Passive BLE Sensing for Indoor Pattern Recognition and Tracking

Afyouni, Imad; Musleh, Mashaal; Basalamah, Anas; Tariq, Zaid Bin

doi:10.1016/j.procs.2021.07.028

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The UWB anchors are installed at the four corners of the room at a height of 2700 mm. We checked the measurement errors of a UWB sensor at nine points in the room, and, as listed in Table 2, the total average error of a UWB sensor is 251.6 mm on the X-axis and 201.3 mm on the Y-axis, whereas the errors at Position IDs (1,3,(7)(8)(9) near the hallway side and metallic bookshelves are larger than the average. First, to confirm the effectiveness of stay-area extraction, we compared it with a simple time-based clustering method in terms of the detection accuracy and size of stay areas.…”

Section: Settingsmentioning

confidence: 99%

Mining User Activity Patterns from Time-Series Data Obtained from UWB Sensors in Indoor Environments

FAWAD RAHIM,

HAYAMA

2024

IEICE Trans. Inf. & Syst.

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In recent years, location-based technologies for ubiquitous environments have aimed to realize services tailored to each purpose based on information about an individual's current location. To establish such advanced location-based services, an estimation technology that can accurately recognize and predict the movements of people and objects is necessary. Although global positioning system (GPS) has already been used as a standard for outdoor positioning technology and many services have been realized, several techniques using conventional wireless sensors such as Wi-Fi, RFID, and Bluetooth have been considered for indoor positioning technology. However, conventional wireless indoor positioning is prone to the effects of noise, and the large range of estimated indoor locations makes it difficult to identify human activities precisely. We propose a method to mine user activity patterns from time-series data of user's locationss in an indoor environment using ultra-wideband (UWB) sensors. An UWB sensor is useful for indoor positioning due to its high noise immunity and measurement accuracy, however, to our knowledge, estimation and prediction of human indoor activities using UWB sensors have not yet been addressed. The proposed method consists of three steps: 1) obtaining time-series data of the user's location using a UWB sensor attached to the user, and then estimating the areas where the user has stayed; 2) associating each area of the user's stay with a nearby landmark of activity and assigning indoor activities; and 3) mining the user's activity patterns based on the user's indoor activities and their transitions. We conducted experiments to evaluate the proposed method by investigating the accuracy of estimating the user's area of stay using a UWB sensor and observing the results of activity pattern mining applied to actual laboratory members over 30-days. The results showed that the proposed method is superior to a comparison method, Time-based clustering algorithm, in estimating the stay areas precisely, and that it is possible to reveal the user's activity patterns appropriately in the actual environment.

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Section: Settingsmentioning

confidence: 99%

Mining User Activity Patterns from Time-Series Data Obtained from UWB Sensors in Indoor Environments

FAWAD RAHIM,

HAYAMA

2024

IEICE Trans. Inf. & Syst.

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“…• Timestamping: this technique is especially effective in preventing replay attacks, as it allows the receiver device to ensure that the received packets are not a replay of any older packets [3]. This method can be implemented by adding a time value to each individual advertisement packet before being sent.…”

Section: Replay Attack Mitigationsmentioning

confidence: 99%

Assessing Cybersecurity Risks in BLE-based Asset Management Systems

Verde

Paiva

Lopes

2023

2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)

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In the current era of digital transformation, Asset Management (AM) systems using Bluetooth Low Energy (BLE) beacons are being applied across various domains, allowing for the detection of individuals or objects within a building. While the impact of a compromised Indoor Positioning System (IPS) may not be significant in certain domains, in others it can pose risks and potentially lead to the loss of human lives or other significant consequences.This work starts with a literature review on vulnerabilities that target BLE beacon devices. With the gathered knowledge from the review, a risk assessment of cyber-attacks targeting AM systems using BLE devices in two specific scenarios is presented: healthcare and industry. The aim is to estimate the attacks that pose the greatest risk in each application area. An experimental setup was also created with a focus on testing a set of vulnerabilities, such as replay attack, device cloning, jamming, battery exhaustion attack and physical hijacking. Lastly, mitigation measures and a list of best practices and guidelines are proposed to help harden these systems.Results show that, risk levels vary depending on the targeted scenario. Replay, battery exhaustion, jamming, fuzzing, blue-smack, and physical hijacking attacks are the ones that pose the greatest risk levels in the considered scenarios. Additionally, the vulnerabilities exploited in the experimental setup manifest a concerning accessibility, that can lead to irreversible damages.

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“…e principle of Canny algorithm will be introduced in detail below. e process of realizing image edge detection based on Canny algorithm mainly includes the following five steps [5][6][7].…”

Section: Canny Edge Detectionmentioning

confidence: 99%

“…erefore, we need to combine maintenance theory and practice. Because each edge detection algorithm has its own advantages and disadvantages, one or more edge detection algorithms can be combined according to di erent tasks [5,6], so that the optimal solution can be designed and the optimal e ect can be achieved to the greatest extent. Intelligent identi cation of intelligent interior decoration design can e ectively meet the requirements of modern society.…”

Section: Introductionmentioning

confidence: 99%

Research on Edge Detection Algorithm of Indoor Soft Decoration Pattern

Zheng

Wang

2022

Mobile Information Systems

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Purpose of edge detection is to identify the points with obvious brightness change in digital image. The image edge detection effect under normal light is good, but the image display effect under dark vision is poor, and most of the edge information is lost. In order to quickly and accurately obtain the image edge part containing a large amount of feature information, many classical algorithms have appeared in the field of edge detection. This paper discusses the development of soft edge detection and intelligent edge detection technology for readers. Aiming at the image difference caused by proportion, rotation, and folding in the process of automatic recognition of indoor soft decoration patterns, this paper focuses on the collection of proportion, rotation, blur, and illumination of indoor soft decoration pattern images under five folding changes. The edge detection algorithm (EDA) is used to quickly and accurately obtain images containing a large amount of feature information at a low cost of edge parts, extract the local features of the pattern, calculate the feature matching with Euclidean distance, and eliminate the false matching with random sampling consistency algorithm. Finally, the experimental results show that EDA algorithm usually combines a variety of features to realize edge detection, and its output edge detection result is better than the traditional edge detection algorithm which only considers the gradient change. The algorithm has the highest accurate matching rate, and the average accurate matching rate is 87.10%; the robustness of this algorithm is better than other algorithms, and the speed is the fastest. Therefore, EDA algorithm has good applicability in indoor soft decoration pattern feature matching.

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Passive BLE Sensing for Indoor Pattern Recognition and Tracking

Cited by 7 publications

References 14 publications

Mining User Activity Patterns from Time-Series Data Obtained from UWB Sensors in Indoor Environments

Mining User Activity Patterns from Time-Series Data Obtained from UWB Sensors in Indoor Environments

Assessing Cybersecurity Risks in BLE-based Asset Management Systems

Research on Edge Detection Algorithm of Indoor Soft Decoration Pattern

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