Sensor Network-based and User-friendly User Location Discovery for Future Smart Homes

User location is crucial context information for future smart homes where a lot of location based services will be proposed. This location necessarily means that User Location Discovery (ULD) will play an important role in future smart homes. Concerns about privacy and the need to carry a mobile or a tag device within a smart home currently makes conventional ULD systems uncomfortable for users. Future smart homes will need a ULD system to consider these challenges. This paper addresses to design such a ULD system for context-aware services in future smart homes stressing on the following challenges: (i) users’ privacy, (ii) device/tag-free, and (iii) fault tolerance and accuracy. On the other hand, emerging new technologies such as Internet of Things, embedded systems, intelligent devices and machine-to-machine communication are penetrating into our daily life with more and more sensors available for use in our homes. Considering this opportunity, we propose a ULD system that is capitalizing on the prevalence of sensors or home while satisfying the aforementioned challenges. The proposed sensor network-based and user-friendly ULD system relies on different types of cheap sensors as well as a context broker with a fuzzy-based decision maker. The context broker receives context information from different types of sensors and evaluates that data using the fuzzy set theory. We demonstrate the performance of the proposed system by illustrating a use case, utilizing both an analytical model and simulation

Multiple detections application for indoor tracking using PIR sensor and Kalman filter

Recently, human tracking in multiple indoor environments is getting broadly in demand to enhance security surveillance. Traditional passive video surveillance shown that it has working ineffectively nowadays because the number of cameras has exceeded the ability of operators to monitor them. In this paper, we proposed methods of detecting human presence using Pyroelectric Infrared (PIR) Motion Sensor and tracking people in multiple indoor locations using Kalman filter-based estimation. The proposed method is implemented to analyze the movement of people within the prescribed area and the result will be presented in footprint mapping of the said area. This will further enhanced building security surveillance especially at the sensitive or restricted areas. Experiments for single target tracking in several areas are carried out to verify the application of the developed system. As the results, the maximum error for tracking trajectory reduced from 0.28m to 0.19m and average error for tracking trajectory also reduced from 0.10m to 0.07m after using Kalman filter estimation algorithm

A Bayesian strategy to enhance the performance of indoor localization systems

This work describes the probabilistic modelling af a Bayesian-based mechanism to improve location estimates of an already deployed location system by fusing its outputs with low-cost binary sensors. This mechanism takes advantege of the localization captabilities of different technologies usually present in smart environments deployments. The performance of the proposed algorithm over a real sensor deployment is evaluated using simulated and real experimental data

On Analyzing User Location Discovery Methods in Smart Homes: A Taxonomy and Survey

User Location Discovery (ULD) is a key issue in smart home ecosystems, as it plays a critical role in many applications. If a smart home management system cannot detect the actual location of the users, the desired applications may not be able to work successfully. This article proposes a new taxonomy with a broad coverage of ULD methods in terms of user satisfaction and technical features. In addition, we provide a state-of-the-art survey of ULD methods and apply our taxonomy to map these methods. Mapping contributes to gap analysis for existing ULDs and also validates the applicability and accuracy of the taxonomy. Using this systematic approach, the features and characteristics of the current ULD methods are identified (i.e., equipment and algorithms). Next, the weaknesses and advantages of these methods are analyzed utilizing ten important evaluation metrics. Although we mainly focus on smart homes, the results of this article can be generalized to other spaces such as smart offices and eHealth environments

"Development Plan for the Project: Integrated System for Patient Localization, Tracking and Monitoring in an Indoor Environment"

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Respiration and Activity Detection based on Passive Radio Sensing in Home Environments

The pervasive deployment of connected devices in modern society has significantly changed the nature of the wireless landscape, especially in the license free industrial, scientific and medical (ISM) bands. This paper introduces a deep learning enabled passive radio sensing method that can monitor human respiration and daily activities through leveraging unplanned and ever-present wireless bursts in the ISM frequency band, and can be employed as an additional data input within healthcare informatics. Wireless connected biomedical sensors (Medical Things) rely on coding and modulating of the sensor data onto wireless (radio) bursts which comply with specific physical layer standards like 802.11, 802.15.1 or 802.15.4. The increasing use of these unplanned connected sensors has led to a pell-mell of radio bursts which limit the capacity and robustness of communication channels to deliver data, whilst also increasing inter-system interference. This paper presents a novel methodology to disentangle the chaotic bursts in congested radio environments in order to provide healthcare informatics. The radio bursts are treated as pseudo noise waveforms which eliminate the requirement to extract embedded information through signal demodulation or decoding. Instead, we leverage the phase and frequency components of these radio bursts in conjunction with cross ambiguity function (CAF) processing and a Deep Transfer Network (DTN). We use 2.4GHz 802.11 (WiFi) signals to demonstrate experimentally the capability of this technique for human respiration detection (including through-the-wall), and classifying everyday but complex human motions such as standing, sitting and falling