Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services
Abstract
1. Introduction
- -
- It reframes IPSs from auxiliary technologies to core infrastructure components.
- -
- It provides a comprehensive, up-to-date review of IPSs from a CI perspective.
- -
- It introduces a structured framework for evaluating trade-offs among IPS technologies.
- -
- It identifies critical research gaps, including interoperability, data privacy, and performance under adverse conditions.
- -
- It proposes a research agenda to guide the development of resilient and context-aware IPS solutions.
2. Overview of IPSs
2.1. Applications of IPSs
- (a)
- Public building
- (b)
- Indoor navigation and Tracking objects
- (c)
- Healthcare
- (d)
- Manufacturing industries
- (e)
- Public Security
- (f)
- Monitoring People and Activities
- (g)
- Service Industries/Sectors
2.2. Related Works
2.2.1. Ubiquity
2.2.2. Seamlessness
2.2.3. Intelligence
2.3. Challenges and Trade-Off of IPSs
- Stand-alone Failure System/technologies
- 2.
- Heterogeneity of Features
- 3.
- Measurement Techniques
- 4.
- Design Requirements
- 5.
- Major Positioning Techniques
- A.
- Algorithmic Methods
- (i)
- Centralized methods focus all localization calculations at a single processing unit, usually a server or cloud-based engine. Centralized models may face problems with latency, privacy, and scalability, especially in large-scale deployments or latency-sensitive applications, even though they are capable of complex inference and integration of heterogeneous data sources.
- (ii)
- Distributed methods delegate localization computation to specific mobile devices or nodes. By reducing communication overhead and facilitating localized inference, these systems improve scalability and robustness. However, because each node only has a limited amount of contextual information available, they might be less accurate.
- (iii)
- By using multi-node collaboration and successive refinement, iterative approaches combine the advantages of distributed and centralized schemes. This class includes methods like belief propagation, particle filtering, and message passing, which provide accuracy and flexibility at the expense of longer computation and convergence times.
- B.
- Positioning Techniques
- (i)
- Range-based techniques: These methods depend on the geometric properties of signal transmission and necessitate precise measurements of physical signal characteristics.
- ○
- The TOA, TDOA, and RTT methods estimate distances from time delays between signal transmission and reception.
- ○
- AOA makes use of directional data obtained by antenna arrays.
- ○
- Using signal attenuation models, Received Signal Strength (RSS) calculates distance.
- (ii)
- Range-free techniques: Direct estimation of distance or angle is avoided by range-free techniques. Rather, they use network topology or pattern recognition to infer location:
- ○
- Pattern-matching algorithms (like k-NN and SVM) and pre-recorded signal characteristics (like RSSI vectors) are used in fingerprinting techniques.
- ○
- Proximity-based models use the closest beacon or access point to determine the user’s location.
- ○
- Using the network’s topological characteristics, connectivity and hop-count-based techniques (like DV-Hop) estimate location.
- C.
- Enabling Technologies
- (i)
- Device-based technologies require an active device (such as a smartphone or BLE/UWB tag) to be carried by the user or object. These systems dominate consumer and commercial applications by taking advantage of the wide availability of mobile devices and their Wi-Fi, Bluetooth, or UWB compatibility.
- (ii)
- Device-free technologies passively determine a subject’s location without requiring them to carry any equipment. These consist of vibration/acoustic sensors, vision systems with depth or RGB cameras, and motion detection based on radio frequency. These methods are especially useful in situations involving non-cooperative tracking, security, and ambient assisted living. An organized and non-redundant framework for analyzing IPS design decisions is provided by this tripartite classification, which consists of approaches, techniques, and enabling technologies. It discusses the trade-offs between system accuracy, deployment complexity, energy efficiency, and cross-application domain adaptability. To achieve reliable, context-aware, and scalable indoor localization solutions, these dimensions must be in line with particular use-case requirements.
- 6.
- Domain-Specific Demands and Trade-Offs in IPS Deployment
3. Experimental Results and Discussion
- (a)
- Wi-Fi Fingerprint-Based Indoor Location Estimation of Targets Utilizing Original Feature Spaces
4. Evaluation Criteria for IPSs
- Accuracy
- 2.
- Energy Efficiency
- 3.
- Reliability
- 4.
- Scalability
- 5.
- Interoperability
- 6.
- Resilience
- 7.
- Privacy and Security
- 8.
- Cost and Ease of Deployment
4.1. Trade-Off Analysis with GPS
4.2. Collaborative Indoor Positioning Systems
5. Discussion and Conclusions
- -
- -
- -
- The need for regulatory frameworks to govern IPS deployment in sensitive environments [46];
- -
6. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Trade-off Dimension | Description | Cause/Constraint | Implications |
---|---|---|---|
Accuracy vs. Environmental Dynamics | High-accuracy systems degrade in NLOS or multipath environments | Signal fluctuation, occlusion, interference | Limits reliability in dynamic or cluttered indoor environments |
Accuracy vs. Energy Efficiency | Improved precision increases computational and sensing load | AI models, multi-sensor fusion | Reduces battery life in wearables and mobile platforms |
Scalability vs. Infrastructure | Greater area coverage often reduces localization precision | Sparse anchor nodes, infrastructure-light setups | Affects usability in large or multi-floor facilities |
Model Generalizability vs. Signal Variability | Fingerprint models struggle with time-dependent or device-specific changes | Device heterogeneity, signal instability | Frequent re-calibration needed; impacts real-time tracking consistency |
Privacy vs. Continuous Tracking | Real-time localization raises ethical and regulatory concerns | Data sensitivity in healthcare, public spaces | Requires privacy-preserving and secure system architectures |
Standardization vs. Technological Diversity | Heterogeneous technologies lack unified standards | Vendor-specific protocols, diverse sensing modalities | Hinders cross-platform integration and interoperability |
Criteria | Dataset Collected on Month 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Training Dataset 1 | Training Dataset 2 | |||||||||
#Testing Samples | #Testing Samples | |||||||||
#MAE (in m) | ||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |
DT | 2.31 | 2.97 | 4.41 | 5.57 | 2.33 | 2.30 | 2.98 | 4.38 | 5.57 | 2.27 |
KNN | 2.26 | 2.88 | 4.38 | 5.71 | 2.34 | 2.19 | 2.94 | 4.45 | 5.67 | 2.18 |
SVC | 2.22 | 2.87 | 4.38 | 5.52 | 2.28 | 2.20 | 2.94 | 4.49 | 5.61 | 2.21 |
LR | 2.21 | 2.86 | 4.36 | 5.62 | 2.36 | 2.23 | 2.92 | 4.42 | 5.62 | 2.27 |
RF | 2.22 | 3.00 | 4.49 | 5.63 | 2.23 | 2.21 | 2.98 | 4.49 | 5.68 | 2.20 |
GMM | 2.23 | 3.11 | 4.27 | 5.67 | 2.06 | 2.08 | 3.21 | 4.51 | 5.67 | 2.08 |
MLP | 2.02 | 3.49 | 5.10 | 6.07 | 2.04 | 1.93 | 2.52 | 4.18 | 5.49 | 1.99 |
Proposed | 1.98 | 2.25 | 3.08 | 3.97 | 1.99 | 1.97 | 2.26 | 3.09 | 3.97 | 1.98 |
Criteria | Dataset Collected on Month 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Training Dataset 3 | Training Dataset 4 | |||||||||
#Testing Samples | #Testing Samples | |||||||||
#MAE (in m) | ||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |
DT | 2.21 | 3.05 | 4.49 | 5.72 | 2.24 | 2.24 | 3.05 | 4.39 | 5.72 | 2.28 |
KNN | 2.22 | 2.98 | 4.44 | 5.69 | 2.26 | 2.17 | 2.99 | 4.48 | 5.84 | 2.19 |
SVC | 2.18 | 2.94 | 4.44 | 5.61 | 2.27 | 2.23 | 2.99 | 4.48 | 5.78 | 2.22 |
LR | 2.21 | 2.91 | 4.43 | 5.66 | 2.28 | 2.29 | 3.02 | 4.43 | 5.76 | 2.29 |
RF | 2.16 | 3.01 | 4.52 | 5.71 | 2.20 | 2.15 | 3.08 | 4.55 | 5.82 | 2.24 |
GMM | 2.35 | 2.98 | 4.27 | 5.73 | 2.36 | 2.20 | 3.05 | 4.41 | 5.67 | 2.29 |
MLP | 1.87 | 3.48 | 5.14 | 6.02 | 1.88 | 1.99 | 2.72 | 4.35 | 5.65 | 1.97 |
Proposed | 1.96 | 2.27 | 3.08 | 3.99 | 1.98 | 1.98 | 2.26 | 3.08 | 3.98 | 1.98 |
Criteria | Dataset Collected on Month 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Training Dataset 1 | Training Dataset 2 | |||||||||
#Testing Samples | #Testing Samples | |||||||||
#RMSE (in m) | ||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |
DT | 8.64 | 9.22 | 10.97 | 13.26 | 8.74 | 8.56 | 9.20 | 10.91 | 13.21 | 8.61 |
KNN | 8.56 | 9.09 | 10.89 | 13.38 | 8.71 | 8.38 | 9.15 | 10.96 | 13.30 | 8.38 |
SVC | 8.49 | 9.06 | 10.86 | 13.13 | 8.57 | 8.44 | 9.14 | 11.00 | 13.25 | 8.45 |
LR | 8.46 | 9.07 | 10.83 | 13.26 | 8.73 | 8.51 | 9.13 | 10.95 | 13.27 | 8.57 |
RF | 8.55 | 9.23 | 10.94 | 13.29 | 8.58 | 8.32 | 9.24 | 11.00 | 13.31 | 8.44 |
GMM | 8.57 | 8.96 | 10.93 | 13.39 | 8.03 | 8.46 | 9.54 | 10.86 | 13.41 | 8.41 |
MLP | 7.90 | 9.61 | 11.40 | 13.58 | 7.97 | 7.45 | 8.29 | 10.28 | 12.99 | 7.75 |
Proposed | 7.10 | 7.91 | 10.09 | 12.43 | 7.12 | 7.10 | 7.94 | 10.11 | 12.44 | 7.10 |
Criteria | Dataset Collected on Month 1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Training Dataset 3 | Training Dataset 4 | |||||||||
#Testing Samples | #Testing Samples | |||||||||
#RMSE (in m) | ||||||||||
1 | 2 | 3 | 4 | 5 | 1 | 2 | 3 | 4 | 5 | |
DT | 8.42 | 9.35 | 10.98 | 13.43 | 8.52 | 8.50 | 9.30 | 10.93 | 13.40 | 8.67 |
KNN | 8.44 | 9.19 | 10.92 | 13.32 | 8.55 | 8.40 | 9.20 | 11.00 | 13.50 | 8.43 |
SVC | 8.41 | 9.14 | 10.89 | 13.26 | 8.58 | 8.52 | 9.22 | 11.03 | 13.45 | 8.49 |
LR | 8.46 | 9.08 | 10.87 | 13.34 | 8.56 | 8.61 | 9.27 | 10.98 | 13.42 | 8.61 |
RF | 8.36 | 9.24 | 10.95 | 13.44 | 8.36 | 8.41 | 9.27 | 11.00 | 13.45 | 8.36 |
GMM | 8.12 | 9.00 | 10.85 | 13.26 | 8.95 | 8.76 | 9.29 | 11.00 | 13.05 | 8.31 |
MLP | 7.16 | 9.30 | 11.24 | 13.28 | 7.16 | 7.79 | 8.75 | 10.63 | 13.16 | 7.74 |
Proposed | 7.05 | 7.97 | 10.12 | 12.47 | 7.07 | 7.05 | 7.91 | 10.06 | 12.5 | 7.03 |
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Hailu, T.G.; Guo, X.; Si, H. Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services. Sensors 2025, 25, 4914. https://doi.org/10.3390/s25164914
Hailu TG, Guo X, Si H. Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services. Sensors. 2025; 25(16):4914. https://doi.org/10.3390/s25164914
Chicago/Turabian StyleHailu, Tesfay Gidey, Xiansheng Guo, and Haonan Si. 2025. "Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services" Sensors 25, no. 16: 4914. https://doi.org/10.3390/s25164914
APA StyleHailu, T. G., Guo, X., & Si, H. (2025). Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services. Sensors, 25(16), 4914. https://doi.org/10.3390/s25164914