Improved Distance Estimation with BLE Beacon Using Kalman Filter and SVM

Lam, Ching Hong; Ng, Pai Chet; She, James

doi:10.1109/icc.2018.8423010

Cited by 26 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, a, b, and the reference RSSI are dependent on the smartphone used and the real-life environment. There are several algorithms, such as SVM and Machine Learning (ML) [46], with the device or environment-specific training parameterization [47] that can be used to calculate the distance between the devices. In addition, AltBeacon can be used to get device-specific information (manufacturer identification number and 1 m reference RSSI) along with the beacon signal [48] which can be used to improve the distance accuracy for different types of devices used.…”

Section: Distance Measuringmentioning

confidence: 99%

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

et al. 2020

View full text Add to dashboard Cite

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this paper, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis. The IoT node tracks health parameters, including body temperature, cough rate, respiratory rate, and blood oxygen saturation, then updates the smartphone app to display the user health conditions. The app notifies the user to maintain a physical distance of 2 m (or 6 ft), which is a key factor in controlling virus spread. In addition, a Fuzzy Mamdani system (running at the fog server) considers the environmental risk and user health conditions to predict the risk of spreading infection in real time. The environmental risk conveys from the virtual zone concept and provides updated information for different places. Two scenarios are considered for the communication between the IoT node and fog server, 4G/5G/WiFi, or LoRa, which can be selected based on environmental constraints. The required energy usage and bandwidth (BW) are compared for various event scenarios. The COVID-SAFE framework can assist in minimizing the coronavirus exposure risk.

show abstract

Section: Distance Measuringmentioning

confidence: 99%

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The RSSI reflects the distance between the device and the beacon. However, since RSSI is easily affected by environmental obstacles and multipath factors [21][22][23], a filtering algorithm is usually required [24][25][26] to enhance the accuracy of RSSI. In addition, the deployment structure of the beacons also greatly affects the availability of RSSI [27].…”

Section: Related Researchmentioning

confidence: 99%

Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons

Liu

Guo

Zhu

2021

IJGI

View full text Add to dashboard Cite

As communication technology and smartphones develop, many indoor positioning applications based on Bluetooth low energy (BLE) beacons have emerged. However, in a complex BLE network, it can be challenging to select the optimal reference beacon, and accurate positioning becomes difficult. Fortunately, if the BLE network is displayed on a map, we can intuitively grasp the structure and density of the beacons in each area, which is important information for accurate positioning. Therefore, in this study we developed a map-aided indoor positioning algorithm to model the relationship between beacons in the positioning area in a parking lot. Specifically, the algorithm split all beacons into multiple cell areas to find the optimal reference beacon in that area. Then, the optimal reference beacon is used to find the preferred reference beacons among the real-time beacons. Finally, the positioning results were calculated and evaluated according to the preferred beacons. According to the results, our method can optimize the selection of reference beacons in different areas. The average positioning accuracy was 2.09 m and the results can be scored accurately. The results verify that our algorithm can effectively use map information to guide the selection of reference beacons in complex environments.

show abstract

“…However, the performance of this method cannot approach the performance of centralized PF. Lam et al [ 40 ] proposed a novel solution to improve RSSI-based distance estimation for smart object interaction applications in the IoT ecosystem. Their algorithm implements a KF on the edge to deal with noisy RSSI measurements and an optimized SVM on the cloud for distance estimation.…”

Section: Related Workmentioning

confidence: 99%

“…While the aforementioned research provides important references and guidance for our work, and some scholars have attempted to address the problems of RSSI-based target positioning, ranging, and tracking by coupling SVM and KF [ 29 , 39 , 40 ], an LBE method based on combining SVM and KF has not yet been proposed. In contrast with existing methods, we propose the SVM + KF algorithm to improve the stability and accuracy of tracking results and validate the effectiveness and stability of the algorithm via experiments and simulations.…”

Section: Related Workmentioning

confidence: 99%

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Wang

Liu

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

Target Tracking (TT) is a fundamental application of wireless sensor networks. TT based on received signal strength indication (RSSI) is by far the cheapest and simplest approach, but suffers from a low stability and precision owing to multiple paths, occlusions, and decalibration effects. To address this problem, we propose an innovative TT algorithm, known as the SVM+KF method, which combines the support vector machine (SVM) and an improved Kalman filter (KF). We first use the SVM to obtain an initial estimate of the target’s position based on the RSSI. This enhances the ability of our algorithm to process nonlinear data. We then apply an improved KF to modify this estimated position. Our improved KF adds the threshold value of the innovation update in the traditional KF. This value changes dynamically according to the target speed and network parameters to ensure the stability of the results. Simulations and real experiments in different scenarios demonstrate that our algorithm provides a superior tracking accuracy and stability compared to similar algorithms.

show abstract

Improved Distance Estimation with BLE Beacon Using Kalman Filter and SVM

Cited by 26 publications

References 24 publications

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life

Map-Aided Indoor Positioning Algorithm with Complex Deployed BLE Beacons

SVM+KF Target Tracking Strategy Using the Signal Strength in Wireless Sensor Networks

Contact Info

Product

Resources

About