Exploring LoRa for Sensing

2021 ◽  
Vol 25 (2) ◽  
pp. 33-37
Author(s):  
Fusang Zhang ◽  
Zhaoxin Chang ◽  
Jie Xiong ◽  
Daqing Zhang
Keyword(s):  
Long Range ◽  
Small Range ◽  
Wireless Sensing ◽  
60 Ghz ◽  
Wireless Technologies ◽  
Sensing Range ◽  
Wireless Signal ◽  
New Applications ◽  
Passive Localization ◽  
Activity Sensing

Wireless sensing received a great amount of attention in recent years and various wireless technologies have been exploited for sensing, including WiFi [1], RFID [2], ultrasound [3], 60 GHz mmWave [4] and visible light [5]. The key advantage of wireless sensing over traditional sensing is that the target does not need to be equipped with any sensor(s) and the wireless signal itself is being used for sensing. Exciting new applications have been enabled, such as passive localization [6] and contactless human activity sensing [7]. While promising in many aspects, one key limitation of current wireless sensing techniques is the very small sensing range. This is because while both direct path and reflection path signals are used for communication, only the weak target-reflection signals can be used for sensing. Take Wi-Fi as an example: the communication range can reach 20 to 50 meters indoors but its sensing range is merely 4 to 8 meters. This small range further limits the through-wall sensing capability of Wi-Fi. On the other hand, many applications do require long-range and through-wall sensing capability. In a fire rescue scenario, the sensing device cannot be placed close to the building, and the long-range through-wall sensing capabilities are critical for detecting people deep inside the building. Table I summarizes the sensing range of existing wireless technologies. We can see that long-range through-wall sensing is still missing with wireless sensing.

Pushing the Limits of Long Range Wireless Sensing with LoRa

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Binbin Xie ◽  
Yuqing Yin ◽  
Jie Xiong
Keyword(s):  
Long Range ◽  
Critical Issue ◽  
Research Area ◽  
Coarse Grained ◽  
Wireless Sensing ◽  
Fine Grained ◽  
Sensing Range ◽  
Human Respiration ◽  
New Research ◽  
Induced Signal

Wireless sensing is an exciting new research area which enables a large variety of applications ranging from coarse-grained daily activity recognition to fine-grained vital sign monitoring. While promising in many aspects, one critical issue is the limited sensing range because weak reflection signals are used for sensing. Recently, LoRa signals are exploited for wireless sensing, moving a big step towards long-range sensing. Although promising, there is still a huge room for improvement. In this work, we qualitatively characterize the relationship between target movements and target-induced signal variations, and propose signal processing methods to enlarge the induced signal variation to achieve a longer sensing range. Experiment results show that the proposed system (1) pushes the contact-free sensing range of human walking from the state-of-the-art 50 m to 120 m; (2) achieves a sensing range of 75 m for fine-grained respiration sensing; and (3) demonstrates human respiration sensing even through seven concrete walls.

New applications of ultrasound-based muscle activity sensing for rehabilitation engineering

2021 ◽  
Vol 150 (4) ◽  
pp. A289-A289
Author(s):  
Siddhartha Sikdar ◽  
Ahmed Bashatah ◽  
Joseph Majdi ◽  
Parag V. Chitnis
Keyword(s):  
Muscle Activity ◽  
Rehabilitation Engineering ◽  
New Applications ◽  
Activity Sensing

High-Speed Cable Replacement for Multimedia Streaming in Wireless Personal and Local Area Networks

2012 ◽  
pp. 51-67
Author(s):  
Chun-Ting Chou
Keyword(s):  
High Speed ◽  
Local Area ◽  
Multimedia Streaming ◽  
Wireless Transmission ◽  
Multimedia Content ◽  
60 Ghz ◽  
High Definition ◽  
Wireless Technologies ◽  
Ieee 802.11Ad ◽  
Standard Quality

The multimedia content is migrating promptly from standard quality to high-definition and even 3D. As a result, existing wireless technologies can no longer support multimedia streaming as their wired counterparts. To overcome this problem, new wireless technologies that support multi Gbps wireless transmission are desperately needed. In this chapter, we focus on the promising 60 GHz technology and investigate two important standards including ECMA-387 and IEEE 802.11ad standards. Key designs of the two standards are discussed and qualitatively evaluated. Based on our evaluation, one can select the solution that suits best for the targeted applications.

21 Gbps OFDM Wireless Signal Transmission at 60 GHz Using a Simple IMDD Radio-over-Fiber System

2010 ◽  
Author(s):  
Anthony Ng’oma ◽  
Po-Tsung Shih ◽  
Jacob George ◽  
Frank Annunziata ◽  
Michael Sauer ◽  
...  
Keyword(s):  
Signal Transmission ◽  
Radio Over Fiber ◽  
60 Ghz ◽  
Fiber System ◽  
Wireless Signal ◽  
Radio Over Fiber System

scholarly journals LoPT : LoRa Penetration Testing Tool

2019 ◽  
Vol 8 (9S2) ◽  
pp. 374-379
Keyword(s):  
Low Power ◽  
Long Range ◽  
Modern World ◽  
Wireless Technologies ◽  
Penetration Testing ◽  
Steady Rate ◽  
Testing Tool ◽  
Iot Devices ◽  
Long Range Communication ◽  
Security Enhancements

The advent of Wireless technologies and IOT are currently ruling the modern world. Everything is going to become Things in future. As the technology progresses , the security of those technologies must also progress with an steady rate. Security tools which will help us to analyze these advanced security enhancements and protocols implemented. In this study , we are going to implement new security tool which concentrates on penetration testing of one such IOT protocol. This tool concentrates on the protocol named LoRa used for wireless long range communication in IOT. The proposed tool will explore all the possible attacks on LoRa protocol which we will see about in detail in the upcoming sections. LoPT is a new penetration testing tool which will work on LoRa (Long Range),a wireless standard used for long range low power communication on IOT devices primarily. This newly bloomed flower performs an effective domination on the field of IOT. Currently there is no existing penetration testing tool for LoRa. Though LoRa has its inbuilt security , there are major vulnerabilities which can be explored . This tool is built primarily on the concept of There’s no such thing as 100

60 GHz wireless signal transmitting gate driver for IGBT

2015 ◽  
Author(s):  
Kenichi Yamamoto ◽  
Fumio Ichihara ◽  
Kazunori Hasegawa ◽  
Masanori Tukuda ◽  
Ichiro Omura
Keyword(s):  
60 Ghz ◽  
Wireless Signal ◽  
Gate Driver

scholarly journals Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

2012 ◽  
Vol 17 (4) ◽  
pp. 9-16 ◽  
Author(s):  
Gabriel Deak ◽  
Kevin Curran ◽  
Joan Condell ◽  
Daniel Deak ◽  
Piotr Kiedrowski
Keyword(s):  
Support Vector Machine ◽  
Probabilistic Neural Network ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Quadratic ◽  
Wireless Signal ◽  
Device Free ◽  
Tracking Devices ◽  
Passive Localization

Abstract The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.

scholarly journals Lab-Based Evaluation of Device-Free Passive Localization Using Multipath Channel Information

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2383
Author(s):  
Jonas Ninnemann ◽  
Paul Schwarzbach ◽  
Andrea Jung ◽  
Oliver Michler
Keyword(s):  
Wide Band ◽  
Location Based Services ◽  
Radio Communication ◽  
Communication Signals ◽  
Environmental Mapping ◽  
Wireless Signal ◽  
Elliptical Model ◽  
Em Method ◽  
Multipath Detection ◽  
Passive Localization

The interconnection of devices, driven by the Internet of Things (IoT), enables a broad variety of smart applications and location-based services. The latter is often realized via transponder based approaches, which actively determine device positions within Wireless Sensor Networks (WSN). In addition, interpreting wireless signal measurements also enables the utilization of radar-like passive localization of objects, further enhancing the capabilities of WSN ranging from environmental mapping to multipath detection. For these approaches, the target objects are not required to hold any device nor to actively participate in the localization process. Instead, the signal delays caused by reflections at objects within the propagation environment are used to localize the object. In this work, we used Ultra-Wide Band (UWB) sensors to measure Channel Impulse Responses (CIRs) within a WSN. Determining an object position based on the CIR can be achieved by formulating an elliptical model. Based on this relation, we propose a CIR environmental mapping (CIR-EM) method, which represents a heatmap generation of the propagation environment based on the CIRs taken from radio communication signals. Along with providing imaging capabilities, this method also allows a more robust localization when compared to state-of-the-art methods. This paper provides a proof-of-concept of passive localization solely based on evaluating radio communication signals by conducting measurement campaigns in an anechoic chamber as a best-case environment. Furthermore, shortcomings due to physical layer limitations when using non-dedicated hardware and signals are investigated. Overall, this work lays a foundation for related research and further evaluation in more application-oriented scenarios.

scholarly journals Feasibility of LoRa for Smart Home Indoor Localization

2021 ◽  
Vol 11 (1) ◽  
pp. 415
Author(s):  
Kyungki Kim ◽  
Sining Li ◽  
Milad Heydariaan ◽  
Nour Smaoui ◽  
Omprakash Gnawali ◽  
...  
Keyword(s):  
Low Power ◽  
Long Range ◽  
Radio Frequency Identification ◽  
Indoor Localization ◽  
Low Cost ◽  
Smart Homes ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Wireless Technologies ◽  
Better Than

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization including WiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates.

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