PMC:7796058 / 42527-48931 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7796058","sourcedb":"PMC","sourceid":"7796058","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7796058","text":"5.2. Proximity-Based Contact Tracing\nFor the purposes of this research the third floor of the CCIT building was selected for an experiment. After extracting the related metadata such as room names for the rooms from the IndoorGML, 12 Estimote Proximity beacons were spatially distributed between 12 different cell spaces. The contact tracing technique applied for this research was designed in a way that protects user privacy. The application detects the proximal appearance of users within the proximity zone of each beacon by considering the value of the Received Signal Strength Indicator (RSSI) that was broadcasted by the beacons. The duration of appearance of the user in the proximity zone defined for each beacon and the corresponding date and time information for this proximal appearance are the only information stored in the internal storage of mobile phones. Figure 8b shows a screenshot of the developed mobile application for collecting different types of observations including BeaconID, time, date, and the duration that the target user spent in the proximal zone of each beacon. Assuming that the incubation period of COVID-19 is two weeks, the application will work as a background service that saves data internally for a two-week period.\nIn situations in which the user becomes a positive COVID-19 case, he/she can voluntarily share data captured within the past two weeks with the backend database management system. An AWS product Amazon Cognito was used to control user authentication and access to data storage. As shown in Figure 8c, users are required to sign in/up for an Amazon Cognito account in order to share their information. After signing in as an authorized client, users can publish their internal information to the Amazon cloud as shown in Figure 8d. All of the data related to the COVID-19 cases will be stored and managed in the DynamoDB database in the Amazon cloud. Our developed application was connected to the DynamoDB using another AWS product, the IoT Core. When new data is added to cloud storage, the contact tracing application will look for any matches between the backend data and the data stored internally in the user device. If it finds any matches that show that a confirmed COVID-19 positive case and the target user were close to each other for more than 15 min, the application will then notify the target user about potential exposure to COVID-19 and alert cleaning staff to disinfect the place. This process is shown in Figure 9. A demo of people trajectories is shown in Supplementary Materials.\nThere are various methods for indoor positioning, such as WiFi, BLE beacons, or dead reckoning. Using BLE technology is cost-effective compared to other indoor positioning techniques, which use maintenance, installation, and cabling costs. Generally, Bluetooth devices cost ~20× less than WiFi devices and have a similar WiFi accuracy [60].\nIn this paper, we focused on BLE proximity detection for contact tracing instead of precise positioning. Three categories of user location will be of importance for this paper including immediate (less than 60 cm), near (1–6 m), and far (\u003e10 m) distance of the Bluetooth receiver from active BLE beacon. On the other hand, it was still a challenge working with BLE signals that are interfered with by structures. Indoor setting and layout have direct effects on radio waves used in Bluetooth technology. Another challenge was that the different beacon types and battery states produce different signal strengths, so using one beacon library for all types of beacons was problematic.\nIn this paper, an active BLE beacon is placed in each IndoorGML cell (e.g., room). Moreover, we focus on proximity detection (i.e., immediate (within 0.6 m away), near (within about 1–8 m), and far (is beyond 10 m) distances from the active BLE beacon) to make indoor spatiotemporal trajectories using IndoorGML cell connectivity. We avoided having to determine the exact range by way of careful beacon placement to prevent overlaps. In the context of COVID-19 spread, locating in the immediate and near distance from the infected host would be dangerous for coronavirus transmission (through droplet transmission). Accordingly, different health organizations such as WHO recommended two meters distance from others. As a result, proximity detection should be of more importance in the COVID-19 context. In other words, considering precise positioning would only increase the computation cost in this specific application. Describing an indoor location using IndoorGML graph cell also helps with privacy. Considering privacy concerns for individual tracking, especially in indoor environments, we believe that proximity positioning respects user privacy more than precise positioning.\nDepending on the size of the data, type of beacons, and network bandwidth, mobile proximity detection performance may differ. In our experiment, various beacons such as Estimote (https://estimote.com/), Accent Systems (https://accent-systems.com/) and Radius Networks (https://www.radiusnetworks.com/) have been evaluated using the developed app on the Samsung Galaxy S9 smartphone. Our results demonstrated that the app could capture a beacon’s proximity of fewer than 60 milliseconds, which is enough for our case study. The complexity of the position determination depends on the beacon software development kit; however, the complexity is O(n) in the worst-case scenario. Concerning the duration spent in a room, we detected and recorded durations of less than five seconds when walking past beacons in a corridor. Significance of time for the sake of COVID-19 risk was not considered important for durations less than 15 min, which was standard practice. So, our sampling and recording intervals were much better than was required for COVID-19 risk evaluation.\nThe mobile application publishes a JSON payload to the AWS IoT Core cloud data management system in which: Online service: A single record showing the presence of a user in the proximity of an active BLE beacon is published to the AWS IoT core.\nOffline service: An array of records showing the user’s pretenses in a time window is published to the AWS cloud.\nA JSON payload showing a single enriched proximity location captured by the developed smartphone app is shown in Supplementary Materials. For more information regarding contact tracing app can be found in 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