RSSI and Machine Learning-Based Indoor Localization Systems for Smart Cities

The rapid expansion of the Internet of Things (IoT) and Machine Learning (ML) has significantly increased the demand for Location-Based Services (LBS) in today’s world. Among these services, indoor positioning and navigation have emerged as crucial components, driving the growth of indoor localization systems. However, using GPS in indoor environments is impractical, leading to a surge in interest in Received Signal Strength Indicator (RSSI) and machine learning-based algorithms for in-building localization and navigation in recent years. This paper aims to provide a comprehensive review of the technologies, applications, and future research directions of ML-based indoor localization for smart cities. Additionally, it examines the potential of ML algorithms in improving localization accuracy and performance in indoor environments

Positioning of multiple unmanned aerial vehicle base stations in future wireless network

Abstract Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS’s limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an ℓ 0 minimization problem. This is a combinatorial, NP-hard problem. We approximate the ℓ 0 minimization problem as non-combinatorial ℓ 1 -norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVABSs simultaneously while satisfying the constraints