# Review of Indoor Positioning: Radio Wave Technology
## 5 Conclusions  
This paper reviews the comprehensive description of radio wave signals for indoor positioning, based on common technologies and effective positioning methods. Additionally, the behaviour of non-radio wave signals was mentioned in the introduction section. As mentioned before, the existing positioning algorithms have addressed the inaccurate positioning issue due to the signal variance of multipath propagation, hardware and software complexity, real-time processing and the changing dynamic environment. These algorithms noticeably coped with the depletion of battery usage, the reduction in numerous RPs' accumulation and the correctness of the estimated current position. According to workload reduction, radio map construction can increasingly offer precise positioning behaviour for an unknown environment of a large-scale building. The crowdsourcing method can compute the localization accuracy and medium error in the respective area by implementing the calibration effortlessness and mitigation of RSS variation. Occasionally, the collected crowdsource data cannot be exchanged during the recording time with a long period in each existing place which causes the multiplicity of smartphones. Alternatively, SLAM also corresponds to the crowdsourcing methods. SLAM typically uses offline data when a person walks through closed loops, whereas, the high computational workload is required to operate the significant result. Regarding the improved accuracy, the integration of the machine learning and filtering approach is reviewed in this paper. Belonging to the linear and nonlinear constraints, most classification and clustering algorithms are able to compute the accuracy score and predict the value of the nearest location.  
Furthermore, the deep learning neural network (DNN) becomes a modernist solution for huge data amounts of multi-story buildings. It can work well in a reduced training data dimension and extract more effective features from successive samples. For indoor location research, statistical and empirical methods will be very effective guidelines upon the different finding ways. Particularly, localization approaches will also confront diverse protocol, latency and different radio waves. Moreover, the accuracy rate relies on the applications' diversity and the performance of algorithms. Location-aware computing is still affirming in the research trend.  
Conceptualization, K.Z.A. and M.S.A.; methodology, K.Z.A.; software, M.T.S.; validation, A.A., C.P.L. and T.C.P.; formal analysis, F.H.; investigation, K.Z.A. and M.S.A.; resources, T.K.G.; data curation, K.Z.A.; writing--original draft preparation, K.Z.A. and M.S.A.; writing--review and editing, F.H. and C.P.T.; visualization, K.Z.A. and M.S.A.; supervision, T.K.G. and M.T.S.; project administration, T.K.G. and F.H.; funding acquisition, T.K.G., A.A. and W.H.Y. All authors have read and agreed to the published version of the manuscript.  
The R&D work and manuscript publication fees were funded by Project name \"Mobile IOT: Location Aware\" with bearing number (MMUE/180025).  
Special thanks to Multimedia University, Telecom Malaysia, and ICT Division Bangladesh for providing the comprehensive financial assistance for this research. Thanks also to FRGS funding body for supporting the project title \"Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation\" (grant no. FRGS/1/2018/TK08/MMU /02/1) for financial support.