# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Radio Map Construction Aiding the Offline Workload  
Apparently, crowdsourcing, simultaneous localization and mapping (SLAM) and the path loss model enable creating the radio map construction within a specific time. These methods could reduce the human workload and the depletion time, and prior map information. The radio map is basically important for indoor localization to handle the collected RSS measurement from pre-labelled RPs. The heterogeneous smartphones have already built-in various sensors bases that are typically workable for acquiring data types, tracking the users' motion, orientation, acceleration, direction, pressures and step counts. However, it cannot be perfect in the case of collecting the spread of RSS values from the whole large building in practical applications. The crowdsourcing method is entitled as an eminent scheme of an automatically constructed radio map by reducing the major IPS issues, intensively, the time and human workforce. Tables 3 and 4 describe the location accuracy and performance comparison of calibration and human effortlessness. The collection of (RSS) information needs to be updated quickly by user phones and to remain for a certain distance and time, because crowdsource data are normally inaccurate. However, the mobile crowdsourcing idea is becoming an attractive way to construct the radio map without pre-labelled reference points and manual calibration [193; 194]. Crowdsourcing data can be received from the updated fingerprinting RSSI information in a database which is provided by the IMU sensor and PDR trajectory. Then, the radio map is automatically constructed at the indoor location [195; 196; 197]. Most of the wireless indoor locations are regarded as a continuous structure independent on the environment changes. The authors in [198] effectively solved the calibration process and maintenance process due to the changing state in the environment. Additionally, associated with crowdsourcing Wi-Fi, the authors in [199] also presented a way to execute three aspects, which are manual calibration-free, reduced measurement time and the preservation of FP values at each location. In [194], Wi-Fi-based IPS crowdsourcing presents the solution to secure consideration for interrupting attacks and intend to obtain the trusty and authentic data submission between RSS fingerprints of neighbouring positions. Indeed, creating a radio map is locally intended to address the laborious problem and the disturbance of numerous RSS measurements. In this case, crowdsourcing could reduce the labour-intensiveness, the overpowering time for map construction and is obstructively used for the estimation of RSS without deduction power [195; 200]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Evaluation** & **Data Type** & **Infrastructure** & **Performance** \\ \hline
[193] & LiFS, automatic FPs calibration & Wi-Fi/accelerometer sensors. & Entirety office building. Cover range 1600 m\({}^{2}\), Total of 26 rooms. & 5.88 m (average error) Error 80\% under 9 m Error 60\% under 6 m (small and room error) \\ \hline
[198] & Unsupervised learning & Wi-Fi & Office building, (length 80 m and width 32 m) & Around 3 m \\  & Manual calibration effortless. & & floor plan. & 1.5 m \\ \hline
[199] & FreeLoc, Handle complexity calibration of users and devices robust and consistent localization performance & Wi-Fi & University building. & Heterogeneity devices error (around 2 and 4 m) \\ \hline
[200] & Extracting effective RSS from crowdsource data & Wi-Fi/ IMU data & Office building, floor area 4600 m\({}^{2}\) and 1.5 m corridor area 411 m\({}^{2}\). & 1.5 m \\  & & & & \\ \hline
[197] & RCILS. Semantic graph and activity sequence, mitigates RSS variance due to device heterogeneity and environmental changes condition. & Wi-Fi/accelerometer/ compass gyroscope and barometer. & Office building, 2756.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of location accuracy based on crowdsourcing.