 802.11a/g/n) nor Bluetooth Low Energy (BLE) compliant to receive messages from both WiFi and BLE transmitters. This allows communication between heterogeneous devices beyond the boundary of WiFi and BLE standards without hardware modification on already-deployed infrastructure. Back- channel messages are created in frequency shift keying (FSK) modulation format by feeding carefully crafted bit sequences in the payload of the WiFi or BLE packets. Systematic algorithms are introduced to embed any desired back-channel messages in FSK format on WiFi and/or BLE compliant packets. Using a commercial off-the-shelf (COTS) narrowband FSK receiver, we demonstrate successful reception of back-channel messages from WiFi and BLE transmitters. Although this COTS narrowband FSK receiver is not specifically designed for the back-channel communication, less than 1% packet error rate (PER) is achieved, validating the concept of software defined back- channel communication among heterogeneous wireless devices.