Unified Fingerprinting/Ranging Localization for e-Healthcare Systems

Indoor localization constitutes one of the main pillars for the provision of context-aware services in e-Healthcare systems. Fingerprinting and ranging have traditionally been placed facing each other to meet the localization requirements. However, accurate fingerprinting may worth the exhaustive calibration effort in some critical areas while easy-to-deploy ranging can provide adequate accuracy for certain non-critical spaces. In this paper, we propose a framework and algorithm for seamless integration of both systems from the Bayesian perspective. We assessed the proposed framework with conventional WiFi devices in comparison to conventional implementations. The presented techniques exhibit a remarkable accuracy improvement while they avoid computationally exhaustive algorithms that impede real-time operation

Design and Implementation of an RSSI-Based Bluetooth Low Energy Indoor Localization System

Indoor Positioning System (IPS) is a crucial technology that enables medical staff and hospital managements to accurately locate and track persons or assets inside the medical buildings. Among other technologies, Bluetooth Low Energy (BLE) can be exploited for achieving an energy-efficient and low-cost solution. This work presents the design and implementation of an received signal strength indicator (RSSI)-based indoor localization system. The paper shows the implementation of a low complex weighted k-Nearest Neighbors algorithm that processes raw RSSI data from connection-less iBeacon's. The designed hardware and firmware are implemented around the low-power and low-cost nRF52832 from Nordic Semiconductor. Experimental evaluation with the real-time data processing has been evaluated and presented in a 7.2 m by 7.2 m room with furniture and 5 beacon nodes. The experimental results show an average error of only 0.72 m in realistic conditions. Finally, the overall power consumption of the fixed beacon with a periodic advertisement of 100 ms is only 50 uA at 3 V, which leads to a long-lasting solution of over one year with a 500 mAh coin battery.Comment: This article has been accepted for publication in the proceedings of the 2021 IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob). DOI: 10.1109/WiMob52687.2021.960627

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

[EN] Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved

GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

A survey on wireless indoor localization from the device perspective

With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p

Unified Fingerprinting/Ranging Localization in Harsh Environments

Context-awareness in wireless sensor networks (WSNs) relies mainly on the position of objects and humans. The provision of this positional information becomes challenging in the harsh environmental conditions where WSNs are commonly deployed. With an antagonistic philosophy of design, fingerprinting and ranging have emerged as the key technologies underpinning wireless localization in harsh environments. Fingerprinting primarily focuses on accurate estimation at the expense of exhaustive calibration. Ranging mainly pursues an easy-to-deploy solution at the expense of moderate performance. In this paper, we present a resilient framework for sustained localization based on accurate fingerprinting in critical areas and light ranging in noncritical spaces. Such framework is conceived from the Bayesian perspective that facilitates the specification of recursive algorithms for real-time operation. In comparison to conventional implementations, we assessed the proposed framework in an indoor scenario with measurements gathered by commercial devices. The presented techniques noticeably outperform current approaches, enabling a flexible adaptation to the fluctuating conditions of harsh environments

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves