Wi-Fi Sensing for Indoor Localization via Channel State Information: A Survey

Wireless Fidelity (Wi-Fi) sensing utilization has been widespread, especially for human behavior/activity recognition. It provides high flexibility since it does not require the person/object to carry any device known as device-free. This "passive" concept is also helpful for another application of Wi-Fi sensing, i.e., indoor localization. The "sensing" is conducted using particular parameters extracted from communication links of Wi-Fi devices, i.e., channel state information (CSI). This paper explores the recent trends in CSI-based indoor localization with Wi-Fi technology as its core, including their advantages, challenges, and future directions. We found tremendous benefits can be gained by employing Wi-Fi sensing in localization supported by its performance and integrability for other intelligent systems for activity recognition

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Wireless Positioning and Tracking for Internet of Things in GPS-denied Environments

Wireless positioning and tracking have long been a critical technology for various applications such as indoor/outdoor navigation, surveillance, tracking of assets and employees, and guided tours, among others. Proliferation of Internet of Things (IoT) devices, the evolution of smart cities, and vulnerabilities of traditional localization technologies to cyber-attacks such as jamming and spoofing of GPS necessitate development of novel radio frequency (RF) localization and tracking technologies that are accurate, energy-efficient, robust, scalable, non-invasive and secure. The main challenges that are considered in this research work are obtaining fundamental limits of localization accuracy using received signal strength (RSS) information with directional antennas, and use of burst and intermittent measurements for localization. In this dissertation, we consider various RSS-based techniques that rely on existing wireless infrastructures to obtain location information of corresponding IoT devices. In the first approach, we present a detailed study on localization accuracy of UHF RF IDentification (RFID) systems considering realistic radiation pattern of directional antennas. Radiation patterns of antennas and antenna arrays may significantly affect RSS in wireless networks. The sensitivity of tag antennas and receiver antennas play a crucial role. In this research, we obtain the fundamental limits of localization accuracy considering radiation patterns and sensitivity of the antennas by deriving Cramer-Rao Lower Bounds (CRLBs) using estimation theory techniques. In the second approach, we consider a millimeter Wave (mmWave) system with linear antenna array using beamforming radiation patterns to localize user equipment in an indoor environment. In the third approach, we introduce a tracking and occupancy monitoring system that uses ambient, bursty, and intermittent WiFi probe requests radiated from mobile devices. Burst and intermittent signals are prominent characteristics of IoT devices; using these features, we propose a tracking technique that uses interacting multiple models (IMM) with Kalman filtering. Finally, we tackle the problem of indoor UAV navigation to a wireless source using its Rayleigh fading RSS measurements. We propose a UAV navigation technique based on Q-learning that is a model-free reinforcement learning technique to tackle the variation in the RSS caused by Rayleigh fading

Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust