ABSTRACT
To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.
ABSTRACT
Technologies for sensing crowd density have a potential to make our society smarter, and such technologies have been used to help social distancing in the context of COVID-19 pandemic. We have developed a method to sense and forecast street-level crowd density by observing public Bluetooth Low Energy (BLE) advertisements from popular contact tracing applications in Japan. We have deployed our methods in several locations in Tokyo and published the estimated street-level crowd density level on our website as well as a television program. In this paper, we report the status of our project, focusing on the result of experiments to verify the potential of our method after the contact tracing applications stop working. Through an experiment in an urban public space in Tokyo, we have shown that BLE advertisements are almost occupied with contact tracing applications and manufacture specific data from a few companies. In addition, by monitoring different types of BLE advertisements in several locations in Japan, we have clarified that those containing manufacture specific data with a certain company identifier have almost the same trend as those from contact tracing applications, with the average correlation coefficient of 0.990. © 2022 TRON Forum.
ABSTRACT
Within a short period of time, the highly infectious COVID-19 virus has progressed into a pandemic which has forced countries to develop contact tracing solutions for closer monitoring of its further spread into the society. Bluetooth low energy (BLE) has been extensively adopted to implement contact tracing focusing mainly on utilizing received signal strength indicator (RSSI) for its distance estimation toward close contact identification (CCI). Nevertheless, when observed closely, many of these solutions were not able to accurately carry out the contact tracing as required by Centers for Disease Control (CDC) and Prevention. The provisions set were distance of within 6-ft (~ 2 m) and period of no less than 15 min for close contact identification. This is mainly because usage of RSSI is highly unstable and volatile. In closing the gap, we proposed a novel approach that utilizes low calibrated transmission power (Tx) employing nRF52832 BLE chipset as wearables, in which, at a distance of greater than 2 m, no close contact will be detected making the accuracy to high and low error distance estimation under ideal condition. Algorithm in establishing close contacts is also demonstrated with complete experimentation. Results show that our proposed solution has maximum error of 0.3209 m in distance estimation of 2 m and 71.43% accuracy in CCI with 4 devices and distance of 2 ± 0.3 m consideration. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Recently, as a consequence of the coronavirus disease (COVID-19) pandemic, dependence on contact tracing (CT) models has significantly increased to prevent the spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. This article addresses this gap and proposes the trustworthy blockchain-enabled system for an indoor CT (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof-of-Work (dPoW) credit-based consensus algorithm coupled with randomized hash window (W-Hash) and dynamic Proof-of-Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving a high localization performance, we capitalize on the availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data-driven localization model based on bluetooth low-energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by the implementation of a highly accurate CT model while improving the users' privacy and security.
ABSTRACT
We are developing a tourism support application using BLE (Bluetooth Low Energy) beacons in Senjogahara, Oku-Nikko. There are several ways to supply power to a BLE beacon. We decided to use solar-powered since the Oku-Nikko area is on a mountain, and it is not easy to visit there frequently for maintenance. So, we developed a BLE beacon with a small silicon solar cell in 2018. However, during the summer, sunlight is blocked by the leaves of trees, and it is not easy to get enough electricity to drive a BLE beacon. Therefore, we redesigned the BLE beacon using dye-sensitized solar cells to generate electricity with weaker light than ordinary silicon solar cells. We replaced BLE beacons in Senjogahara with the new beacon in 2021. We confirmed that the new BLE beacons work stably even in an environment covered with forests. In this paper, we explain the design of the new BLE beacon and how it works under weak light. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
With the advent of Bluetooth Low Energy (BLE)-enabled smartphones, there has been considerable interest in investigating BLE-based distancing/positioning methods (e.g., for social distancing applications). In this paper, we present a novel hybrid learning method to support Mobile Ad-hoc Distancing (MAD) / Positioning (MAP) using BLE-enabled smartphones. Compared to traditional BLE-based distancing/positioning methods, the hybrid learning method provides the following unique features and contributions. First, it combines unsupervised learning, supervised learning and genetic algorithms for enhancing distance estimation accuracy. Second, unsupervised learning is employed to identify three pseudo channels/clusters for enhanced RSSI data processing. Third, its underlying mechanism is based on a new pattern-inspired approach to enhance the machine learning process. Fourth, it provides a flagging mechanism to alert users if a predicted distance is accurate or not. Fifth, it provides a model aggregation scheme with an innovative two-dimensional genetic algorithm to aggregate the distance estimation results of different machine learning models. As an application of hybrid learning for distance estimation, we also present a new MAP scenario with an iterative algorithm to estimate mobile positions in an ad-hoc environment. Experimental results show the effectiveness of the hybrid learning method. In particular, hybrid learning without flagging and with flagging outperform the baseline by 57 and 65 percent respectively in terms of mean absolute error. By means of model aggregation, a further 4 percent improvement can be realized. The hybrid learning approach can also be applied to previous work to enhance distance estimation accuracy and provide valuable insights for further research. IEEE
ABSTRACT
Tourism, which has developed in line with the development of transport, has had to undergo major changes. As the push for SDGs spreads across the world, and for safe travel post-COVID-16, environmentally friendly smallgroup tourism is being promoted. It would be beneficial if the smartphones, which is used daily lives, could be useful in the nature for small groups of novice walkers to walk safety and knowing some new information about the area. However, the signal conditions are not always perfect in forests. Therefore, we have developed a smartphone application using Bluetooth Low Energy (BLE) beacons equipped with solar panels in Nikko National Park in Japan. Japan has long had the concept of forest bathing. Walking in the forest is told to have positive effects on the body and in the mind. We tried to clarify one of effects of forest bathing by measuring brain waves. We measured the effects of walking in nature by conducting simple EEG measurements while walking and measuring the degree of relaxation in the forest in 2021. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Healthcare-associated infections (HAI) are a primary concern in critical care units, dialysis centers, and nursing stations. Practicing Alcohol-Based Hand Hygiene (ABH) is predicted to reduce the risk of disease transmission. Recent technological breakthroughs in the Internet of Things (IoT) and Long Range communication (LoRa) protocols provide cutting-edge solutions to reduce HAIs, particularly community and hospital-acquired pneumonia. This research uses an IoT-enabled LoRa network to monitor and track hand hygiene practices to avoid pneumonia and other HAI infections. The ABH dispenser can recognize the subjects, activate the hand sanitizer, permit the subject admission, and record hand-washing activities to a server powered by an NVIDIA Jetson Nano Graphical Processing Unit computer. All the data, with a user ID and GPS location, is deployed in a cloud server and an application server for storage and display and relayed to a LoRa gateway using the ESP32 IoT platform equipped with a LoRaWAN and short-range Bluetooth Low Energy. A series of field tests was conducted in various hospital buildings and simulated scenarios. Real-world LoRa network situations have brought an overall success rate of 92%, whereas laboratory testing has an overall success rate of 98%. As individuals grew more conscious of the need for personal and institutional hygiene during the Covid-19 pandemic, the frequency of HAIs increased. The IoT-enabled intelligent ABH network is a cost-effective infection prevention and control mechanism, and it reduces pneumonia, HAI rate, and stress of healthcare workers and critical care unit patients. © 2022 Science Press. All rights reserved.
ABSTRACT
Digital Contact tracing with smartphone apps may help control the spread of serious pathogens, such as COVID-19. Such apps typically use peer-to-peer Bluetooth data transfer to record a contact. However, they suffer from low adoption rates, high false alarm contact indications, battery drain, and user privacy concerns. This paper proposes BECT or BEacon-based Contact Tracing, a contact tracing framework using static Bluetooth beacon devices installed in public or private places that periodically broadcast packets to nearby users that are stored as coins. Users that are positively diagnosed submit their coin IDs to a third-party service (e.g., local health authority) which can mark these coins as infected and disseminate them to other users. A match between a user's stored coins and an infected coin implies that the user has come in direct or indirect contact with an infected person. The BECT framework does not expose users' private data and conserves the device battery. We use MATLAB simulations to compare the performance of the BECT framework to phone-phone apps in a restaurant scenario and show that BECT has superior contact tracing performance. We also provide general deployment guidelines. © 2022 IEEE.
ABSTRACT
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node’s behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users’privacy and security. IEEE
ABSTRACT
Covid-19 pandemic has compelled countries to conduct contact tracing vigorously in order to curb the highly infectious virus from further spread. In this context, Bluetooth Low Energy (BLE) has been broadly used, utilizing Received Signal Strength Indicator (RSSI) for Close Contact Identification (CCI). However, many of the available solutions are not able to adhere to the guidelines provided by Centers for Disease Control (CDC) and Prevention which are: (1) Distance requirement of within 6-feet (~2 meters) and (2) Minimum 15-minutes duration for CCI. In providing some closure to the gap, we proposed a novel approach of utilizing: (1) Low calibrated transmission power (Tx) and (2) Number of signal captures. Our proposed approach is to lowly calibrate Tx so that when distance is at 2 meters between users, number signal capture gets lower as the chipset’s smallest RSSI sensitivity value has been reached. In this paper, complete experimentation for Proof of Concept (POC) and Pilot test conducted are demonstrated. Results obtained shows that the accuracy for POC utilizing signal captures for 2±0.3 m distance is at: (1) 71.43% for 5 users and (2) 70.69% for 9 users. While so, accuracy for the Pilot test when considering CCI on individual case-basis is at 95% for 5 users. © 2022 by the authors. Licensee ESJ, Italy.
ABSTRACT
In many of the world's major cities, commuter trains provide vital transportation support and thus play an essential role in our daily lives. Therefore, it has become necessary to estimate the degree of congestion in each train car, both to improve passenger comfort levels and, more recently, to prevent worsening the COVID-19 pandemic infection rate. However, it is difficult to estimate the degree of congestion within a train without violating passenger privacy. The same issues are true for busses, which is noteworthy because we have previously developed and evaluated a system that can estimate the degree of congestion within a bus while protecting passenger privacy by using Bluetooth Low Energy (BLE) signals. In this paper, we report on our efforts to extend that system to railway use, which were conducted on actual trains in cooperation with Kintetsu Railway Co., Ltd. During this trial, we collected BLE signals and used the data to estimate congestion levels in each car using an ML regression model. The results show that the mean absolute error (MAE) and the mean absolute percentage error (MAPE) could be estimated at accuracy levels of 5.56 and 0.27, respectively. © 2022 IEEE.
ABSTRACT
COVID-19 (Corona Virus Disease 2019) is a pandemic which has been spreading exponentially around the globe. Many countries had adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home can cause worse effects like economic crises, unemployment, food scarcity, and mental health problems of individuals. EasyBand2.0 is a wearable personal safety device that helps in social distancing and also helps in safe mobility. Under the IoMT (Internet of Medical Things) framework the wearable EasyBand2.0 device helps in social distancing, it avoids human-to-human contact and helps maintain a safer distance. EasyBand2.0 uses the Low Power BLE technology to sense distance between two user devices and alert them based on the distance and time spent in proximity. Safe mobility of people is also important as travel is resumed in all forms. This paper proposes a software application along with the easy band to further be integrated with a system that works based on GPS (Global Positioning System) or GIS (Geographic Information System) to provide travel logging for contact tracing without exposing personal data. A CARS (Context Aware Recommendation System) based safe zone recommender system is proposed in this paper to aid safe mobility. © 2022 IEEE.
ABSTRACT
Coronavirus disease 2019 continues to devastate many countries around the world including Fiji, which relies on digital contact tracing apps such as careFiji to help contain the virus. Certain audits and papers show that Quick Response (QR) codes have low rates of usage which might affect the effectiveness of contact tracing efforts, especially in Fiji. This paper is a limited review of the technologies as well as contact protocols used in contact tracing, the official contact tracing apps used in the south pacific and an overview of the careFiji app. The aim of this is to find out about the contact tracing technologies and protocols to aid in designing a solution to the problems encountered in careFiji as well as other similar contact tracing apps in the South Pacific. The authors propose a NearField Communication (NFC) Contact Tracing Solution Model to supplement the current QR scanning feature of the careFiji app to allow for increased usage of the location-coupled tracking feature of contact tracing efforts. Results of tests conducted prove the convenience and time saving measures of a sample contact tracing app employing NFC versus the careFiji app relying on QR scanning for location-coupled tracking.
ABSTRACT
The recent COVID pandemic challenged healthcare systems worldwide and highlighted not only a lack of sufficient resources in some cases, but also an overall inefficiency in managing available PMDs (Portable Medical Devices). Hospitals typically provide their staff with smartphones to facilitate internal communication and access to hospital services. The key contribution of PMD-Track lies in the use of smartphones replacing expensive stationary gateways scattered across a hospital, acting as mobile gateways associated with a front-end that allows staff to quickly find PMDs. Thus, employees walking nearby tagged PMDs - as they perform daily activities - constantly help to automatically update these PMDs' locations in a live inventory tracker allowing to retrieve up-to-date information.PMDs equipped with traditional Bluetooth Low Energy (BLE) tags will update a backend service with the location of recently spotted tags and display information concerning their position in real-time. Different PMD types can issue alerts according to the type of their mobility (i.e., considering that portable devices can be more or less 'dynamic'). Thus, it is expected that PMD-Track will enable hospitals to make efficient use of their PMDs in emergency situations, such as a pandemic or eventual natural disasters, where a sudden increase in demand can now be foreseen. © 2022 IFIP.
ABSTRACT
The spread of COVID-19 disease has reduced the visitors count to various public places like parks, libraries and museums. Even though Indian Government has relaxed the rules for the public to visit these places during the early 2021, people deny visiting those places due to the fear created by the impact of the disease. This leads to huge revenue loss due to lack of visitors. In order to solve this issue, a safer visiting procedure through a Mobile Application based Secured Smart Museum (MSSM) has been provided. This system strictly monitors the entry of the visitors through two way screening process which ensures the safety of the visitors at the museum. Two-way screening process involves the measuring the temperature of the visitors with the support of IR temperature sensor and monitoring the availability of the face mask with the help of Open Computer Vision. The system also facilitates the users to book the ticket through Mobile Application. This system also alerts the museum's housekeeping department to clean and sanitize the museum based upon the visitors count. In addition to this, our system facilitates the visitors to gain the knowledge about the contents available in the museum through mobile application itself in their own preferred language. © 2022 IEEE.
ABSTRACT
Shopping is considered as an essential activity for many households and is negatively impacted ever since the COVID-19 pandemic had begun. People are panic buying and crowding without social distancing and practice hygiene after exchanging physical touch with objects or cashiers at retail stores. A Smart Shopping System that utilizes RFID and integrated with mobile website technology can improve efficiency and effectiveness for customers to shop even during a pandemic in a more controlled manner. It can be achieved by integrating closely both hardware and software over an active internet connection to send and receive data for real-time updates for each respective customer. The research aims to facilitate users for an improved shopping experience that highlights the accessibility of easy, contactless purchasing, engaging marketing and simultaneously reduces direct contact with public possessions during the coronavirus (COVID-19) pandemic. An extensive discussion of the existing similar systems reviews, literature review and the current developments are performed in depth. The research gathered public insights through a quantitative research approach and applied the gather data for meaningful information in order to recommend an efficient solution. With the creation and visualization of a prototyping model, the researcher demonstrated how a Smart Shopping System could achieve efficiency and effectiveness in a retail store on a small scale. © 2022 IEEE.
ABSTRACT
Using computational techniques to foster new empathetic relationships between human bodies and the space around them, Behnaz Farahi, Assistant Professor in the Department of Design at the California State University in Long Beach, presents some of the concepts and events that have inspired her research and focuses on a recent project for an interactive niqab. Copyright © 2022 John Wiley & Sons, Ltd.
ABSTRACT
When an uninfected person is within 6 feet of an infected person for more than 15 minutes, the probability of COVID-19 transmission increases. Contact Tracing Apps aid people in COVID-19 infection prevention. Our proposed work focuses on developing a Contact Tracing App named CT-19 using Bluetooth Low Energy (BLE). When users run CT-19 App on their smartphone, it broadcasts and records unique private key along with private keys broadcasted from nearby smartphones. When a patient is informed that he/she has COVID-19 infection, the health authority uploads private key from their phone to a central server (only with the consent of patient). Periodically central server broadcasts these keys to all the users who has installed CT-19 App. Once broadcasted keys are received, CT-19 App checks whether any already stored keys match with this received broadcasted key. If it matches, then it implies that user was in physical proximity to an infected individual. Based on this information user can take some precautionary steps like undergoing COVID-19 tests/quarantine. When a user runs an app on his/her smartphone, user privacy is extremely crucial. Our proposed app CT-19 protects user privacy by neither capturing nor keeping the user's GPS or phone number.
ABSTRACT
The recent pandemic revealed weaknesses in several areas, including the limited capacity of public health systems for efficient case tracking and reporting. In the post-pandemic era, it is essential to be ready and provide not only preventive measures, but also effective digital strategies and solutions to protect our population from future outbreaks. This work presents a contact tracing solution based on wearable devices to track epidemic exposure. Our proximity-based privacy-preserving contact tracing (P3CT) integrates: 1) the Bluetooth Low Energy (BLE) technology for reliable proximity sensing, 2) a machine-learning approach to classify the exposure risk of a user, and 3) an ambient signature protocol for preserving the user's identity. Proximity sensing exploits the signals emitted from a smartwatch to estimate users' interaction, in terms of distance and duration. Supervised learning is then used to train four classification models to identify the exposure risk of a user with respect to a patient diagnosed with an infectious disease. Finally, our proposed P3CT protocol uses ambient signatures to anonymize the infected patient's identity. Extensive experiments demonstrate the feasibility of our proposed solution for real-world contact tracing problems. The large-scale dataset consisting of the signal information collected from the smartwatch is available online. According to experimental results, wearable devices along with machine learning models are a promising approach for epidemic exposure notification and tracking. © 2013 IEEE.