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1. Using RSSI to Form Path in an Indoor Space

   https://doi.org/10.1109/ICCCN54977.2022.9868912  

   Fuad, Muztaba; Deb, Debzani; Panlaqui, Brixx-John G.; Mickle, Charles F. (July 2022, 2022 International Conference on Computer Communications and Networks (ICCCN))

   Generating paths of a mobile device in indoor space by sensing its Bluetooth RSSI value is challenging but has real-world applications. Although Bluetooth RSSI suffers from different factors that limit its usability, this research shows that it can still be used to detect mobility and, over a duration of time, can be used to form paths. This poster presents algorithms that can create a path of a moving mobile device by sensing its RSSI values over time and then presents early results of the algorithm's effectiveness while tracking health practitioners' movement within a community care clinic setting. 

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2. Path Forming of Healthcare Practitioners in an Indoor Space Using Mobile Crowdsensing

   https://doi.org/10.3390/s22197546  

   Panlaqui, Brixx-John; Fuad, Muztaba; Deb, Debzani; Mickle, Charles (October 2022, Sensors)

   While there are numerous causes of waste in the healthcare system, some of this waste is associated with inefficiency. Among the proposed solutions to address inefficiency is clinic layout optimization. Such optimization depends on how operating resources and instruments are placed in the clinic, in what order they are accessed to attain a particular task, and the mobility of clinicians between different clinic rooms to accomplish different clinic tasks. Traditionally, such optimization research involves manual monitoring by human proctors, which is time consuming, erroneous, unproductive, and subjective. If mobility patterns in an indoor space can be determined automatically in real time, layout and operation-related optimization decisions based on these patterns can be implemented accurately and continuously in a timely fashion. This paper explores this application domain where precise localization is not required; however, the determination of mobility is essential on a real-time basis. Given that, this research explores how only mobile devices and their built-in Bluetooth received signal strength indicator (RSSI) can be used to determine such mobility. With a collection of stationary mobile devices, with their computational and networking capabilities and lack of energy requirements, the mobility of moving mobile devices was determined. The research methodology involves developing two new algorithms that use raw RSSI data to create visualizations of movements across different operational units identified by stationary nodes. Compared with similar approaches, this research showcases that the method presented in this paper is viable and can produce mobility patterns in indoor spaces that can be utilized further for data analysis and visualization. 

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3. Sensing the walking velocity of a person by using mobile devices

   Anthony Smith, Muztaba Fuad (April 2022, The Twenty-Sixth ACM Annual Consortium for Computing Sciences in Colleges Northeastern Conference)

   In an indoor space, determining a person’s speed of mobility has a lot of research significance and applicability in real-world scenarios. This research has developed a mobile application to investigate how to determine a person’s walking speed. The goal was to determine a person’s walking speed by using the number of steps. There has been similar work to test the accelerometer sensor in detecting steps. However, the accuracy of using the steps to calculate the velocity was not studied. This application uses the accelerometer sensor in the mobile device to detect steps and then compute the velocity. The accelerometer provides information about the user’s motion and acceleration, and an algorithm was developed to use that data to determine the steps. Once steps are determined, the person’s speed is calculated by using the change of location within a pre-determined space and time. Therefore, accurately measuring the number of steps was vital and it was determined that the position of the mobile device in the body plays a significant role in that accuracy. Therefore, the experiment used three device positions: the pants front pocket, the right hand, and the backpack. While walking, the number of steps were manually counted and recorded. A comparison was made between the recorded number of steps to the application’s measured steps. The experiment was conducted multiple times for each device position. The placement of the mobile devices in the front pants pocket gives the most accurate results, whereas the other two device positions gave reasonably accurate results. The position of the device played an important part in the research and had a significant impact on the accuracy of the results. In the future, testing can include additional device positions. Additionally, other mobile device sensors could be included in the testing and can be compared with this approach. 

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4. Rethinking ranging of unmodified BLE peripherals in smart city infrastructure

   https://doi.org/10.1145/3204949.3204950  

   Islam, Bashima; Uddin, Mostafa; Mukherjee, Sarit; Nirjon, Shahriar (January 2018, MMSys '18 Proceedings of the 9th ACM Multimedia Systems Conference)

   Mobility tracking of IoT devices in smart city infrastructures such as smart buildings, hospitals, shopping centers, warehouses, smart streets, and outdoor spaces has many applications. Since Bluetooth Low Energy (BLE) is available in almost every IoT device in the market nowadays, a key to localizing and tracking IoT devices is to develop an accurate ranging technique for BLE-enabled IoT devices. This is, however, a challenging feat as billions of these devices are already in use, and for pragmatic reasons, we cannot propose to modify the IoT device (a BLE peripheral) itself. Furthermore, unlike WiFi ranging - where the channel state information (CSI) is readily available and the bandwidth can be increased by stitching 2.4GHz and 5GHz bands together to achieve a high-precision ranging, an unmodified BLE peripheral provides us with only the RSSI information over a very limited bandwidth. Accurately ranging a BLE device is therefore far more challenging than other wireless standards. In this paper, we exploit characteristics of BLE protocol (e.g. frequency hopping and empty control packet transmissions) and propose a technique to directly estimate the range of a BLE peripheral from a BLE access point by multipath profiling. We discuss the theoretical foundation and conduct experiments to show that the technique achieves a 2.44m absolute range estimation error on average. 

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5. BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection

   https://doi.org/10.1145/3412382.3458262  

   Billah, Md Fazlay; Saoda, Nurani; Gao, Jiechao; Campbell, Bradford (January 2021, IPSN '21: Proceedings of the 20th International Conference on Information Processing in Sensor Networks)

   null (Ed.)

   The emergence of radio frequency (RF) dependent device-free indoor occupancy detection has seen slow acceptance due to its high fragility. Experimentation shows that an RF-dependent occupancy detector initially performs well in the room to be sensed. However, once the physical arrangement of objects changes in the room, the performance of the classifier degrades significantly. To address this issue, we propose BLECS, a Bluetooth-dependent indoor occupancy detection system which can adapt itself in the dynamic environment. BLECS uses a reinforcement learning approach to predict the occupancy of an indoor environment and updates its decision policy by interacting with existing IoT devices and sensors in the room. We tested this system in five different rooms for 520 hours in total, involving four occupants. Results show that, BLECS achieves 21.4% performance improvement in a dynamic environment compared to the state-of-the-art supervised learning algorithm with an average F1 score of 86.52%. This system can also predict occupancy with a maximum 89.23% F1 score in a completely unknown environment with no initial trained model. 

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