5G positioning is an essential part of many applications, such as driverless vehicles, drone tracking, emergency services, location-based services, etc. Such positioning can attain an accuracy on the order of 1 meter or even lower in both indoor and outdoor settings due to its several advantageous aspects, which include millimeter wave signal, multiple input multiple output antennas, device-to-device communication, etc. Hence, many researchers have paid attention to the development of 5G positioning systems over the past decade. This paper, therefore, presents a state-of-the-art review of the existing 5G positioning methods along with their taxonomy and comparative analysis in several metrics including their merits and demerits. Moreover, the architecture and advantageous aspects of 5G positioning are also presented in this paper. Similar to the satellite signals, 5G signals, however, can be impeded by various obstructions, further declining the accuracy of such positioning. Moreover, many security concerns and privacy issues are also associated with 5G positioning. Thus, the development of a precise and secure 5G positioning system faces a number of challenges. Therefore, various ways to improve the 5G positioning accuracy and several solutions to address the security concerns and privacy issues of such positioning are pointed out in this study.

Smartphone sensor-based pedestrian dead reckoning (PDR) systems provide a viable solution to the problem of localization in an infrastructure-less area. Step detection (SD) and step length estimation (SLE), being two fundamental operations of the PDR based localization technique, have drawn many researchers’ attention in the recent time. Most of the existing SD and SLE methods proposed over the years, however, provide either server- or cloud-based solution that consume additional network bandwidth and suffer from increased transmission delay. Moreover, nonavailability of the inertial sensors like gyroscope, magnetometers etc. at every smartphone makes majority of the existing SLE methods less applicable to such devices. To address the above-said issues, in this paper we focus on devising an improved SLE method that would detect the pedestrian’s steps and subsequently estimate the step length in real-time by processing the accelerometer data at the device itself. Our proposed method transforms the measured acceleration values along the earth coordinate system and also applies sliding window meaning to mitigate the negative effects of the smartphone’s orientation and gravitational bias on the accuracy of SD and SLE. The performances of our proposed method are evaluated in terms of accuracy for five different users by taking the device in two different postures (handheld and trouser pocket) under two different walking modes (normal and fast) to demonstrate its efficacy. Moreover, our proposed method also obtains more than 88% accuracy (median) for all participants when the device is placed in trouser pocket under two different walking modes.