High-precision indoor localisation is becoming a necessity with novel location-based services that are emerging around 5G. The deployment of high-precision indoor location technologies is usually costly due to the high density of reference points. In this work, we propose the opportunistic fusion of several different technologies, such as ultra-wide band (UWB) and WiFi fine-time measurement (FTM), in order to improve the performance of location. We also propose the use of fusion with cellular networks, such as LTE, to complement these technologies where the number of reference points is under-determined, increasing the availability of the location service. Maximum likelihood estimation (MLE) is presented to weight the different reference points to eliminate outliers, and several searching methods are presented and evaluated for the localisation algorithm. An experimental setup is used to validate the presented system, using UWB and WiFi FTM due to their incorporation in the latest flagship smartphones. It is shown that the use of multi-technology fusion in trilateration algorithm remarkably optimises the precise coverage area. In addition, it reduces the positioning error by over-determining the positioning problem. This technique reduces the costs of any network deployment oriented to location services, since a reduced number of reference points from each technology is required.

The world is currently undergoing a new industrial revolution characterized by the digitization and automation of industry through the use of Information and Communication Technologies (ICTs). The construction sector is one of the largest sectors of the industry. Most of the tasks associated with this sector are carried out at worksites that are defined by their dynamism, decentralization, temporality, and the intervention of a large number of workers, subcontractors, machinery, equipment, and materials. These characteristics make this sector a great challenge for the implementation of ICTs. In this paper, the benefits of the use of the Fifth-Generation (5G) of mobile networks in the construction industry are presented. To that end, first, the digitization and automation needs of the sector are jointly analyzed, establishing different use cases and identifying the requirements of each one. Second, the main characteristics of 5G that address these use cases are identified. Third, a global framework for the application of 5G technology to the construction industry is proposed. Finally, an overview of some directions for future work are provided.

Location will have a central role in Research and Development (R&D) towards 6G networks, both as a service offered by the network (improving the current offering of 5G) and as an input to increasingly location-aware services and network functions. To integrate location into 6G standards, it will be very important to design validation systems such as testbeds, even when the actual technology is not yet commercially available. This paper performs a review of the use cases and their requirements, enabling technologies in 6G, and challenges; and proposes a flexible testbed architecture for performing network location related R&D. This architecture will allow to deploy an evolving infrastructure which will allow early validation of 6G technologies.

Ultra-Wide Band (UWB) technology stands out as one of the most promising technologies for locating the user in indoor scenarios for the new 5G mobile generation. As a drawback, it requires a dense infrastructure. For this study, a simulation of a real environment with UWB and Long Term Evolution (LTE) base stations for positioning users is presented, tracked by an Extended Kalman Filter (EKF). The proposed method uses information that is unusable with UWB alone, and combines it with LTE location, improving the precision for the latter and enabling sparse infrastructure deployments.

Precise positioning will play a key role in future 5G/6G services. The upcoming location-based services drive the necessity of high-precision positioning to indoors. In fingerprinting, which is the most commonly used location algorithm indoors, comprehensive radio maps are essential for a precise localization service and highly influence on the result of the final position of the user. A Machine Learning (ML) algorithm that supports missing reference points information when maps are incomplete that are used during the training phase may improve the robustness and reliability of the localization service. In this work, we compare the performance of the classical fingerprinting technique and different Decision Tree Regressor (DTR)-based algorithms that are Decision Tree Adaboost (DTA), Linear Tree Adaboost (LTA) and Random Forest (RF). The experiments were carried out with real 5G and WiFi data in an indoor scenario to test the performance of the techniques. Additionally, we demonstrate the benefits of fusion of technologies when positioning with radio maps. Finally, an evaluation of the robustness from the different methods was carried out when missing information in radio maps during the training phase.

Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios.