Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era
1. Introduction and Motivation
- We offer a comprehensive review of user location tracking, proximity detection, and wireless contact-tracing solutions, by also explaining the underlying principles and technologies for such solutions.
- We address the issue of decentralization, as opposed to centralized approaches; discuss both centralized and decentralized approaches in terms of architectures and protocols; and summarize the advantages and challenges of a decentralized approach versus a centralized approach.
- We discuss the technical and privacy-related challenges in digital contact-tracing applications and present our ideas on open research questions and the way forward.
2. Terminology and Conceptual Definitions
3. Added Value of Our Survey Compared to the State-Of-The-Art
4. Wireless Location Technologies for User Location Tracking, Proximity Detection, and Contact Tracing
4.1. Classification of Wireless Connectivity Solutions
- Cellular versus non-cellular connectivity solutions: cellular solutions refer to the wireless communication techniques where a large geographical area is split into smaller cells, and each cell is served by a base station or an access node; the connectivity between base stations can be ensured either in a wireless manner or via optical fiber, and there is typically a centralized server/operator to maintain and control the whole cellular network. Examples or cellular technologies are Fourth-generation cellular systems (4G) or Long-Term Evolution (of cellular systems) (LTE) and Fifth-generation cellular systems (5G). Extensive surveys of cellular network connectivity solutions can be found, for example, in  (focus on localization aspects in cellular networks),  (focus on mobility models in cellular networks), or  (focus on green communications with cellular networks). The non-cellular solutions do not rely on the geographical division into cells and can operate both in an ad hoc/decentralized manner and in an infrastructure/centralized manner. Two of the most encountered solutions of non-cellular connectivity are BLE and Wi-Fi connectivity. To the best of the Authors’ knowledge, cellular solutions have not been much investigated yet in the context of digital contact tracing, but this can change shortly with the advent of powerful 5G-based positioning technologies [45,46,47]. 5G-based positioning is addressed in more detail in Section 4.2. Examples of non-cellular wireless technologies are BLE, Wi-Fi, ZigBee, RFID, Long Range access (LoRa), NarrowBand Internet of Things (NB-IoT), etc. A good survey of non-cellular technologies can be found in . The most used wireless technology for digital contact tracing at present is the BLE technology, due to its widespread on mobile devices and relative low-power consumption.
- Licensed versus non-licensed frequency bands: a licensed band is a frequency band where wireless transmission is only allowed for operators having purchased a license; for example, traditionally, cellular operators have designated licensed frequency bands to operate. Unlicensed bands are the Industrial, Scientific, and Medical (ISM) bands as well as frequencies not yet allocated in the spectrum (e.g., some millimeter-wave frequencies and Terahertz (THz) frequencies). Interoperability issues between licensed and non-licensed spectra and cellular versus non-cellular communications have been addressed, for example, in . A wireless tracking or contact-tracing solution relying on licensed bands would require the participation of operators having access to such licensed bands. Therefore, it is likely to be a centralized solution. Decentralized solutions in licensed bands are not available at the present moment to the best of the authors’ knowledge.
- Mass-market versus high-end connectivity solutions: a mass-market solution typically has low cost, low energy/power consumption, and it is affordable to the mass-market consumers. High-end connectivity solutions such as those relying on Augmented Reality (AR), Virtual Reality (VR), or Mixed Reality (XR) and, sometimes, on machine learning, are unlikely to become relevant promptly in the context of contact tracing and user tracking, as they aim at a narrow or niche market. They are unlikely to be adopted on a large scale (usually due to high costs). A good review of low-end, middle-end, and high-end IoT devices can be found in .
4.2. User Location Tracking
|Reference||Main Findings||Relevance for COVID-19|
|||A comprehensive survey on cellular and non-cellular positioning methods; high-accuracy simultaneous location and mapping solutions.||While contact tracing is not explicitly addressed here, centralized contact-tracing solutions can benefit from the reported high-accuracy localization solutions.|
|||A thorough overview of cellular-based localization techniques, including sensor-aided and Assisted GNSS solutions.||Might be relevant for centralized contract tracing solutions with operators’ collaboration, but sufficient accuracy can be achieved only with high-power/high energy-consumption solutions such as assisted GNSS and 5G.|
|||A survey on indoor user-tracking solutions with pros and cons of each solution; challenges in indoor localization are also addressed in detail.||RSS-based localization solutions were typically found more energy efficient than TOA/AOA-based solutions; also Visible Light Communications (VLC)-based localization was emphasized as having both good accuracy and low energy consumption and might be a good future candidate for COVID-19 contact tracing.|
|||Another survey on indoor user-tracking solutions with focus on RSS and FP solutions.||The application is not relevant for COVID-19 control.|
|[45,82]||Comprehensive surveys on 5G positioning methods.||Particular topic was not yet studied in COVID-19 context, but has high potential of high accuracy for user location both indoors and outdoors.|
|Ref.||Main Findings||Relevance for COVID-19|
|||Proximity detection via ZigBee of two devices equipped with ZigBee chipsets and using RSS.||Only objects placed at maximum 20 cm from each other were studied in , but the detection results higher than 96% are promising and zigBee ranges can go to several meters, thus making ZigBee a potential useful candidate for COVID-19 contact tracing.|
|||A privacy-preserving protocol for proximity detection for any range measurements (RSS, TOA, etc.).||Rather high latencies (order of tens of ms) can hinder a good real-time implementation.|
|||Proximity detection via BLE RSS and compressed sensing to deal with incomplete observations.||Very promising approach also in the COVID-19 contact-tracing context, even if not studied for this purpose in ; detection probabilities up to 90% for sub-m distances.|
|||RFID-based proximity detection with ambient backscattering.||A digital contact tracing with such solution would require a centralized server, many RFID readers and people equipped with passive RFID tags; such solution would be quite expensive and impractical.|
|||Wi-Fi RSS-based proximity detection between two mobile phones.||Promising approach as experiments in  showed 90% accuracy for distances below 2 m and 100% accuracy for distances below 3 m.|
|||A sociometric sensor capable of detecting proximity, movement, and verbal interaction between people via Wi-Fi RSS, Inertial Measurement Unit (IMU), and sound sensor.||Another promising solution with proximity detection of 90% up to a distance of 3 m and having the ability to also detect verbal interactions; however it is an expensive and high-power solution.|
|||Magnetic field-based proximity detection.||It requires centralized architectures and it has rather moderate accuracy.|
|||Infrastructure-less proximity detection between users based on public web cameras.||Might provide a low-cost solution for the users, but it requires public webcams as well as protocols to identify the users; user privacy cannot be ensured.|
|||MIT SafePaths is an open source location sharing app relying on BLE and GPS; it relies on Private Automated Contact Tracing (PACT) decentralized protocol.||It has already been proposed as a contact-tracing app, but its usefulness is still to be tested at large scales.|
4.3. Proximity Detection
4.4. Contact Tracing
4.5. Comparative Summary
4.6. Open Access Datasets
4.7. Mathematical Models for Digital Contact Tracing
5. Decentralization Concept
5.1. Decentralized Versus Centralized Architectures
5.2. Centralized Versus Decentralized Protocols
|DP-3T ||Ensures a sufficient level of user privacy; open source; the protocol has proved a good accuracy in several field tests; works with both Android and iOS devices.||Relies on voluntary actions from infected users (to upload own EphIDs to a cloud server) and on available long-range and short-range connectivity.|
|GAEN ||Already deployed in some countries; relies on existing infrastructure; no additional costs to the users with smart devices; suitable for both iOS and Android devices.||It has proprietary software; battery consumption may be an issue; privacy level highly relate to the Apple and Google technical platforms and their vulnerabilities.|
|ROBERT and DESIRE ||Open-source protocols; low-power and it relies on BLE widely spread infrastructure.||Advantages over GAEN and DP-3T (if any) are unclear; relies on setting new legal structures for inter-governemts collaboration.|
|SafePaths and PACT ||Open-source contact-tracing app and protocol with privacy by design; it relies on random user IDs, as GAEN and DP-3T; works on both iOS and Android.||Advantages over GAEN and DP-3T are unclear; authors say it is identical to Covid-Watch app and very close to DP-3T.|
|Covid-Watch ||It does not collect user location data; relies on low-power BLE technology available on most iOS and Android devices.||Same challenges as SafePaths/PACT.|
|TCN ||Open-source contact-tracing app working on iOS and Android; relies on BLE and temporary and random user IDs, as DP-3T.||Advantages over GAEN and DP-3T are unclear; the main difference is that the participation of a health authority in the protocol chain is optional; this opens the paths to possibly fake reports from users in the fully crowdsourced mode.|
|OpenCovidTrace ||Open source; aims at integrating several existing protocols such as GAEN, DP-3T, BlueTrace, among others.||Location of users is also stored in encrypted form, thus it is less private than other decentralized approaches which do not store the user location.|
|Whisper Tracing ||This protocol uses ‘interaction IDs’ based on secured/hash exchanges between users in proximity to each other; interaction IDs are seen as more private than temporary IDs used in DP-3T and GAEN; it can work both in centralized and decentralized modes; no health authority is needed in the protocol chain to certify the users.||Still in research phase; might introduce long delays due to a long hash security key needed by the protocol; fake reports possible as no certification stage exists.|
5.3. Users’ Perception of the Usefulness of Decentralized Architectures
6. Privacy-Preservation Aspects in Contact Tracing and User Tracking
- Physical layer approaches: physical layer approaches for increased location privacy typically rely on some form of obfuscation, i.e., concealment, of user-based measurements, e.g., by increasing the estimation error in the RSS reported measurements . Nevertheless, such approaches are also likely to decrease the accuracy in detecting whether two users are in close vicinity of each other and may generate many false alarms or misdetections;
- Enhanced security keys for the generation of the temporary of ephemeral IDs; for example, attribute-based encryption based on multi-authorities/decentralization was proposed in , and homomorphic Paillier encryption with selective aggregation was proposed in ; the typical downside of such approaches is the long delay introduced during the generation of the encryption keys;
- Use of decentralized identifiers  are among the modern approaches for authentication of digital data; such approaches have not yet been investigated in the context of proximity detection, user location, or contact tracing;
- Blockchain concept: Blockchain is a type of Distributed Ledger Technology (DLT) where all transactions are recorded with a changeless cryptographic signature called a hash. In this case, any changes in the block are becoming apparent to the participant. To “lie” within a blockchain system, every block in the chain across all of the chain’s distributed versions should be altered. As a system, Blockchain works as a stable ledger that allows performing transactions in a decentralized mode, which is known to be a privacy-preserving solution. Blockchain-based applications spring up, covering various fields, including financial services, healthcare domain, and IoT, among others. Despite its popularity, there is still room for improvement in the blockchain technology, such security obstacles remaining to be overcome .
- Differential Privacy concept: Differential privacy  has been implemented in centralized Location-Based Service providers’ databases by adjusting partition structures of the current dataset on the spatial structure of the previous moment and adding Laplace noise. It proved to be a privacy-enhancing solution compared to obfuscated locations exclusively submitted by the users. As with background knowledge of a user’s obscured locations, and an attacker could still presume actual locations by carrying out long-term observation attacks. Moreover, as stated in , merging the differential privacy concept with other privacy-preserving solutions such as Blockchain proved to be a working scheme in the users’ location domain.
- Audits and aggregation of data: Auditing who accesses and publishes the patient data is mentioned in . In  one proposed method is using aggregate location data. However, knowing how many people are traveling from hotspots to nearby towns and villages would still reveal the virus’s possible spreading without personal data.
7. Energy-Efficiency Aspects
- Improved signal processing at the transmitter side, for example, by the optimized power amplifier to obtain high efficiency at low transmit power levels ;
- Improved signal processing at the receiver side—the authors of  used, for example, dynamic modeling via Markov chains for more efficient integration of sensors readings for positioning;
- Improved communications and/or localization protocols for example by optimized routing of the events or packets ;
- Ultra low-power communication technologies: low-power technologies, such as BLE, ZigBee, LoRa, etc., are essential for a lasting battery life, but decreasing even more the power consumption is a topic of active research under the umbrella of “ultra low-power” technologies such as wireless-powered networks with back-scattered communications , tunable impulse radio UWB technologies , or wearable technologies relying on sensors which use the electrostatic induction current generated by human motion ;
- Data compression methods for transmitting a lower amount of data by removing redundancies in data to be transmitted - while such methods have been vastly studied in the context of wireless communications, e.g., in  or via compressed sensing in , their applicability to user tracking and contact tracing is still to be determined;
- Approximate computing methods rely on trading accuracy for a lower power consumption , for example, by reducing the number of quantization bits of by approximating some tasks in the execution flow;
- Task offloading methods  rely on delegating/moving some of the more computationally demanding tasks to an edge or cloud server; such methods typically demand the presence of a centralized unit/server and therefore are not well suited to decentralized approaches. In addition, task offloading increases the wireless transmission delays and may hinder a real-time contact-tracing app’s viability.
- Energy harvesting [174,175] from surroundings such as ambient back-scattering communications  rely on collecting additional energy from the surrounding environment, such as due to reflections, interferences, and body movements, and transforming it into useful energy for the desired purpose. These kinds of methods have not yet been studied in the context of contact tracing or user tracking to the best of the authors’ knowledge.
8. Challenges to Overcome towards Mass Adoption of Contact-Tracing Applications
- Technical domain—refers to challenges and errors caused by the wireless propagation of signals, as well as errors caused by the transmitter and/or receiver devices, such as device calibration errors or errors due to shadowing effects;
- Medical domain—refers to challenges and errors in evaluating the probability of getting infected due to variability of the human factor/immune system, the variability of the impact of the contact duration on the outcomes, as well as other medical factors related to human metabolism and genes.
- Ethical domain—refers to challenges in adopting or imposing a new mobile application due to ethical constraints such as the GDPR regulations in Europe. For example, as emphasized above, decentralized semi-automated applications cannot serve as a standalone solution to respond to virus exposure, as human actions are further needed (from governments, healthcare personnel, citizens, etc.) for efficient and robust solutions. However, a decentralized approach promises to benefit best in synchronization with preventive measures, a well developed public healthcare system, and thorough compliance with the government recommendations.
8.1. Technical Domain
8.2. Medical Domain
8.3. Ethical Domain
9. Conclusions and Way Ahead
Conflicts of Interest
List of Acronyms
|4G||Fourth-generation cellular systems|
|5G||Fifth-generation cellular systems|
|AOA||Angle of Arrival|
|AOD||Angle of Departure|
|BLE||Bluetooth Low Energy|
|COVID-19||Coronavirus infectious disease 2019|
|DOA||Direction of Arrival|
|DLT||Distributed Ledger Technology|
|DP-PPT||Decentralized Privacy-Preserving Proximity Tracing|
|DP-3T||Decentralized Privacy-Preserving Proximity Tracing (another abbreviation for DP-PPT)|
|EPIC||Efficient Privacy-Preserving Contact Tracing|
|FDOA||Frequency Difference of Arrival|
|GAEN||Google/Apple Exposure Notification|
|GDPR||General Data Protection Regulation|
|GNSS||Global Navigation Satellite System|
|GPS||Global Positioning System|
|IMU||Inertial Measurement Unit|
|IoT||Internet of Things|
|LoRa||Long Range Internet of Things technology|
|LoRa||Long Range access|
|LTE||Long-Term Evolution (of cellular systems)|
|MIT||Massachussets Institute of Technology|
|NB-IoT||NarrowBand Internet of Things|
|PACT||Private Automated Contact Tracing|
|PEPP-PT||Privacy-Preserving Proximity Tracing|
|POA||Phase of Arrival|
|QR code||Quick Response code|
|RFID||Radio Frequency Identification|
|ROBERT||ROBust and privacy-presERving proximity Tracing|
|RSS||Received Signal Strength|
|RTT||Round Trip Time|
|SARS-COV-2||Severe acute respiratory syndrome coronavirus 2|
|SVM||Support Vector Machines|
|TCN||Temporary Contact Numbers|
|TDOA||Time Difference of Arrival|
|TOA||Time of Arrival|
|UWB||Ultra Wide Band|
|VLC||Visible Light Communications|
|WHO||World Health Organization|
|WPAN||Wireless Personal Area Network|
|WSN||Wireless Sensor Networks|
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Supplementary material provided by the authors in open access, with digital contact-tracing apps worldwide, https://sites.tuni.fi/survey-of-digital-solutions/.
See “Location Privacy Survey”, https://sites.tuni.fi/survey-of-digital-solutions/location-privacy-survey/.
|Authors||Addressing Contact-Tracing Solutions||Addressing Proximity-Detection Solutions||Addressing User Location-Tracking Solutions||Addressing Privacy Protocols||Addressing Energy Efficiency|
|Li and Guo||✓||✗||✗||✗||✗|
|Bolic et al.||✓||✓||✗||✗||✗|
|Ye et al.||✗||✗||✓||✗||✓|
|Chen et al.||✓||✗||✓||✗||✓|
|Kaptchuk et al.||✓||✗||✓||✓||✗|
|Li et al.||✓||✗||✓||✓||✗|
|Ahmed et al.||✓||✗||✓||✓||✓|
|Martin et al.||✓||✓||✗||✓||✗|
|Nasajpour et al.||✗||✓||✗||✗||✗|
|Sun et al.||✓||✗||✗||✓||✗|
|Hernández-Orallo et al.||✓||✗||✗||✗||✗|
|Reichert et al.||✓||✗||✗||✓||✗|
|Braithwaite et al.||✓||✗||✗||✗||✗|
|Hybrids||Range, phase, bearing, …||[72,73,74]|
|Ref.||Main Findings||Relevance for COVID-19|
|||Existing privacy challenges related to the use of Static IDs and possible data linkage issues. Different BLE signal intensity at the ISM bands, multipath interference and spatial blockage between devices in BLE-based contact tracing.||The survey provides a systematic mapping of global status for contact-tracing applications deployed worldwide. The authors perform a qualitative analysis and compare the amounts of active users for particular applications. No single solution is emphasized as the way to go ahead.|
|||Models of calibration and proximity accuracy with BLE RSS measurements.||BLE found as the best solutions nowadays for proximity detection; UWB suggested as future solution on smart phones.|
|||Magnetometer-based contact tracing.||Requires a centralized architecture and it needs a large number of magnetic field samples for reliable results.|
|||Efficient Privacy-Preserving Contact Tracing (EPIC) privacy-preserving protocol for any RSS-based contact tracing.||It relies on centralized server with encrypted information for better privacy protection; trustable servers are a must in such approaches.|
|||Decentralized Privacy-Preserving Proximity Tracing (another abbreviation for DP-PPT) (DP-3T) protocol based on BLE RSS.||This is one of the most popular contact-tracing protocols nowadays, as it relies on a decentralized architecture and it is also the source of inspiration for GAEN protocol.|
|||GAEN Exposure notification solution, supported by iOS and Android devices and relying on BLE RSS measurements.||Apps relying on GAEN protocol are currently the most downloaded mobile apps according to [36,38] and are the most promising to be widely adopted at long-term, due to their multi-device support, ease-of-installation, and decentralized architectures.|
|||ROBust and privacy-presERving proximity Tracing (ROBERT) is developed by PRIVATICS team from Fraunhofer Institute and INRIA as an open source hybrid (decentralized plus some robust centralized features) protocol using BLE; DESIRE is a similar protocol as ROBERT developed by INRIA researchers.||Still in the research phase; aims at collaboration between various governments towards a cross-country adoption.|
|Technology||Location-Tracking Capabilities||Contact-Tracing Capabilities||Proximity-Detection Capabilities||Examples of Wearables or Mobile Apps||Examples of Used Protocols|
Accent wristband 
BLE-enabled device [103,104]
TraceTogether app 
|ZigBee||TelosB motes ||N/A|
|Wi-Fi||VR headsets ||Cloud/centralized EPIC |
|UWB||UWB Sensors ||N/A|
|RFID||Wearable with RFID ||Cloud/centralized |
|GNSS||Sports wearables for football player tracking |
Comarch LifeWristband 
|LoRa||MoKo wearables ||Cloud/centralized|
|VLC/Li-Fi||VLC for wearable patient monitoring ||Cloud/centralized|
|Acoustic/Sound||Acoustic localization via smartphone ||Cloud/centralized|
|Infrared (IR)/LED||LED-based positioning ||Cloud/centralized|
|Magnetic sensors||Smartphone with magnetometer ||Cloud/centralized|
|Images/Webcams||Pedestrian proximity detection via webcams ||Cloud/centralized|
|5G||A 5G-based positioning testbed by Ericsson ||Cloud/centralized|
|Ref.||Main Purpose||Positioning Accuracy||Energy Consumption||Tested Deployment||Requirements|
|||User location and tracking based on BLE RSS||Mixed Indoors/Outdoors||Centralized architecture based on existing BLE infrastructure|
|||Contact-tracing tool for livestock disease control||N/A||Outdoor livestock movements extracted from Swedish Board of Agriculture||Relies on GPS-based location tracking|
|[121,122]||Contact tracing, proximity detection, and co-presence detection||Student campus||Uses existing infrastructure such as various mobile sensors, e.g., magnetometer, accelerometer, and WiFi networks|
|||Contact-tracing calibration data for BlueTrace protocol||N/A||Mixed Indoors/Outdoors||Centralized architecture based on BLE|
|||BLE-based dataset and software for user positioning and proximity detection||Indoors||Centralized architecture based on BLE|
|||BLE and UWB data for calibration in user tracking||N/A||Indoors||Centralized architecture based on BLE and UWB|
|||BLE and Wi-Fi data for user location and tracking||Indoors||Centralized architecture based on BLE and Wi-Fi|
|||UWB data for user location and tracking||Indoors||LOS between anchors and tags|
|||Dataset for the estimation of COVID-19 transmission dynamics;|
dataset collected in Tunisia during Feb-May 2020.
|||R-based software for modeling the periods of “infectiousness” of infected persons of generic infectious diseases.|
|||A collection of links to more than 30 datasets for COVID-19 data analysis.|
|||a limited dataset with various indicators, such as COVID-19 fatality rates, number of hospital beds, emergency investment in healthcare, etc.|
|||A set of 100 computed tomography scans from 40 COVID-19 patients.|
|||20 audio samples of coughs with 10 of them coming from COVID-19-positive patients.|
|||Images with chest scans and clinical data from COVID-19-positive patients,|
collected by University of Arkansas for Medical Sciences.
|||A dataset with location information extracted from tweets related to the COVID-19 pandemic.|
|BlueTrace ||Open source; it does not collect location data; users mobile phones are collected only temporarily; it relies on temporary user IDs for users registrations and proximity detection.||It has some limitations on iOS devices; it is sensitive to replay/relay attacks.|
|EPIC ||It aims at a high level of privacy with a centralized approach.||In research phase; not yet implemented for real-field testing.|
|PEPP-PT ||Joint effort of several teams in Europe; meant to be available as open source for Android and iOS devices; user location data not collected.||Moderate robustness to privacy and security attacks.|
|TraceSecure ||More secure than TraceTogether.||In research phase; not yet implemented for real-field testing; high overheads at server side.|
|TraceTogether ||Minimal infrastructure needed as it relies on BLE RSS data.||Low privacy preservation.|
|Prefer not to disclose||9||4.7%|
|70 or more||7||3.7%|
|Prefer not to disclose||7||3.7%|
|The living area|
|Prefer not to disclose||3||1.6%|
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Shubina, V.; Holcer, S.; Gould, M.; Lohan, E.S. Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era. Data 2020, 5, 87. https://doi.org/10.3390/data5040087
Shubina V, Holcer S, Gould M, Lohan ES. Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era. Data. 2020; 5(4):87. https://doi.org/10.3390/data5040087Chicago/Turabian Style
Shubina, Viktoriia, Sylvia Holcer, Michael Gould, and Elena Simona Lohan. 2020. "Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era" Data 5, no. 4: 87. https://doi.org/10.3390/data5040087