775 research outputs found
Channel State Information from pure communication to sense and track human motion: A survey
Human motion detection and activity recognition are becoming vital for the applications in
smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to
track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
Adaptive Control of IoT/M2M Devices in Smart Buildings using Heterogeneous Wireless Networks
With the rapid development of wireless communication technology, the Internet
of Things (IoT) and Machine-to-Machine (M2M) are becoming essential for many
applications. One of the most emblematic IoT/M2M applications is smart
buildings. The current Building Automation Systems (BAS) are limited by many
factors, including the lack of integration of IoT and M2M technologies,
unfriendly user interfacing, and the lack of a convergent solution. Therefore,
this paper proposes a better approach of using heterogeneous wireless networks
consisting of Wireless Sensor Networks (WSNs) and Mobile Cellular Networks
(MCNs) for IoT/M2M smart building systems. One of the most significant outcomes
of this research is to provide accurate readings to the server, and very low
latency, through which users can easily control and monitor remotely the
proposed system that consists of several innovative services, namely smart
parking, garden irrigation automation, intrusion alarm, smart door, fire and
gas detection, smart lighting, smart medication reminder, and indoor air
quality monitoring. All these services are designed and implemented to control
and monitor from afar the building via our free mobile application named Raniso
which is a local server that allows remote control of the building. This
IoT/M2M smart building system is customizable to meet the needs of users,
improving safety and quality of life while reducing energy consumption.
Additionally, it helps prevent the loss of resources and human lives by
detecting and managing risks.Comment: Accepted in IEEE Sensors Journa
Wi-Fi Sensing: Applications and Challenges
Wi-Fi technology has strong potentials in indoor and outdoor sensing
applications, it has several important features which makes it an appealing
option compared to other sensing technologies. This paper presents a survey on
different applications of Wi-Fi based sensing systems such as elderly people
monitoring, activity classification, gesture recognition, people counting,
through the wall sensing, behind the corner sensing, and many other
applications. The challenges and interesting future directions are also
highlighted
Intelligent Image Capturing Alarm System Using Raspberry Pi
Home surveillance system assumes an essential part in this present day living style to help recognizing illegal activities. In this proposed paper, an intelligent image capturing alarm system to protect locker was developed. Raspberry Pi 2 is used as the main controller (server). At the point when any conceivable intrusion is identified, a webcam installed to Raspberry Pi 2 will capture the picture of the intruder. In the meantime, the spotlight or light of the house which represented by an LED will be turned "ON" alongside an alarm sound from a buzzer which is fixed as an output. Taking everything into account, this improvement offers reasonable and easy to use surveillance alarm system
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey
Due to the fact that roughly sixty percent of the human body is essentially
composed of water, the human body is inherently a conductive object, being able
to, firstly, form an inherent electric field from the body to the surroundings
and secondly, deform the distribution of an existing electric field near the
body. Body-area capacitive sensing, also called body-area electric field
sensing, is becoming a promising alternative for wearable devices to accomplish
certain tasks in human activity recognition and human-computer interaction.
Over the last decade, researchers have explored plentiful novel sensing systems
backed by the body-area electric field. On the other hand, despite the
pervasive exploration of the body-area electric field, a comprehensive survey
does not exist for an enlightening guideline. Moreover, the various hardware
implementations, applied algorithms, and targeted applications result in a
challenging task to achieve a systematic overview of the subject. This paper
aims to fill in the gap by comprehensively summarizing the existing works on
body-area capacitive sensing so that researchers can have a better view of the
current exploration status. To this end, we first sorted the explorations into
three domains according to the involved body forms: body-part electric field,
whole-body electric field, and body-to-body electric field, and enumerated the
state-of-art works in the domains with a detailed survey of the backed sensing
tricks and targeted applications. We then summarized the three types of sensing
frontends in circuit design, which is the most critical part in body-area
capacitive sensing, and analyzed the data processing pipeline categorized into
three kinds of approaches. Finally, we described the challenges and outlooks of
body-area electric sensing
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
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