Table 3 Communication technology-based systems

Guo et al. [88]

Indoor navigation

Wi-Fi fingerprints

(−) The user should guess his direction at junctions

Wu et al. [91]

Indoor positioning

Wi-Fi fingerprint spatial gradient.

(+) Reduced the positioning errors caused by fluctuations in RSSI

Wu et al. [106]

Indoor localization

BLE, Magnetic field, PDR, Trilateration, Particle filter

(+) Reduced the issues associated with the unwanted shaking of smartphones

Li et al. [103]

Indoor navigation

Wi-Fi, magnetic fingerprint, PDR-based Kalman filter

(+) The system is scalable

Lee et al. [109]

Indoor localization

BLE (Trilateration algorithm) and PDR

(−) Limitation of sensors

Campana et al. [114]

Evaluation of machine learning algorithms for positioning

BLE fingerprints, Random Forest and Bayes classifier

(+) Random Forest algorithm increased the accuracy by 30% with respect to the Bayesian classifier

Abu et al. [121]

Wayfinding in library for people with VI

Localization: Wi-Fi fingerprints, Book finding: Bluetooth + RFID, Dijkstra’s algorithm

(+) Decimeter level accuracy

Tsirmpas et al. [124]

Indoor navigation system for people with VI

RFID for localization, ultrasonic rangefinder, and IMU for safe navigation

(+) 99% Success rate during real time testing

Lin and Guo [125]

Indoor positioning

Frequency shift caused in the RFID system

(+) System overhead is reduced

Loconsole et al. [126]

Indoor navigation system for people with VI

Kalman filter to reduce gravity and sensor baizes, RFID to reduce drift errors, Dijkstra’s algorithm

(+) Drift errors got reduced significantly

Xu et al. [127]

To solve the fluctuations in RFID-based localization systems

RFID and Bayesian algorithm integrated with K-NN

(+) The average location estimation error was 15 cm

Ganti et al. [128]

Indoor navigation system

VLC (Trilateration algorithm), sequential important particle filter, and Kalman filter

(+) The particle filter method is better for user tracking

Jayakody et al. [129]

Indoor navigation system for people with VI

Visible light, geomagnetic sensor, and sonar for orientation and obstacle determination

(+) Implementation cost low

Martinez-Sala et al. [23]

Indoor navigation system for people with VI

UWB (TDOA, AOA approach) and A* algorithm

(+) Centimeter level accuracy (< 20 cm)

Fan et al. [131]

To reduce the error in UWB and IMU-based navigation system

UWB (TDOA approach) and IMU, double state adaptive Kalman filter

(+) Significant reduction in positioning errors

Murata et al. [116]

Indoor localization for blind navigation

RSSI from BLE beacons, IMUs, and probabilistic localization algorithm

(+) Reduced the error in localization up to 1.5 m

Ahmetovic et al. [117]

Indoor navigation for people with VI

BLE fingerprinting and IMUs

(−) Current version lack the functionality to notify the users traveling in the wrong path

Kim et al. [118]

Indoor navigation for people with VI

BLE beacons based proximity detection and IMUs

(+) Proposed system was tested in a complex, highly crowded environment and observed results showed its potential for large scale deployment

Cheraghi et al. [119]

Indoor navigation for people with VI

BLE beacons based proximity detection and IMUs

(+) Low-cost beacons reduced the implementation cost

Jin-Woo et al. [96]

Indoor localization

CNN based Wi-Fi fingerprinting approach

(+) The proposed system is robust to small RSS fluctuations

Mittal et al. [97]

Indoor localization

CNN based Wi-Fi fingerprinting approach in smartphone

(+) Average localization error < 2 m

Ibrahim et al. [98]

Indoor localization

CNN based Wi-Fi fingerprinting using time series of RSS value as input.

(+) Building and floor prediction accuracy: 100%