Collaborative Indoor Positioning Systems: A Systematic Review
Abstract
:1. Introduction
- Systematically collecting and analyzing research works related to CIPSs;
- Identifying and classifying the technologies, techniques, and methods applied;
- Identifying and classifying the computation architectures and infrastructures required for positioning;
- Identifying and describing the types of evaluation performed;
- Analyzing and discussing the results, in order to provide an overview of CIPS, and to uncover trends, challenges, and gaps in this research field.
2. Background
2.1. Indoor Positioning Systems
2.2. Indoor Positioning Technologies
2.3. Indoor Positioning Techniques
2.4. Indoor Positioning Methods
2.5. Collaborative Indoor Positioning Systems
- Case 1 aims to enhance the position accuracy of User 5, which has large uncertainty. The CIPS applies EKF to integrate the ranging information from Users 2 and 3 to estimate a better position.
- Case 2 aims to determine the position of User 4, who is not able to self-determine its position as it is far from the Wi-Fi area. The CIPS applies EKF to integrate the ranging information from Users 1–3 to estimate the position even if the non-collaborative part fails.
3. Research Methodology
3.1. Research Questions
- RQ1: What are the infrastructures, architectures, technologies, techniques, and methods (also called algorithms) used in/for CIPSs?
- RQ2: In which combination are technologies, techniques, and methods used in/for CIPSs?
- RQ3: How have CIPSs been evaluated, and what are the metrics used?
- RQ4: What are the limitations, current trends and gaps, and future research avenues that have been reported?
3.2. Inclusion and Exclusion Criteria
3.2.1. Inclusion Criteria
- IC1: Any full, primary research article written in English and published in a peer-reviewed international journal or conference proceedings.
- IC2: Any article that explicitly presents a Collaborative Indoor Positioning System for human use.
3.2.2. Exclusion Criteria
- EC1: Any articles that are not full papers (e.g., short papers, demo papers, extended abstracts), or are not primary research (e.g., reviews, surveys), or are not published in a peer-reviewed international conference or journal (e.g., white books, blog posts, workshop papers).
- EC2: Any articles that do not propose or analyze as main topic at least one CIPS for providing a user’s indoor position (e.g., non-collaborative systems, outdoor systems, algorithms outside the context of a CIPS) or target non-human use (e.g., aerial drones, underwater robotic systems).
- EC3: Any articles that do not consider the definition of collaboration as the action of joint working between neighboring actors to provide positioning (e.g., sensor fusion, data fusion algorithm, stand-alone device with multi-sensors cooperation).
3.3. Study Selection Process
3.4. Classification of the Studies
3.4.1. Non-Collaborative and Collaborative Phases
- Technologies. This category covers the technologies used to calculate the position on one hand (non-collaborative part) and to provide collaboration between users or nodes on the other hand. In one CIPS, the same or different technologies may be used for either part. Examples of technologies include IMU, Radio-Frequency Identification (RFID), and VLC for the non-collaborative part, and Bluetooth Wi-Fi and UWB for both parts.
- Techniques. Includes the techniques used for positioning and collaboration between users or nodes. Examples of techniques include fingerprinting, Dead Reckoning (DR), and Time of Arrival/Flight (ToA/ToF) for the non-collaborative part, and position sharing, Two-way Ranging (TWR), and Time Difference of Arrival (TDoA) for the collaborative part. We define techniques as the way certain technologies and derived data are organized and used to achieve positioning.
- Methods. Includes the algorithms and mathematical methods to compute the positioning and integrate collaboration among users. Examples of methods include Received Signal Strength (RSS)-based, PDR and k-NN for the non-collaborative part, and Particle Filter, Belief Propagation and EKF for the collaborative part. We define methods as a set of logical rules or processes to be followed in calculations in order to determine a positioning estimate.
3.4.2. Overall System
- System Architecture. The System Architecture refers to the type of data processing architecture used in the CIPS, distributed or centralized in this review.
- System Infrastructure. The hardware deployed in the environment that the CIPS requires to operate such as BLE beacons, RFID tags, fixed cameras, and other ad hoc elements installed in the environment.
- System Evaluation. This category refers to how the system’s accuracy and performance are evaluated, for example, using numerical simulation, field tests, or both.
- Main Finding(s) Reported. In this category the main findings reported by the authors of the CIPS are classified. They are related with the evaluation metrics (position accuracy, position precision, system robustness, computational complexity, energy consumption), which are strongly linked with overarching concerns (i.e., concerns not specific to a particular architecture, infrastructure, technology, technique or method, but instead relevant for all systems), limitations of the systems and future research avenues.
4. Results
4.1. Evolution of CIPS over Time and Their Evaluation Metrics
4.2. Infrastructure and Architecture
4.3. Non-Collaborative Technologies, Techniques, and Methods
- The most used technology, Wi-Fi (used in 53.5% of all articles), was in the majority of cases combined with the Received Signal Strength Indicator (RSSI) technique (42% of articles using Wi-Fi technology), also to an equal amount with Fingerprinting (42%).
- The Inertial Measurement Unit (IMU) technology was exclusively used in combination with the Dead Reckoning (DR) techniques and the Pedestrian Dead Reckoning (PDR) methods (with the exception of a single use of a collaborative algorithm).
- Wi-Fi and Ultra-wide band (UWB) were the technologies that had been combined with the largest number of techniques (four techniques each). The most common technique for both technologies was Received Signal Strength Indicator (RSSI).
- Dead Reckoning (DR) was used in combination with a single technology, Inertial Measurement Unit (IMU).
- Received Signal Strength Indicator (RSSI) and Time of Arrival/Flight (ToA/ToF), respectively the first and fourth most used technique, were used in combination with the greatest number of different technologies (respectively seven and three technologies). The TDoA, TWR, and AoA techniques were used with two technologies each; all other techniques were used with a single technology (with the exception of the Fingerprinting that was used with Wi-Fi and Bluetooth).
- RSSI, Fingerprinting, and Time of Arrival/Flight (ToA/ToF) were used with the highest number of methods (six methods each).
- The two most popular methods, PDR-based and Cooperative methods, were used in almost half of the articles (53.5%). Together with the group of four reasonably well-used methods (i.e., Ranging, RSSI-based, Fingerprinting-based methods, and k-NN), they appeared in almost 98% of the reviewed papers. The remaining 10 methods were less common and appeared in less than 20% of papers.
- The popular Cooperative and Ranging methods (respectively second and third most used) were combined with a variety of techniques. Cooperative methods were used in combination with RSSI (45% inputs), TDoA (20% inputs), ToA/ToF (20% inputs), and with DR, TWR, Fingerprinting in 5% inputs each. Ranging methods were highly coupled with the RSSI technique (67% of inputs to the method), but it was also used with other techniques, namely TWR (17% inputs), ToA/ToF (8% inputs), and UTDoA (8% inputs). In contrast, k-NN exclusively worked with the Fingerprinting technique, which in turn was mainly used in combination with the Wi-Fi technology.
- Artificial Intelligence (AI) was present in three positioning methods, namely k-NN (in 9.5% of analysed works), and k-Means Clustering + Random Forest and Kullback-Leibler Divergence the (in 1.1% of analyzed works each).
- Almost half of the methods (7 out of 16) were only used in one article and were evidently each combined with a single technique and method.
4.4. Collaborative Technologies, Techniques, and Methods
- The most used technology, Wi-Fi (used in 41.6% of all articles) was in the majority of cases combined with the RSSI technique (68% of articles using Wi-Fi technology), yet to a lower extent also with ToA/ToF (20%), Fingerprinting (9%) and minimally with AoA (3%).
- The top three technologies, Wi-Fi, UWB, and Bluetooth, were all combined with multiple techniques, respectively with four, five, and three. Specifically Wi-Fi with RSSI, ToA/ToF, Fingerprinting, and AoA; UWB with RSSI, ToA/ToF, TWR, TDoA, and Multipath components; Bluetooth with RSSI, ToA/ToF, and Positioning sharing, as can be observed in Figure 6.
- The RSSI technique was by far mostly used (72.6%) and was combined with a large variety of technologies and methods. It was mostly combined with the technologies Wi-Fi (40.8%), Bluetooth (22.4%), UWB (14.4%), Acoustic (6.4%), RFID (4.8%), Other RF (3.2%), VLC (3.2%), IEEE.802.15.4a.CSS (1.6%), Magnetic Resonant Sensor (1.6%), and it was combined with 24 of the 30 methods, with Particle Filter being the most used (28%) combination.
- From virtually every technique, there was a diversity of combined technologies and methods. Only Fingerprinting and Multipath Components were combined with a single technology, respectively Wi-Fi and UWB; all techniques, except UTDoA that appeared in just one paper, were combined with multiple methods.
- The most used method, Particle Filtering, was used 85% in combination with RSSI, 10% in combination with TWR, and 5% with Fingerprinting.
- Artificial Intelligence (AI) had a significant presence in collaborative methods, with more than 20 out of 30 methods. The most popular methods were Particle Filter, Belief Propagation, Least Square, and Bayesian Filtering, which were present in a 22.6%, 10.7%, 7.1%, and 4.7% of works, respectively. Other interesting AI methods were Mutidimensional Scaling, Non-Linear Least Squares (NLLSs), Self-organizing Map, and Semidefinite Programming, each present in 3.5% of works, followed by Gaussian Weighting Function and Max. Gradient Descendent with a presence in 2.3% papers each. The least common collaborative AI methods appeared in just one paper each and included Likelihood Function, Coalitional Game, Devaluation Function, Distributed Stochastic Approx., Information Filter, Max. Likelihood Estimator, Max. Shared Border, Non-parametric Belief Propagation, Probabilistic Density Distribution, Recursive Position Estimation, and Simulated Annealing.
- A majority of methods were only used once (16 of 30) or twice (6 of 30). Evidently, methods that were used once combined with a single technique and method.
4.5. Evaluation of Systems
5. Discussion
5.1. Architectures and Infrastructure of Collaborative Indoor Position Systems
5.2. Technologies, Techniques, and Methods in Collaborative Indoor Positioning Systems
5.2.1. Analysis on the Non-Collaborative Part
5.2.2. Analysis on Collaborative Part
5.2.3. Overarching Concerns
5.3. Evaluation of Collaborative Indoor Position Systems
5.4. Recommendations, Gaps, and Limitations
- Architecture: A decentralized architecture is the most suitable option for a collaborative approach since it avoids communication bottlenecks, delays in response times, and dependence on a server. However, computing algorithms on (restricted) user devices limits the implementation of complex algorithms and, due to device variability, its performance might not be homogeneous for all users.
- Infrastructure: A CIPS based on Infrastructure-less approach or based on signals of opportunity might be preferable, due to the continuous mobility of users in different environments, and the cost of developing an infrastructure to provide coverage of the operational area. In addition, an Infrastructure-less approach provides versatility to the system in order to be used in a larger number of scenarios. However, the lack of an ad hoc infrastructure for the CIPS implies a challenge in its design in order to compensate for the inaccurate positioning that the uncontrolled environments provide. Only for specific real-world scenarios, an infrastructure-based approach may be preferable.
- Technologies: Despite the great accuracy and precision positioning provided by some technologies (mainly VLC, UWB, and 5G), Wi-Fi and BLE might currently be better suited, as other relevant factors are the ubiquity of the technologies used, the low implementation costs, and the low energy consumption that Wi-Fi and BLE offer. An evolution in general availability and supporting hardware, e.g., particularly in the case of 5G, may cause a shift in preferred technology.
- Techniques: From the point of view of positioning accuracy and considering Wi-Fi as main positioning technology, Wi-Fi Fingerprinting is widely used because the position of the anchors (APs) is not needed. However, the techniques based on RSSI perform better as the geometry and distribution of the APs are well known. Further investigation of the supporting infrastructure—e.g., estimating the APs by manual inspection or automatic detection [161,162]—might allow the replacement of fingerprint-based with more accurate RSSI-based methods.
- Methods: Due to the diversity of scenarios and conditions in which the systems have been tested, it is difficult to specify which method is the most appropriate. We consider that different alternative methods should be compared in different dimensions—mainly accuracy, precision, robustness, and computational cost—when a new CIPS is proposed, and the final proposed one should be selected according to some pre-defined criteria (e.g., best positioning error, lowest execution time, or a trade-off between the two).
- The proposed CIPSs tend to focus on excelling in one relevant characteristic, mainly the deployment costs, the computational complexity, the real-time operation, energy consumption, or the positioning accuracy. The main limitation of current CIPSs is that none of them try to balance all these aspects, specially in complex environments.
- In general, the CIPS select a single technology for the non-collaborative part and a single technology for the collaborative part. Generally, the reviewed CIPS neither exploit sensor fusion nor multiple positioning alternatives. We consider that technology diversity in both parts might make the CIPS more robust, as it has been demonstrated in conventional IPS.
- None of the reviewed works = considered the privacy of the users nor the security of the CIPSs. Privacy is a main overarching concern that has already been regulated in many countries (e.g., the European General Data Protection Regulation (GDPR) [163]). The vast majority of positioning solutions (in the non-collaborative and collaborative phase) rely on communication technologies that can be attacked (i.e., jamming or spoofing mainly) to alter the outputs of the positioning system and/or the sensing data processed by the user, which might be considered a security breach of the CIPS. Energy consumption is also a relevant overarching concern, which may deter users from using a CIPS, and this area is insufficiently studied.
- The evaluations of the CIPSs are tightly coupled to the technology used in the non-collaborative part. The community needs an evaluation framework able to objectively evaluate the collaborative part of the CIPS with independence to the positioning technology used in the non-collaborative part. An important part of such a framework is comparable evaluation metrics. Moreover, evaluation considering multiple technologies working simultaneously has not been widely explored yet.
- Evaluation is done over simulations in almost half of reviewed works because it does not require deploying expensive hardware and manual labor. Although some simulated environments are able to mimic the real world, a comprehensive empirical evaluation is needed to demonstrate the feasibility of the proposed CIPSs in realistic conditions. A repository of extensive multi-sensor and multi-user datasets for that purpose could enhance research reproducibility, enable the fair comparison of CIPSs, reduce evaluation costs (assuming the datasets are publicly available), and be an incentive to further research CIPSs.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AAL | Ambient Assisted Living |
AI | Artificial Intelligence |
AoA | Angle of Arrival |
AP | Access Point |
BLE | Bluetooth Low Energy |
CDF | Cumulative distribution Function |
CIPS | Collaborative Indoor Positioning System |
CSI | Channel State Information |
CTP | Collection tree protocol |
D2D | Device to Device |
DR | Dead Reckoning |
EKF | Extended Kalman Filter |
FM | Frequency Modulation |
GLONASS | Globalnaya Navigazionnaya Sputnikovaya Sistema |
GDPR | General Data Protection Regulation |
GNSS | Global Navigation Satellite System |
GPS | Global Positioning System |
IMU | Inertial Measurement Unit |
IoT | Internet of Things |
IPS | Indoor Position System |
k-NN | k-Nearest Neighbors |
LBS | location-based service |
LOS | Line-of-sight |
LS | Least Square |
LTE | Long-Term Evolution |
MEMS | Microelectro-Mechanical System |
NLLS | Non-Linear Least Square |
NLOS | Non-line-of-sight |
PDR | Pedestrian Dead Reckoning |
PRISMA | Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
RF | Radio Frequency |
RFID | Radio-Frequency Identification |
RMSE | Root Mean Square Error |
RSS | Received Signal Strength |
RSSI | Received Signal Strength Indicator |
SVM | Support Vector Machine |
TDoA | Time Difference of Arrival |
ToA | Time of Arrival |
ToA/ToF | Time of Arrival/Flight |
TWR | Two-way Ranging |
UDP | User Datagram Protocol |
UTDoA | Uplink Time-Difference-of-Arrival |
UWB | Ultra-wide band |
VLC | Visible Light Communication |
WASP | Wireless Application Service Provider |
Wi-Fi | IEEE 802.11Wireless LAN |
WSN | Wireless Sensor Network |
Appendix A
Appendix A.1. Search Queries
Database | Input Query | No. Articles |
---|---|---|
Scopus | (TITLE-ABS-KEY (((Collabora* OR Coopera*) AND Indoor) AND (Position* OR Track* OR Locati* OR Locali* OR Navigat*)) AND LANGUAGE (english)) | 1404 |
Web of Science | TS=((Collabora* OR Coopera*) AND Indoor AND (Position* OR Track* OR Locati* OR Locali* OR Navigat*)) | 1425 |
Appendix A.2. Articles Included in the Systematic Review
Year | Ref. | Technology | Technique | Method | Arch. | Infr. | Eval. | Eval. Metric | |||
---|---|---|---|---|---|---|---|---|---|---|---|
Non-Collaborative | Collaborative | Non-Collaborative | Collaborative | Non-Collaborative | Collaborative | ||||||
2020 | [143] | Bluetooth | Bluetooth | F. printing | RSSI | F. printing-B. | Geom. Algorithm | D | I-L | E | PA |
[144] | IMU, UWB | UWB | DR, RSSI | RSSI | PDR-B. M., Ranging | Bayessian F. | D | W/I | S | PA+E | |
[145] | IMU | Bluetooth | DR | RSSI | PDR-B. M. | EKF | D | I-L | E | PA | |
[146] | IMU | Wi-Fi, UWB | DR | RSSI | PDR-B. M. | P. Filter | D | I-L | S+E | PA+CC | |
[147] | IMU, Wi-Fi | UWB | DR, F. printing | TWR | PDR-B. M., F. printing-B | P.Filter | D | I-L | E | PA | |
[148] | 5G | 5G | RSSI | RSSI | Coop. Algorithm | LS | N/S | W/I | S | PA | |
[149] | Wi-Fi | Bluetooth | F. printing | RSSI | F. printing-B | P. Filter | C | I-L | E | PA | |
2019 | [141] | IMU | UWB | DR | TWR | PDR-B. M. | EKF | D | W/I | E | PA |
[140] | UWB | UWB | TWR | TWR | Coop. Algorithm | EKF | C | W/I | E | PA | |
[139] | UWB | UWB | TDoA | TDoA | Coop. Algorithm | Bayesian F. | N/S | W/I | E | PA | |
[138] | UWB | UWB | TDoA | TDoA | Coop. Algorithm | Analytic | N/S | W/I | E | PA | |
[137] | 5G | 5G | AoA | AoA | Multilateration | LS | D | I-L | S | PA+CC | |
[49] | UWB | UWB | ToA/ToF | ToA/ToF | Entropy-based ToA | LS | D | I-L | S | PA+PP | |
[81] | Wi-Fi | Wi-Fi | RSSI | RSSI | Ranging | Multidimensional Scaling | N/S | I-L | S | PA+CC+R | |
[77] | VLC | VLC | RSSI | RSSI | RSSI-B. M. | Max. Likelihood E. | D | W/I | S | PA+CC | |
[80] | RFID | RFID | RSSI | RSSI | Anlytic | Multidimensional Scaling | C | W/I | E | PA | |
[135] | VLC | VLC | RSSI | RSSI | Trilateration | B. Propagation | D | W/I | E | PA | |
[79] | Wi-Fi | Wi-Fi | F. printing | ToA/ToF | KNN | Analytic | D | I-L | E | PA | |
[137] | Wi-Fi | Wi-Fi | F. printing | RSSI | KNN | Geom. Algorithm | N/S | I-L | E | PA | |
[46] | UWB | UWB | RSSI | RSSI | Ranging | B. Propagation | D | I-L | S | PA+CC | |
[142] | Wi-Fi, Bluetooth | Wi-Fi, Bluetooth | RSSI | RSSI | Coop. Algorithm | Geom. Algorithm | C | W/I | S+E | PA | |
2018 | [134] | Wi-Fi | Wi-Fi | F. printing | RSSI | KNN | Geom. Algorithm | N/S | I-L | E | PA |
[48] | Wi-Fi | Other RF | RSSI | TDoA | RSSI-B. M. | Spatial Analysis-based | N/S | I-L | S | PA | |
[129] | IMU, RFID | RFID | DR, RSSI | RSSI | PDR-B. M., RSSI-B. M. | P. Filter | C | W/I | E | PA | |
[133] | Wi-Fi | Wi-Fi | RSSI, ToA/ToF | RSSI, ToA/ToF | Coop. Algorithm | LS | N/S | N/S | E | PA | |
[132] | UWB | UWB | TWR | TWoA | Ranging | B. Propagation | N/S | W/I | S | PA+CC | |
[43] | IMU, Wi-Fi | Wi-Fi, Bluetooth | DR, RSSI | RSSI | PDR-B. M. | Geom. Algorithm | D | I-L | E | PA+E | |
[131] | IMU | Bluetooth, Acoustic | DR | RSSI | PDR-B. M. | B. Propagation | D | I-L | E | PA+CC+R | |
[130] | Wi-Fi | Wi-Fi | RSSI | RSSI | Ranging | B. Propagation | N/S | I-L | S | PA+CC | |
[44] | Wi-Fi | Bluetooth | F. printing | RSSI | K-mean clustering+R. Forest | P. Filter | C | W/I | E | PA | |
[128] | IMU | UWB | DR | TWR | PDR-B. M. | Bayesian F. | D | I-L | S+E | PA | |
[63] | IMU, Wi-Fi | Wi-Fi | DR, F. printing | F. printing | PDR-B. M., KNN | Least Lost Matching E | D | I-L | S+E | PA | |
2017 | [127] | IMU | Other RF | DR | Pos. Sharing | PDR-B. M. | Geom. Algortihm | C | W/I | S | PA |
[126] | LTE | LTE | TDoA | TWR | Coop. Algorithm | P. Filter | D | I-L | S | PA | |
[125] | Hybrid S. | Other RF | Hybrid Techni. | RSSI | Hybrid Methods | EKF | N/S | I-L | N/S | PA | |
[124] | Laser+Compass | Laser+Compass | ToA/ToF | ToA/ToF | Geom. Ranging | Geom. Algorithm | C | I-L | E | PA | |
[123] | IMU, Wi-Fi | Bluetooth | DR, RSSI | RSSI | PDR-B. M., RSSI-B. M. | P. Filter | N/S | I-L | S | PA | |
[122] | Wi-Fi | Wi-Fi | ToA/ToF | ToA/ToF | Coop. Algorithm | Semidefinite Programming | C | I-L | S | PA+CC | |
[121] | IMU | UWB | DR | RSSI | Coop. Algorithm | Info. Filter | N/S | I-L | E | PA | |
2016 | [120] | IMU | UWB | DR | RSSI | PDR-B. M. | P. Filter | D | I-L | E | PA+R |
[119] | Wi-Fi | Wi-Fi | F. printing | ToA/ToF | KNN | Max. Grad. Descendent | D | I-L | S | PA | |
[118] | IMU, Wi-Fi | Wi-Fi, Acoustic | DR, F. printing | RSSI | PDR-B. M., KNN | P. Filter | C | W/I | S+E | PA | |
[117] | IMU | RFID | DR | RSSI | PDR-B. M. | P. Filter | C | W/I | E | PA | |
[116] | Wi-Fi | Other RF | F. printing | RSSI | Kullbakc-Leibler Div. | Multidimensional Scaling | C | W/I | E | PA+R | |
[115] | UWB | UWB | TDoA | RSSI | Coop. Algorithm | EKF | C | I-L | S | PA+PP | |
2015 | [150] | UWB | UWB | RSSI | Multiphath C. | Ranging | EKF | N/S | N/S | S | PA+CC+R |
[114] | Wi-Fi | Wi-Fi | F. printing | RSSI | KNN | Self-organized map | D | I-L | S | PA | |
[42] | Wi-Fi | Wi-Fi, Bluetooth | RSSI | RSSI | Ranging | Trilateration | C | I-L | E | PA+E | |
[113] | LTE | LTE | UTDoA | UTDoA | Ranging | NLLS | C | I-L | S | PA | |
[105] | Wi-Fi | Wi-Fi | ToA/ToF | ToA/ToF | Coop. Algorithm | Semidefinite Programming | N/S | N/S | S | PA | |
[111] | UWB | UWB | RSSI | RSSI | Ranging | Simulated Annealing | N/S | W/I | S | PA | |
[110] | IMU | Wi-Fi | DR | RSSI | PDR-B. M. | Semidefinite Programming | N/S | I-L | S | PA | |
[109] | Wi-Fi | Wi-Fi | ToA/ToF | ToA/ToF | Ranging | B. Propagation | D | W/I | E | PA+CC | |
[108] | Wi-Fi | Bluetooth | F. printing | RSSI | KNN | Edge Spring M. | C | I-L | S | PA | |
[41] | Wi-Fi | Bluetooth | RSSI | RSSI | RSSI-B. M. | Trilateration | C | I-L | E | PA | |
2014 | [107] | IMU | Acoustic | DR | Pos. Sharing | PDR-B. M. | EKF | D | I-L | E | PA |
[106] | IMU | UWB | DR | RSSI | PDR-B. M. | EKF | N/S | I-L | S | PA | |
[40] | Wi-Fi | Wi-Fi | RSSI | RSSI | RSSI-B. M. | Trilateration | C | I-L | E | PA | |
[105] | Wi-Fi | Wi-Fi | RSSI | RSSI | Coop. Algorithm | D. Stochastic Approx. | D | W/I | S | PA | |
[104] | Wi-Fi | Bluetooth | RSSI | RSSI | Multilatration | Max. Grad. Descendent | N/S | I-L | S | PA | |
[45] | Wi-Fi | Bluetooth | ToA/ToF | ToA/ToF | Trilateration | LS | D | I-L | S+E | PA | |
[103] | Wi-Fi | Wi-Fi | RSSI | RSSI | RSSI-B. M. | Self-organazed map | N/S | W/I | S | PA | |
2013 | [102] | IMU, Wi-Fi | Wi-Fi | DR, F. printing | RSSI, F. printing | PDR-B. M., F. printing-B. M. | P. Filter | N/S | I-L | S | PA |
[101] | IMU | Acoustic | DR | RSSI | PDR-B. M. | KF | D | I-L | N/S | PA | |
[100] | Wi-Fi | Wi-Fi | F. printing | RSSI | F. printing-B. M. | Likelihood func. | C&D | I-L | S | PA | |
[99] | UWB | UWB | RSSI | RSSI | Coop. Algorithm | Non-Parametric B. Propagation | N/S | N/S | S | PA+CC | |
[98] | IMU, Wi-Fi | Wi-Fi | DR, F. printing | RSSI | PDR-B. M., F. printing-B. M. | P. Filter | D | I-L | S+E | PA+R | |
2012 | [97] | IMU | Bluetooth | DR | RSSI | PDR-B. M. | Bayessian F. | D | I-L | E | PA |
[47] | IEEE.802.15.4a.CSS | IEEE.802.15.4a.CSS | TWR | TWR | Ranging | B. Propagation | C | W/I | E | PA+R | |
[96] | UWB | UWB | ToA/ToF | Multipath C. | Coop. Algorithm | B. Porpagation | C/D | I-L | S | PA | |
2011 | [95] | Wi-Fi | Wi-Fi | F. printing | F. printing | Coop. Algorithm | Self-organized map | D | I-L | S | PA+CC |
[50] | Wi-Fi | Wi-Fi | AoA, ToA/ToF | AoA, ToA/ToF | Geom. Ranging | GWF | N/S | I-L | S | R | |
[94] | IEEE.802.15.4a.CSS | IEEE.802.15.4a.CSS | RSSI | RSSI | Multilateration | Trilateration | D | W/I | E | PA | |
[93] | Wi-Fi | Wi-Fi | RSSI | RSSI | Ranging | P. Filter | N/S | N/S | S | PA | |
[92] | IMU | Magnetic Resonant S. | DR | RSSI | PDR-B. M. | Probabilistic D. Distrib. | D | I-L | N/S | PA | |
[91] | Camera | Bluetooth | QR Code | RSSI | QR Code Recongnition | Devaluation Func. | D | W/I | E | PA | |
[90] | Wi-Fi | Wi-Fi | RSSI | RSSI | Coop. Algorithm | Coalitional Game | D | N/S | S | PA+CC | |
2010 | [89] | Wi-Fi | Wi-Fi | RSSI | RSSI | RSSI-B. M. | P. Filter | C | N/S | S | PA+CC |
[88] | Wi-Fi | Wi-Fi | RSSI | RSSI | Coop. Algorithm | NLLS | D | I-L | E | PA+CC | |
2009 | [87] | Wi-Fi | Bluetooth | F. printing | Pos. Sharing | Max. Shared Border | Max. shared Border | D | I-L | N/S | PA+CC |
2007 | [86] | UWB | UWB | ToA/ToF | ToA/ToF | Multilateration | Rec. Pos. Est. | D | I-L | S | PA |
[85] | Wi-Fi | Wi-Fi | RSSI | RSSI | Coop. Algorithm | NLLS | D | I-L | E | PA | |
2006 | [83] | Wi-Fi | Wi-Fi | F. printing | RSSI | F. printing-B. M. | P. Filter | D | I-L | E | PA |
[84] | Wi-Fi | Wi-Fi | RSSI | RSSI | RSSI-B. M. | D. Loc-coverage | C | I-L | S | PA |
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|
|
|
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Liu et al. [52] (2007) | Gu et al. [55] (2009) | Zafari et al. [54] (2019) | Mendoza-Silva et al. [53] (2019) |
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|
|
|
|
Güvenc and Chong [57] (2009) | He and Chan [56] (2016) | Yassin et al. [72] (2017) | Chen et al. [66] (2017) | |
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In LOS scenarios
| In NLOS scenarios
|
|
|
|
Research Question | Systematic Review Section |
---|---|
Research Question 1 | Results, Section 4.2 and Section 4.4 |
Discussion, Section 5.1 and Section 5.2 | |
Research Question 2 | Results, Section 4.4 |
Discussion, Section 5.2 | |
Research Question 3 | Results, Section 4.5 and Section 4.1 |
Discussion, Section 5.3 | |
Research Question 4 | Discussion, Section 5.4 |
Conclusion, Section 6 |
Method | Advantages | Disadvantages | |
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Non-Collaborative | PDR-based |
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Ranging |
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| |
RSSI-based |
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| |
Fingerprint-based |
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k-NN |
|
| |
Collaborative | P. Filter |
|
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Belief Propagation |
|
| |
EKF |
|
| |
Geom. Algorithm |
|
| |
LS |
|
| |
Trilateration |
|
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Pascacio, P.; Casteleyn, S.; Torres-Sospedra, J.; Lohan, E.S.; Nurmi, J. Collaborative Indoor Positioning Systems: A Systematic Review. Sensors 2021, 21, 1002. https://doi.org/10.3390/s21031002
Pascacio P, Casteleyn S, Torres-Sospedra J, Lohan ES, Nurmi J. Collaborative Indoor Positioning Systems: A Systematic Review. Sensors. 2021; 21(3):1002. https://doi.org/10.3390/s21031002
Chicago/Turabian StylePascacio, Pavel, Sven Casteleyn, Joaquín Torres-Sospedra, Elena Simona Lohan, and Jari Nurmi. 2021. "Collaborative Indoor Positioning Systems: A Systematic Review" Sensors 21, no. 3: 1002. https://doi.org/10.3390/s21031002