Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits

Oosterlinck, Dieter; Benoit, Dries F.; Baecke, Philippe; Weghe, Nico Van de

doi:10.1016/j.apgeog.2016.11.005

Cited by 81 publications

(43 citation statements)

References 34 publications

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“…Das and Varshneya [8] suggested to point out mall atmospherics to capture consumers' excitement and value perceptions. There are several research and innovation on technology advancement [9][10] [11] for mall industry that developed Bluetooth beacon technology and GPS-based to map for products at mall. Those technology failed to meet the needs for mall regarding to the high investment and lack of improvement.…”

Section: Introductionmentioning

confidence: 99%

Exploring Design Needs of Wifi-Marketing as Promotional Strategy for Mall Industry

Muna

Fatmasari

Murti

2018

Proceedings of the 4th Bandung Creative Movement International Conference on Creative Industries 2017 (4th BCM 2017)

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Jakarta is one of mall paradise in Indonesia. The emergent of middle class economy has shifted consumer behavior in visiting Mall. Nowadays, people visit mall for various reasons such as hangout with friends, meeting with client, window shopping and many more. Moreover, Wifi Marketing is new trend in marketing innovation. It is one of competitive advantage in attracting more visitors. However, More visitors don't guarantee the increase sales of tenants in Mall. Promotions are needed to help tenants in reaching out mall visitors through wifi-marketing. The purpose of this research is to explore the design needs of Wifi-marketing as cutting edge technology to maximize promotional strategy for Mall business. This is qualitative research in which data is collected through observation and in-depth interview with 8 informants (3 tenants and 5 visitors of shopping mall). The findings showed positive technological readiness towards wifi-marketing which can be interpreted from their optimism, innovativeness, discomfort and insecurity. Optimism and innovativeness become the drivers of wifi-marketing since it showed users' likelihood to adopt wifi for promotional purpose. However, discomfort and insecurity become the constraints for wifi-marketing in terms of privacy issue. Using design ethnography, the design of wifi-marketing welcome page should meet with visitors's needs such as information about mall directory, promotions and special offerings, new tenants information, restaurant recommendation, and customer service support.

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Section: Introductionmentioning

confidence: 99%

Exploring Design Needs of Wifi-Marketing as Promotional Strategy for Mall Industry

Muna

Fatmasari

Murti

2018

Proceedings of the 4th Bandung Creative Movement International Conference on Creative Industries 2017 (4th BCM 2017)

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“…In industrial manufacturing, an RTLS enables autonomous robots to navigate towards designated locations or other objects whose locations the RTLS tracks simultaneously [1]. In malls and public transit, it allows managers to track assets in the field and analyze and predict customer traffic to provide adequate time to prepare for their arrival [2]. In clinical environments, an RTLS can increase workflow efficiency and improve patient safety by, for example, facilitating rapid searching of equipment and accessories, which reduces the time spent locating items for procedures.…”

Section: Introductionmentioning

confidence: 99%

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

et al. 2020

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An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and robustness because Bluetooth signal strength is subject to fluctuation. We developed a machine learning-based solution using a Long Short-Term Memory (LSTM) network followed by a Multilayer Perceptron classifier and a posterior constraint algorithm to improve RTLS performance. Training and validation datasets showed that most machine learning models perform well in classifying individual location zones, although LSTM was most reliable. However, when faced with data indicating cross-zone trajectories, all models showed erratic zone switching. Thus, we implemented a history-based posterior constraint algorithm to reduce the variability in exchange for a slight decrease in responsiveness. This network increases robustness at the expense of latency. When latency is less of a concern, we computed the latency-corrected accuracy which is 100% for our testing data, significantly improved from LSTM without constraint which is 96.2%. The balance between robustness and responsiveness can be considered and adjusted on a case-by-case basis, according to the specific needs of downstream clinical applications. This system was deployed and validated in an academic medical center. Industry best practices enabled system scaling without substantial compromises to performance or cost.In EROC at UTSW, we deployed a fleet of 142 Raspberry Pis to host the Bluetooth sensors. Raspberry Pis are small, robust, low-cost ($35), and simple-to-install computers that run a Linux distribution. They are powered and connected by four 24V routing switches using power-over-Ethernet with a voltage transformer for each Pi. Thus, they require no additional 110V electric outlets, which eliminates a common safety concern for hospitals. Additionally, because we use a wired connection to communicate with the Raspberry Pis, data transmission is more reliable than with Wi-Fi, and there is no increased burden for the Wi-Fi that might affect patient and staff internet usage. The Raspberry Pis are installed in the ceiling, invisible from the ground, so they do not affect the building's appearance.Many RTLS implementations use fixed parts in the infrastructure, such as Wi-Fi routers, as transmitters and moving parts, such as smart phones, as receivers. While this scheme is convenient for brief applications, it is limited when it comes to mass deployment. For example, the battery life of the moving parts is extremely low (e.g., 1-2 days for smart phones) because of an excessive workload that includes continuous BLE signal scanning and communication with clouds, which makes tracking equipment infeasible. Moreover, smart phones or tags with communication functions are usually more expensive than simple active BLE beacons, and their communication with clouds is not as robust or efficient as wired devices. Thus, considering battery life...

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“…In addition to tracking [5], advertisement packets include data that can reveal the type of the device [2], exposing the user to inventory attacks and inference of sensitive attributes. For instance, some medical devices are broadcasting their types (hearing aid, insulin pen, etc.)…”

Section: Introductionmentioning

confidence: 99%

Himiko: A human interface for monitoring and inferring knowledge on bluetooth-low-energy objects

Celosia

Cunche

2019

Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks

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The Bluetooth Low Energy (BLE) protocol is being included in a growing number of connected objects such as smartphones, fitness trackers, headphones and smartwatches. As part of the service discovery mechanism of BLE, devices announce themselves by broadcasting radio signals called advertisement packets that can be collected with off-the-shelf hardware and software. To avoid the risk of tracking based on those messages, BLE features an address randomization mechanism that substitutes the device MAC address with random temporary pseudonyms. However, the payload of the advertisement packet still contains fields that can hamper the randomization mechanism by exposing counters and static identifiers. In addition to defeating the randomization mechanism, some of these fields can leak sensitive attributes of the owner such as his medical condition. As a consequence, we implemented Himiko to raise awareness about the privacy issues that the BLE advertising mechanism can involve. This tool aims to show the information that a passive eavesdropper can infer by leveraging the contents of BLE advertisement packets. The advertising raw data are collected and processed from devices that have their Bluetooth interface enabled. The user is then shown the information that are leaking from his device. CCS CONCEPTS • Networks → Network privacy and anonymity; • Security and privacy → Mobile and wireless security.

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Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits

Cited by 81 publications

References 34 publications

Exploring Design Needs of Wifi-Marketing as Promotional Strategy for Mall Industry

Exploring Design Needs of Wifi-Marketing as Promotional Strategy for Mall Industry

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

Himiko: A human interface for monitoring and inferring knowledge on bluetooth-low-energy objects

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