Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review
Abstract
:1. Introduction
2. Related Work
3. Research Method
3.1. Research Questions
- MRQ
- What are the possible gaps or issues in Cloud Platforms for positioning and navigation in GNSS-denied environments?
- RQ1
- Are the main computing paradigms used in current indoor positioning platforms?This research question permits to identify if the current indoor positioning platforms are using the main computing paradigms. This question allows us to determine future trends regarding indoor positioning platforms and computing paradigms.
- RQ2
- What network protocols do the current platforms use to provide reliable services?This question addresses the need to know which network protocols are used in the current indoor positioning platforms. Additionally, this research question helps to identify strengths and weaknesses of the used communication protocols in the scope of indoor positioning.
- RQ3
- Do the current platforms permit heterogeneous positioning technologies for GNSS-denied scenarios?This research question allows us to determine the current platforms’ flexibility to support diverse position technologies.
- RQ4
- Do the current platforms adapt to different scenarios?As the diversity of indoor scenarios are currently considered a main challenge for providing positioning indoors, this research question helps to identify the limitations of the current systems.
- RQ5
- What were the improvements done in similar studies?This research question aims to identify the main contribution of the studies analysed and the current challenges in indoor positioning platforms.
- RQ6
- How is the standardization aspect dealt with on different platforms?Standardization is key to providing a more reliable and high-quality indoor positioning platform. Thus, this research question aims to identify if the current systems consider the existing standards for IPS in different dimensions.
3.2. Keywords
3.3. Query
- Web Of Science Query:
- SCOPUS Query:
3.4. Study Selection
3.4.1. Stage 1: Identification
3.4.2. Stage 2: Screening and Selection Criteria
- IC1
- Full research works written in English
- IC2
- Research works dealing with platforms supporting positioning
- EC1
- Works not dealing with any computing paradigm (e.g., Cloud computing) or GNSS-denied scenarios
- EC2
- Works not published in peer-reviewed international journals or conference proceedings
- EC3
- Studies not dealing with wearable devices (we consider smartphones as wearable devices)
3.4.3. Stage 3: Eligibility
3.4.4. Stage 4: Included
3.5. Main Figures for the PRISMA Process in the Current Review
3.6. Overview of the Selected Studies
3.7. Data Extraction
4. Results
4.1. Computing Paradigms Used in Current Indoor Positioning Platforms (RQ1)
4.1.1. Cloud Computing (CC)
4.1.2. Mobile Cloud Computing (MCC)
4.1.3. Fog Computing (FC)
4.1.4. Mist Computing
4.1.5. Edge Computing
4.1.6. Multi-Access Edge Computing
4.2. Network Protocols Used in Current Cloud-Based Indoor Positioning Platforms (RQ2)
4.2.1. Communication Protocols
4.2.2. Security Protocols
4.2.3. IoT Protocols
4.2.4. Other Protocols
4.3. Do the Current Platforms Permit Heterogeneous Positioning Technologies for GNSS-Denied Scenarios? (RQ3)
4.3.1. Radio Frequency Technologies
IEEE 802.11 Wireless LAN (Wi-Fi)
Bluetooth
Ultra Wideband (UWB)
Cellular/Mobile Networks
IEEE 802.15.4—Zigbee
Radio Frequency Identifier (RFID)
4.3.2. Magnetic Field
4.3.3. Inertial Technology
4.3.4. Computer Vision-Based Technology
4.3.5. Sound-Based Technologies
4.3.6. Optical Technologies
4.4. Do the Current Platforms Adapt to Different Scenarios? (RQ4)
4.4.1. Platform
4.4.2. Environment
4.4.3. Client
4.5. What Improvements Were Done in Similar Studies (RQ5)
- Efficient Computation [45,48,52,61,68,74,85,94,102,115]: It consists of improving the methods or algorithms used in mobile devices and the Cloud in order to decrease the use of computational resources. To reduce the computational load in mobile devices, the authors offload specific processes to the Cloud or other computational paradigms (see Section 4.1). Moreover, researchers have proposed some optimizations to traditional algorithms and databases in order to improve their efficiency and time response.
- Interoperability [54,78,79,95]: It is the capability to interact with other systems, platforms, or devices through its interfaces. Thus, they can exchange information simultaneously, allowing them to integrate with each other and provide synchronous communication. This is especially valuable in light of the heterogeneity of the deployed IPS, and the need for position and localisation services in other areas such as healthcare systems, i.e., if an ILS only shares the estimated position and does not provide interfaces to share raw data, these raw data cannot be integrated into a sensor fusion approach.
- Position [55,56,58,74,85,123]: The articles studied proposed different technologies, techniques, and methods to reduce the error in the position estimation (see Section 4.3). Additionally, the use of computing paradigms (e.g., CC, FC, MCC, EC) have been used in some articles to support the positioning process.
- Usability [8,13,39,40,43,77,79,80,83,96,97,106,111,124]: It is linked to the user experience providing a platform easy to use that satisfy the user’s requirements. For instance, Yeh et al. [39] developed a Cloud platform for parking services (e.g., search parking places, reservation, navigation), providing a useful and efficient system to end-users which satisfies the need for parking systems. Additionally, some of the applications or frameworks are oriented to developers or users in general who have limited knowledge of positioning systems and programming, allowing fast development of a new application.
- Localisation [7,10,16,42,58,72,75,76,82,107,110,114,121,125,126]: Similar to position, localisation aims to provide better localisation accuracy by combining different techniques, technologies, and algorithms. In the current studies, localisation techniques have been used to locate people in different environments such as shopping malls, universities, hotels, among others.
- Cost [45,67,88,94,105]: The cost is one of the prime considerations when researchers and companies develop their indoor positioning platforms. That is why technologies like Wi-Fi and BLE have been chosen, despite their poor accuracy compared to, for instance, UWB. Moreover, the use of Cloud Computing (CC) offers pay-as-you-go, enabling users to pay only for the services and resources procured.
- Navigation [11,46,55,59,69,99,111,112,120,127]: Many of the current studies are focused on improving or providing navigation service. For instance, to provide navigation services for shopping malls or select the best route to emergency exits. The navigation service is also used to choose the least congested route to a particular place.
- Low latency [64,106,111]: This is a critical point in time-sensitive networks, and it is related to the delay in the data transmission. It is the time that it takes a message to go from the source to the target. It is specially required for real-time communications. Thus, in order to provide real-time indoor positioning/localisation/navigation applications, the authors use different technologies, techniques and computing paradigms (see Section 4.1 and Section 4.3) in order to reduce the latency, for instance, FC is used for facing the latency problems caused by the large number of connections to the IPS.
- Energy efficiency [58,59,103,116,128]: Several measures have been taken to reduce the energy consumed while performing a task. However, the main method used in the current studies is to offload certain processes from the mobile device to computing paradigms. This allows the use of IPS in low-profile devices such as IoT and wearable devices.
- Reliability [8,110]: To provide reliable positioning and localisation information in a variety of environments with a minimum of errors, providing a high-quality service. However, providing reliable systems is not easy given the complexity of IPS/ILS. For instance, the authors of [110] implemented their system at a building construction site in three scenarios in order to test the accuracy, latency, and system reliability, obtaining a precision of 85% and accuracy of 88%, approximately. Ref. [8] developed a reliable localization web platform providing remote access to numerous users.
- Tracking: It determines the current user position in real-time with minimum delays. In order to provide tracking services, the authors use certain algorithms and technologies. For example, Sujin et al. [121] used a stochastic model, namely the Markov model, which is used for device tracking.
- Evaluation: It is one of the important aspect for indoor positioning platforms in order to determine the performance and if it fulfils all the technical and user requirements. This evaluation could be carried out in simulated environments or real environments following a specific standard similar to the platform developed by Haute et al. [43].
- Privacy [70]: Given that some of the information collected to train positioning and localisation models might contain sensitive data, the authors provide some mechanism to protect the privacy of the user information during the process in any of the computing paradigms. For instance, Zhang et al. [70] applied differential privacy to Edge-based IPS. This research aims to protect user information when it is used to train positioning localisation models in the EC.
- Security [63,71,91,119,129]: Various techniques, protocols, or devices have been developed over the year to protect user information. Thus, protocols like HTTPS are widely used to provide safe data transfer. Moreover, other mechanisms have been adopted to determine anomalies during the data collection and verify security issues in indoor positioning platforms.
4.6. How Is the Standardization Aspect Focused on Different Platforms? (RQ6)
4.6.1. Maps
4.6.2. Position Technologies
4.6.3. Evaluation Methods
4.6.4. Software Architecture
5. Discussion of the State of the Art
5.1. Computing Paradigms and Improvements (RQ1 and RQ5)
5.2. Network Protocols RQ2
5.3. Indoor Positioning Technologies (RQ3)
5.4. Cloud-Based Indoor Positioning Platforms—Scenarios (RQ4)
5.5. Standardization (RQ6)
5.6. Current Challenges
5.6.1. Challenges Related to Computing Paradigms
5.6.2. Challenges Related to Software
5.6.3. Challenges Related to Standardization
5.7. Future Trends
6. Threats to Validity
6.1. Methodology Selection
6.2. Primarily Studies Selection
6.3. Selection of Studies
6.4. Replicability
7. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AMQP | Advanced Message Queuing Protocol |
AoA | Angle of Arrival |
AP | Access Point |
API | Application Programming Interface |
AR | Augmented Reality |
BIM | Building Information Modeling |
BLE | Bluetooth Low Energy |
CC | Cloud Computing |
CNN | Convolutional Neural Networks |
CoAP | Constrained Application Protocol |
CSI | Channel State Information |
D2D | Device to Device |
DR | Dead Reckoning |
EC | Edge Computing |
eMBB | enhanced Mobile Broadband |
FC | Fog Computing |
GNSS | Global Navigation Satellite System |
GPS | Global Positioning System |
GSM | Global System for Mobile Communications |
HTTP | HyperText Transfer Protocol |
HTTPS | Hypertext Transfer Protocol Secure |
IaaS | Infrastructure as a Service |
ILS | Indoor Location System |
IMU | Inertial Measurement Units |
iNaaS | Indoor Navigation as a Service |
INS | Indoor Navigation System |
IoT | Internet of Things |
IP | Internet Protocol |
IPS | Indoor Positioning System |
ISO | International Organization for Standardization |
KF | Kalman filter |
k-NN | k-Nearest Neighbor |
LBS | Location-Based Services |
LoST | Location-to-Service Translation Protocol |
LSTM | Long short-term memory |
LTS | Localization and Tracking System |
M2M | Machine to Machine |
MAC | Media Access Control |
MC | Mist Computing |
MDS | Multidimensional Scaling |
MEC | Multi-access Edge Computing |
ML | Machine Learning |
mMTC | massive Machine Type Communications |
MCC | Mobile Cloud Computing |
MSA | Microservice Architecture |
MVC | Model–view–controller |
MVVM | Model–view–viewmodel |
MQTT | Message Queuing Telemetry Transport |
NFC | Near-field Communication |
NFV | Network Functions Virtualization |
NIST | National Institute of Standards and Technologies |
OBEX | OBject EXchange |
OGC | Open Geospatial Consortium |
OS | Operating System |
OSI | Open System Interconnection |
PaaS | Platform as a Service |
PDR | Pedestrian Dead Reckoning |
P-FP | Probabilistic FingerPrinting |
POI | Point-of-Interest |
PRISMA | Preferred Reporting Items for Systematic reviews and Meta-Analyses |
QoE | Quality of Experience |
QoS | Quality of Service |
REST | REpresentational State Transfer |
RF | radio frequency |
RFID | Radio Frequency Identifier |
RSS | Received Signal Strength |
RSSI | Received Signal Strength Indicator |
SaaS | Software as a Service |
SIP | Session Initiation Protocol |
SLA | Service Level Agreement |
SOAP | Simple Object Access Protocol |
SOA | Service Oriented Architecture |
SLAM | Simultaneous Localization and Mapping |
SSL | Secure Sockets Layer |
SVM | Support Vector Machine |
TCP | Transport Control Protocol |
TDoA | Time Difference of Arrival |
TLS | Transport Layer Security |
ToA | Time of Arrival |
UDP | User Datagram Protocol |
UHF | Ultra High Frequency |
URI | Uniform Resource Identifier |
URLLC | Ultra-Reliable Low Latency Communications |
UWB | UltraWideband |
VLC | Visible Light Communication |
VHF | Very High Frequency |
VR | Virtual Reality |
Wi-Fi | IEEE 802.11Wireless LAN |
XML | Extensible Markup Language |
XMPP | Extensible Messaging and Presence Protocol |
Appendix A. Parameters Analised
Article | Year | Technology | Technique | Algorithm | Indoor | Outdoor | Area | Metric/Error | Protocol(S)/Interfaces | Standard |
---|---|---|---|---|---|---|---|---|---|---|
[39] | 2015 | RFID | N/A | ✓ | ✓ | N/A | N/A | N/A | N/A | |
[83] | 2015 | Inertial Sensors, Camera | Fusion techniques | Filter-base (low and high-pass filter) | ✓ | ✗ | N/A | N/A | WebSocket, HTTP | N/A |
[43] | 2015 | N/A | Range based, Range free, Fingerprinting | N/A | ✓ | ✗ | N/A | Mean error m to m | REST | ISO/ICE 18305:2016 |
[8] | 2015 | Wi-Fi | Fingerprinting, ToA | LQI, Pompeiu-Hausdorff | ✓ | ✗ | 22.5 m2, 11 m2 and 5 m2 per point | Mean error m to m | HTTP, API | N/A |
[40] | 2016 | Bluetooth | N/A | N/A | ✓ | ✓ | N/A | N/A | HTTP, API | Cloud-native |
[127] | 2016 | N/A | Path planing | Multi access Point (MaP algorithms) | ✓ | ✗ | N/A | N/A | N/A | N/A |
[110] | 2016 | Wi-Fi, RFID | Proximity | N/A | ✓ | ✗ | 32 m × 12 m and 21 m × 20 m | Accuracy 88.1% | N/A | BIM |
[61] | 2016 | Wi-Fi | Fingerprinting | Probabilistic-Fingerprinting (P-FP) | ✓ | ✗ | N/A | N/A | N/A | N/A |
[77] | 2016 | Bluetooth | N/A | N/A | ✓ | ✓ | N/A | N/A | REST API | OGC |
[78] | 2016 | N/A | N/A | k-NN | ✓ | ✗ | N/A | N/A | REST API | SOA |
[135] | 2016 | ZigBee | Multilateration | N/A | ✓ | ✗ | N/A | N/A | REST API | N/A |
[102] | 2017 | Wi-Fi | Fingerprinting | k-NN | ✓ | ✗ | N/A | N/A | HTTPS | N/A |
[50] | 2017 | Bluetooth | Proximity, Fingerprinting | k-NN | ✓ | ✗ | N/A | N/A | API | N/A |
[97] | 2017 | Wi-Fi, Mobile Network | Statistical Approximation, AAL | N/A | ✓ | ✓ | N/A | N/A | LoST | Cloud-native |
[123] | 2017 | Wi-Fi, Inertial Sensors, Bluetooth, Mobile Network | Deep Learning, Signal visualization, Scene Analysis, Triangulation | N/A | ✓ | ✓ | N/A | COEX env. Mean error 4.16 m, Store 3.54 m | N/A | N/A |
[67] | 2017 | Wi-Fi | Probabilistic, Bayesian theory | N/A | ✓ | ✗ | 62.22 m × 10.23 m | Average error from 1 m to 2 m | N/A | N/A |
[120] | 2017 | Bluetooth, Inertial Sensors, Mobile Network, Wi-Fi, Camera | Path planning | N/A | ✓ | ✓ | Indoor 175 m2, Outdoor 15 km, 125 m2 | 1–3 m | API | |
[106] | 2017 | Bluetooth | N/A | N/A | ✓ | ✗ | N/A | N/A | N/A | BIM |
[111] | 2017 | Camera, RFID | ToF | N/A | ✓ | ✓ | N/A | NA | N/A | N/A |
[121] | 2017 | N/A | Probabilistic | Markov | ✓ | ✗ | 12 m × 12 m | N/A | N/A | N/A |
[59] | 2017 | Camera, Inertial Sensors | Image Based, Structure from Motion (SfM) technique, Path planning | N/A | ✓ | ✓ | 150 m2 | 1 m | N/A | Cloud-native |
[41] | 2017 | Wi-Fi, Bluetooth | Fingerprinting, ML, Trilateration | SVM | ✓ | ✗ | N/A | Average distance error 11.48 ft. | N/A | N/A |
[136] | 2017 | N/A | ML | Genetic Algorithm | ✓ | ✓ | N/A | Accuracy > 98% | Spanish Inquisition Protocol (SIP) | N/A |
[105] | 2018 | Bluetooth | Geometric approach, triangulation | N/A | ✓ | ✗ | N/A | N/A | N/A | N/A |
[62] | 2018 | ZigBee, Bluetooth | Proximity, Waypoint-based navigation | N/A | ✓ | ✗ | 3 m | OBEX, BR/EDR | N/A | |
[119] | 2018 | Bluetooth, Wi-Fi | Probabilistic | N/A | ✓ | ✗ | 8 m × 8 m and 44 m × 44 m | Maximum error 5.94% | N/A | N/A |
[88] | 2018 | Wi-Fi | Fuzzy logic, Trilateration, Fingerprinting | Genetic algorithms | ✓ | ✗ | N/A | Mean error ≈ 2.11 m ± 0.6 m | UDP/IP and TCP/IP | N/A |
[42] | 2018 | Bluetooth | Proximity | N/A | ✓ | ✗ | N/A | N/A | HTTP | SOA |
[73] | 2018 | Wi-Fi, Bluetooth, RFID, Cellular | Fingerprint, Proximity | N/A | ✓ | ✓ | N/A | Mean error 4.62 m ± 0.31 m | HTTP/OpenFlow | N/A |
[52] | 2018 | Bluetooth, Inertial Sensors | ML, image processing | Brute-Force Marching and ORB descriptors | ✓ | ✗ | N/A | N/A | HTTP, API | N/A |
[48] | 2018 | Bluetooth | ML | k-NN, SVM | ✓ | ✓ | 64 m2 | 1 m | Web Service | Cloud-native |
[124] | 2018 | N/A | N/A | N/A | ✓ | ✓ | N/A | N/A | N/A | N/A |
[137] | 2018 | N/A | N/A | Hidden Markov Model | ✓ | ✗ | N/A | N/A | N/A | N/A |
[53] | 2018 | Camera, Ultrasound | inertial Sensors | N/A | ✓ | ✗ | Accuracy > 97% | N/A | N/A | |
[45] | 2019 | Wi-Fi | ML | Support Vector Regression | ✓ | ✗ | In a mall, 2500 m2, and 562,000 m2 | N/A | RestFUL web service API | N/A |
[72] | 2019 | Wi-Fi, Bluetooth, ZigBee | N/A | RACIL algorithm | ✓ | ✗ | Exp. 100 m2, Real 2 m × 40 m | Simulated 0.2 m to 1.1 m, Real 0.4 m to 1.6 m | N/A | N/A |
[103] | 2019 | Wi-Fi | ML, Fingerprinting | Multi-Objective Evolutionary Algorithm, W k-NN | ✓ | ✗ | N/A | Average error 1 m | N/A | N/A |
[91] | 2019 | Wi-Fi, Bluetooth, | Proximity | N/A | ✓ | ✗ | N/A | N/A | TLS | N/A |
[16] | 2019 | Bluetooth, Wi-Fi | ML | LSTM | ✓ | ✗ | 68 m × 16 m, 34 m × 16 m, 26.5 m × 16 m, 19 m × 16 m | N/A | MQTT | N/A |
[99] | 2019 | Bluetooth, Wi-Fi, Inertial Sensors | Fingerprinting, PDR, Map Matching | Particle Filter | ✓ | ✗ | N/A | Mean error 2.34 m | N/A | N/A |
[70] | 2019 | Wi-Fi | ML | ELM-based | ✓ | ✗ | 12 m × 6 m, 8.7 m × 55 m | 15 m | N/A | N/A |
[64] | 2019 | Bluetooth | Proximity | N/A | ✓ | ✗ | N/A | N/A | MQTT, Mosquito | N/A |
[85] | 2019 | Wi-Fi | Probabilistic | Motley Keenan | ✓ | ✗ | N/A | N/A | OpenFlow | N/A |
[112] | 2019 | Wi-Fi, Inertial Sensors, Geomagnetic | Deterministic | k-NN, Dynamic Time Warping (DTW),PF (Particle Filter) | ✓ | ✗ | N/A | N/A | N/A | |
[116] | 2019 | Light, Inertial Sensors | N/A | Peak Intensity detection, IIR, Filter, DTW | ✓ | ✗ | 1000 m2, 20,000 m2, 800 m2 | Accuracy 98% | N/A | N/A |
[74] | 2019 | Wi-Fi | Neural Networks, Image Based | Genetic Algorithm | ✓ | ✓ | ≈ 4 km | 1 m to 5 m | MQTT | N/A |
[95] | 2019 | Bluetooth | ML, Probabilistic, Winsorization technique | Trimmed mean | ✓ | ✓ | 10 m × 4 m, 20 m × 2 m | 1 m | MQTT, HTTP | WGS84 |
[125] | 2019 | Wi-Fi | Triangulation | N/A | ✓ | ✗ | 120 m × 120 m | < 5.09 m | N/A | N/A |
[76] | 2019 | UWB,Inertial Sensors,Wi-Fi | ML, Markov | N/A | ✓ | ✗ | 39 m × 18 m | Accuracy 90% | WebSocket, HTTP | N/A |
[54] | 2019 | Wi-Fi, Inertial Sensors | Proximity | Nearest-checkpoint identification | ✓ | ✓ | N/A | N/A | HTTPS, REST API | SOA |
[75] | 2019 | Wi-Fi | Experience-based | Heuristic algorithm, GBOMD, EBOP | ✓ | ✗ | N/A | N/A | N/A | N/A |
[79] | 2019 | Wi-Fi | Fingerprinting | k-NN, etc. | ✓ | ✗ | N/A | N/A | REST API | N/A |
[128] | 2019 | Wi-Fi, Inertial Sensors | N/A | Light-Weight Magnetic-Based Door Event Detection method | ✓ | ✗ | N/A | Detection accuracy 90% | N/A | N/A |
[56] | 2019 | Wi-Fi | Fingerprinting | W k-NN | ✓ | ✗ | 42 m × 12 m | Average error 3.8 m | MQTT, HTTP | N/A |
[58] | 2019 | Bluetooth | N/A | Bounding Box Algorithm | ✓ | ✗ | 36 m × 36 m | Average error 1.55 m | N/A | N/A |
[82] | 2020 | Bluetooth | Proximity | N/A | ✓ | ✓ | 42.5 m2 | Mean accuracy 97.7% | API, HTTP | N/A |
[80] | 2020 | Bluetooth | N/A | N/A | ✓ | ✓ | N/A | N/A | HTTP, Rest | N/A |
[46] | 2020 | Bluetooth | Proximity | N/A | ✓ | ✗ | N/A | ≈2.6 m | MQTT | Cloud-native |
[94] | 2020 | Audible Sound | ML | k-NN, SVM, Naïve Bayes (NB) | ✓ | ✗ | - | Accuracy 71% | MQTT | N/A |
[7] | 2020 | Bluetooth | ML, Trilateration | N/A | ✓ | ✗ | 12 m × 16 m | RMSE 0.86 m | MQTT | N/A |
[63] | 2020 | Wi-Fi | Markov model | N/A | ✓ | ✗ | N/A | N/A | N/A | N/A |
[114] | 2020 | Camera | AR technique | N/A | ✓ | ✓ | N/A | N/A | API | N/A |
[11] | 2020 | Camera, Ultrasound | Fuzzy logic, image processing | N/A | ✓ | ✓ | N/A | N/A | N/A | N/A |
[13] | 2020 | ZigBee | N/A | Oriented FAST and Rotate BRIEF (ORB) algorithm | ✓ | ✗ | N/A | N/A | N/A | N/A |
[10] | 2020 | UWB | ML, image processing | Brute-Force Marching and ORB descriptors | ✓ | ✗ | 10 m × 10 m × 3.3 m | N/A | N/A | N/A |
[68] | 2020 | Camera | Visual-SLAM | N/A | ✓ | ✗ | N/A | Mean error ≈ 20 cm | N/A | N/A |
[69] | 2020 | Bluetooth | ML, Proximity, Trilateration, | LSTM, RNN | ✓ | ✓ | N/A | N/A | N/A | N/A |
[107] | 2020 | Bluetooth | ML | N/A | ✓ | ✓ | 2.50 m × 3.29 m, 2.50 m × 1.00 m, 2.34 m × 2.21 m, 5.60 m × 7.80 m, 1.60 m × 5.60 m | Average error 35.23 cm ± 11.86 cm | MQTT | N/A |
[96] | 2020 | Wi-Fi, Bluetooth, Mobile Network | N/A | k-NN, k-d Tree | ✓ | ✓ | 1.48 km2 | N/A | WebSocket, XMPP | N/A |
[55] | 2020 | Wi-Fi, Inertial Sensors, Bluetooth, UWB | ML | N/A | ✓ | ✓ | N/A | N/A | SSL, RestFUL API | MSA |
[126] | 2020 | Wi-Fi | ML, Fingerprinting | Manifold Alignment algorithm | ✓ | ✗ | 68.9 ft × 52.5 ft | N/A | N/A | N/A |
[71] | 2020 | N/A | ML, Fingerprinting | k-NN, SVM, NN, RF, MLP | ✓ | ✗ | N/A | N/A | N/A | N/A |
[115] | 2020 | Camera | ML | DNN | ✓ | ✓ | 42 m × 37 m, 17 m × 13 m, 8 m × 5 m | 60 cm | HTTP | N/A |
[49] | 2020 | Wi-Fi | multidimensional spatial similarity (MDSS), k-NN | N/A | ✓ | ✗ | 10 m × 10 m | Positioning error from 0.037 to 0.269 m | N/A | N/A |
[108] | 2020 | Mobile Network | eMBB, mMTC, URLLC | N/A | ✓ | ✗ | N/A | N/A | N/A | Cloud-native |
[138] | 2021 | Bluetooth | ML | ANN-SVM, KWNN | ✓ | ✗ | N/A | Accuracy > 91% | N/A | N/A |
[66] | 2021 | Wi-Fi | Fingerprinting | kNN, RLAEW | ✓ | ✗ | N/A | Mean error 2.67 m | N/A | N/A |
[139] | 2021 | Wi-Fi | Fingerprinting | Reputation Mechanism | ✓ | ✗ | N/A | N/A | N/A | N/A |
[65] | 2021 | Wi-Fi | Fingerprinting | Dynamic Routing Algorithm of CapsNet | ✓ | ✗ | N/A | Average localization error 7.93 m | N/A | N/A |
[12] | 2021 | Light | N/A | Visible Light Positioning algorithm | ✓ | ✗ | 3.3 m × 3.15 m | Positioning error from 3 to 6 m | HTTP | N/A |
[140] | 2021 | Wi-Fi | N/A | classical multidimensional scaling (CMDS) | ✓ | ✗ | 2400 m2 | 80 percentil 3 m | N/A | N/A |
[113] | 2021 | Inertial Sensors | Pattern matching technique | Dijkstra’s algorithm | ✓ | ✗ | N/A | mean error 7.39 m | HTTP, API | N/A |
[141] | 2021 | Bluetooth | N/A | Levenber-Marquardt algorithm | ✓ | ✗ | 5 m × 5 m approx. | Mean error < 1.7m | N/A | N/A |
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Approach | |||||||||
---|---|---|---|---|---|---|---|---|---|
Article | Year | Applications | Technologies | Techniques | Methods | Cloud-Based | Device-Based | Standards | Protocols |
[22] | 2017 | ✗ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[27] | 2018 | ✓ | ✓ | ✓ | ✓ | ✵ | ✓ | ✗ | ✗ |
[25] | 2018 | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
[23] | 2018 | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |
[5] | 2019 | ✓ | ✓ | ✓ | ✓ | ✵ | ✓ | ✗ | ✗ |
[9] | 2019 | ✗ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[6] | 2019 | ✓ | ✓ | ✓ | ✓ | ✵ | ✓ | ✗ | ✓ |
[30] | 2019 | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[28] | 2019 | ✗ | ✗ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[24] | 2020 | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[21] | 2020 | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
[26] | 2020 | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
[29] | 2021 | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✗ | ✗ |
our review | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Keyword Infrastructure | Keywords Environment | Keywords System |
---|---|---|
Cloud Computing | Indoor * | Position * |
Edge Computing | Location | |
Fog Computing | Localisation | |
MIST Computing | ||
Platform |
Group | Technology/Feature | Max. Range | Accuracy * | Power Cons. |
---|---|---|---|---|
Cellular [117] | 500 m–80 km a | <50 m [9] | Moderate-low | |
Wi-Fi [118] | < 100 m b | average > 1 m [67,88,103] | Moderate | |
Bluetooth [7,58,104] | v2.1–4.0 → 100 m, v5.0 → 400 m | average > 1.5 m [46,58] | Low | |
RF | UWB [9] | 10–20 m | median < 50 cm [9] | Low |
Zigbee [117] | 100 m | median < 5 m [9] | Low | |
RFID | 200 m | median < 3 m [110] | Low | |
Optical | Light | - | - | Low |
Vision | Camera | - | average ≈ 20 cm [68] | High |
Sound | Ultrasound [6] | <20 m | median < 10 cm [9] | Low |
Audible Sound | - | - | Low | |
Inertial sensors | Gyroscope, accelerometer, etc. | - | <5 m [110] c | Low |
Magnetic Field | - | - | median < 5 m [9] | Low |
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Share and Cite
Quezada-Gaibor, D.; Torres-Sospedra, J.; Nurmi, J.; Koucheryavy, Y.; Huerta, J. Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review. Sensors 2022, 22, 110. https://doi.org/10.3390/s22010110
Quezada-Gaibor D, Torres-Sospedra J, Nurmi J, Koucheryavy Y, Huerta J. Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review. Sensors. 2022; 22(1):110. https://doi.org/10.3390/s22010110
Chicago/Turabian StyleQuezada-Gaibor, Darwin, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, and Joaquín Huerta. 2022. "Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review" Sensors 22, no. 1: 110. https://doi.org/10.3390/s22010110