The Need for New Authentication Methods for Internet of Things
draft-hsothers-iotsens-ps-01
| Document | Type | Active Internet-Draft (individual) | |
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| Authors | Dirk v. Hugo , Behcet Sarikaya | ||
| Last updated | 2022-01-26 | ||
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draft-hsothers-iotsens-ps-01
Network Working Group D. von Hugo
Internet-Draft Deutsche Telekom
Intended status: Standards Track B. Sarikaya
Expires: 29 July 2022 25 January 2022
The Need for New Authentication Methods for Internet of Things
draft-hsothers-iotsens-ps-01.txt
Abstract
The document attempts to establish the need for new authentication
methods in the Internet of Things (IoT) as a future networking area
beyond 5G going into 6G for standardization. Several scenarios are
described where the current authentication protocols do not work or
are insufficient. Next we discuss a few new approaches such as
Wireless LAN/6G sensing and LED light based which can be further
explored.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 29 July 2022.
Copyright Notice
Copyright (c) 2022 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 4
3. Need for New Authentication Models . . . . . . . . . . . . . 4
4. Academic Approaches to Sensing Based IoT Authentication . . . 5
5. IoT Authentication Protocols . . . . . . . . . . . . . . . . 6
6. IoT Authentication Problem . . . . . . . . . . . . . . . . . 7
6.1. Architectural and Procedural Issues for Future IP-based
IoT-Authentication . . . . . . . . . . . . . . . . . . . 7
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
8. Security Considerations . . . . . . . . . . . . . . . . . . . 9
9. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 9
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 9
10.1. Normative References . . . . . . . . . . . . . . . . . . 9
10.2. Informative References . . . . . . . . . . . . . . . . . 9
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 13
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
Future networking to make full use of 5G capabilities or even
resembling an evolution to beyond 5G will have to exploit a much more
heterogeneous environment in terms of network and device connectivity
technologies and applications. In addition, ease of use for
customers and human-independent operation of a multitude of devices
and machines (things) has to be provided.
Therefore current authentication models like 802.1X [IEEE802.1X]
which are based on human intervention do not fit well. Also this
model does not scale well for the Internet of Things (IoT).
We can summarize the use cases we are currently considering here:
Authenticating the device that is playing a melody, or a person has
just touched; authenticating devices, i.e. smart teapot with certain
manifests, like blinking red and blue; authenticate the device when a
camera is pointed at it; and the like [Henning].
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In looking for possible approaches for new authentication methods, we
have identified a few which will be shortly introduced in this
document.
Detection and interpretation of audio signals by microphones and
corresponding
software has been under investigation since some time and
can be achieved with high precision nowadays. Coding of haptic
information is currently under standardisation at IEEE P.1918
[P1918].
Using an objects' position to grant authentication could be achieved
via geometrical information
(as e.g., position and orientation of a trusted device like the
camera) or via radio sensing.
IEEE 802.11 has a project on Wireless LAN (WLAN sensing) and 802.11bf
task group (TG) in charge of this project [BFSFD]. Use cases for
802.11bf TG includes room sensing, i.e., presence detection, counting
the number of people in the room, localization of active people,
audio with user detection, gesture recognition at different ranges,
device proximity detection, home appliance control. There are also
health care related use cases like breathing/heart rate detection,
surveillance of persons of interest, building a 3D picture of an
environment, in car sensing for driver sleepiness detection
[BFUseCases].
TGbf is also working on Specification Framework Document with an
outline of each the functional blocks that will be a part of the
final amendment like wireless LAN sensing procedure [BFSFD]. TGbf
sensing is based on obtaining physical Channel State Information
(CSI) measurements between a transmitter and receiver WLAN nodes,
called stations (STA). Using these measurements, presence of
obstacles between a transmitter and receiver can be detected and
tracked. This way, using feature extraction and classification of
artificial intelligence (AI), more higher level tasks like human
activity recognition and object detection are available for
authentication purposes, while corresponding authentication context
information can be obtained through computation of phase differences,
etc.
TGbf Wi-Fi Sensing (SENS) is achieved by signaling between just an
initiator and a responder. TGbf may also define more effective
collaborative SENS (in short, CSENS) where multiple SENS-enabled
devices can collaborate as a group in an orderly fashion to capture
additional information about the surrounding environment [Rest21].
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2. Conventions and Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
Sensing (SENS) is defined as the usage of received Wi-Fi signals from
a Station (STA) to detect features (i.e., range, velocity, angular,
motion, presence or proximity, gesture, etc.) of intended targets
(i.e., object, human, animal, etc.) in a given environment (i.e.,
house, office, room, vehicle, enterprise, etc.).
Collaborative sensing (CSENS) defines the operation in which multiple
SENS enabled devices can collaborate as a group in an orderly fashion
to capture additional information about the surrounding environment
and allow for more precise detection thus enabling a more reliable
authentication.
Multi-band sensing is defined as sensing using both sub-7-GHz Channel
State Information (CSI) measurements that provide indication of
relatively large motions and that can propagate through obstacles
(e.g., walls) and 60-GHz Received Signal Strength Indicator (RSSI)
measurements at mmWave that provide highly-directional information
through the usage of beam forming toward a given receiver, but have
small range due to the presence of blockers (e.g., walls).
3. Need for New Authentication Models
Aim of this document is to lay ground for the need for new
authentication models in the framework of devices (e.g., machines in
IoT communication) within a (wireless or wireline-based) network.
Currently employed authentication models (such as e.g., 802.1X
certificate model) is based on a human being using the machine and
providing credentials (e.g., user name/password or a permitted
digital certificate) to the authenticator. Similarly, for user
equipment (UE) to access a cellular network the device has to be
equipped with a USIM and the user has to provide a secret key, i.e.,
PIN (Personal Identification Number). With the use case of massive
IoT (mIoT) as foreseen, e.g., in 5G and with an increasing amount of
devices within a household (smart home) and/or in the ownership of a
customer (smart watch etc.) the need for an ease-of-use admission
model arises.
Focusing on corresponding procedures starting with detection
(sensing) of a new device and subsequent mutual authenticating of the
device by and to the network a set of potential technologies are
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identified and described to allow for analysis in terms of criteria
as reliable operation (working), scalability, ease of use and
convenience, security, and many more. Sensing could be a basis for
new authentication models yet to be found because sensing (together
with intelligent interpretation using possibly neural network models)
will allow the detection of the device playing a melody, blinking red
and blue, being pointed at, or somebody just touched and the like.
Furthermore, the method should be applicable to future generations of
network and of users, upcoming new applications and devices, assuming
that todays established standard procedures do not fulfill the
requirements sufficiently.
New authentication methods could leverage collaborative and multi-
band sensing technologies to enable sensing with much higher
precision and capacity using the state-of-art equipment. Also
equally important is the use of all artificial intelligence and
neural networks research results developed by the academia.
4. Academic Approaches to Sensing Based IoT Authentication
The following list of literature on sensor data and WiFi sensing for
securing and authenticating a user and a device shows the wide range
of approaches and interest in this topic [Rest21].
[Ma], [Wang], [Zhu], [Wang2], [Qian] provide a holistic overview on
the evolution of Wi-Fi technology and on investigations in
opportunistic applications of Wi-Fi signals for gesture and motion
detection.
[Henning2] is investigating geospatial access control for IoT. There
are attribute, role and identity based, time based and geospatial
access control techniques. Real-world IoT access control policies
will be a combination of all three, leading to powerful access
control techniques to use in practice such as in university campus.
Such access control or authorization techniques will likely be used
in conjunction with these new IoT Authentication models.
Other notable literature includes [Al-Qaness] on the so-called
device-free CSI-based Wi-Fi sensing mechanism, [Pahlavan] using Wi-Fi
signals for gesture and motion detection as well as for
authentication and security, [Lui] distinguishing between Line-of-
Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in case of
obstacles appearing between the transmitter and the receiver [Guo]
studying HuAc (Human Activity Recognition) as a combination of WiFi-
based and Kinect-based activity recognition system, [FURQAN]
analyzing the wireless sensing and radio environment awareness
mechanisms, highlighting their vulnerabilities such as dependency of
sensing modes on external signals, and provides solutions for
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mitigating them, e.g., the different threats to REM (radio
environment mapping) and its consequences in a vehicular
communication scenario.
[Ma2] has studied reliable SENS algorithm for human and animal
identification. The aim is to make it resilient to spoofing and
adverse channel conditions, i.e., presence of noise and interference
from other technologies.
[Restuccia] investigates data driven algorithms, neural networks,
especially convolutional neural network (CNN) or digital signal
processing (DSP) block to classify complex sensing phenomena. Also
[Liao] and [Liao2] proposed to enhance security of industrial
wireless sensor networks (IWSNs) by neural network based algorithms
for sensor nodes' authentication and implementations in IWSNs have
shown that an improved convolution preprocessing neural network
(CPNN)-based algorithm requires few computing resources and has
extremely low latency, thus enabling a lightweight multi-node PHY-
layer authentication.
Further research on these and similar issues can be found in [Tian],
[Bai] [Axente].
5. IoT Authentication Protocols
Since IoT applications cover a broad range of domains from smart
cities, industry, and homes to personal (e.g., wearable) devices,
including security and privacy sensitive areas as e-health, and can
reach a huge number of entities the security requirements in terms of
preventing unauthorized access to data are very high. Therefore very
robust authentication mechanisms have to be applied. At the same
time depending on the specific scenario a trade-off between resources
as processing power and memory and security protocol complexity has
to be considered. Also a plethora of attack scenarios has to be in
focus as well as scalability of the considered implicit and explicit
hardware- and software-based authentication procedures. [RFC8576]
serves as a reference for details about IoT specific security
considerations including the area of authentication and documents
their specific security challenges, threat models, and possible
mitigations. Also the OAuth [RFC6749] protocol is referred to which
extends traditional client-server authentication by providing a third
party client with a token instead of allowing it to use the resource
owner's credentials to access protected resources while such token
resembles a different set of credentials than those of the resource
owner.
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6. IoT Authentication Problem
Most of the state-of-art identification techniques to authenticate
the user use finger prints a.k.a. touch id and facial identification
and they use detection by touch, accelerometer, and gyro sensors or
cameras. They are based on creating a signature, or the user's
already stored password [Wang3].
On the other hand to authenticate a device based on a set of
characteristic parameters which should be flexibly chosen by the
owner and subsequently made known to the authentication system will
require a certain level of processing and storage capacity either
within the local system components (e.g., the device itself and the
wireless point of attachment or access point) and/or within the
network (e.g., an edge cloud instance or a central data base). The
result of the detection process (e.g., radio wave analysis outcome in
terms of parameters as modulation scheme, number of carriers, and
fingerprinting) has to be compared with the required (correct)
parameter values which are safely stored within the network
components. On all levels of handling these data, i.e., storage,
processing, and transport via a communication network, the integrity
of the content has to be preserved. One should keep in mind, that
any unintended authentication request should be prevented to minimize
the risk of occasional attachment to networks and subsequent exposure
to attack to sensitive user data.
6.1. Architectural and Procedural Issues for Future IP-based IoT-
Authentication
Authentication for IoT may rely on a protocol such as 6LowPAN (Low-
power Wireless Personal Area Network) which is defined for optimizing
the efficient routing of IPv6 packets for resource constrained
machine- type communication applications.
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[RFC8995] on 'Bootstrapping Remote Secure Key Infrastructure' (BRSKI)
deals with authentication of devices, including sending
authorizations to the device as to what network they should join, and
how to authenticate that network by specifying automated
bootstrapping of an Autonomic Control Plane (ACP). Secure Key
Infrastructure (SKI) bootstrapping using manufacturer- installed
X.509 certificates combined with a manufacturer's authorizing
service, both online and offline, is called the Bootstrapping Remote
Secure Key Infrastructure (BRSKI) protocol. Bootstrapping a new
device can occur when using a routable address and a cloud service,
only link- local connectivity, or limited/disconnected networks and
includes support for deployment models with less stringent security
requirements. When the cryptographic identity of the new SKI is
successfully deployed to the device, completion of bootstrapping is
achieved. A locally issued certificate can be deployed to the device
via the established secure connection as well.
LED light based authentication attempts to authenticate hard to reach
IoT devices using LED light indicator available on the device. Here
LED light is used as an out-of-band channel in addition to a wireless
LAN peer-to-peer connection to the device using a smartphone over TLS
connection which is not secured. Smartphone initially obtains
device's public key certificate. Smartphone as the client requests
certificate fingerprint over visible LED light channel. Device
transmits fingerprint by modulating LED. Client receives data with
camera and decodes. Client compares TLS certificate fingerprint with
received fingerprint to complete authentication. LED light based
authentication does not support multiple ways of getting the hash
value from the device. Although most devices have LED type of output
leading to visible light communication, some devices have speaker
type of output and not readily visible [Lins18], [Oden18].
Note that LED light based authentication is similar to EAP-NOOB,
Nimble out-of-band authentication for EAP [RFC9140] where Zigbee or
802.15.4 channel is the main channel and blinking LED light is used
as out-of-band channel. In the main channel, the device is connected
to the Internet over 802.15.4 channel to a controller (a laptop,
acting as a Wi-Fi access point) which connects over the Internet to
AAA server as EAP server where the user has an account. In the OOB
channel, the device is connected to a smartphone using blinking LED
light and the smartphone is connected to AAA server using its 4G/5G
air interface. OOB channel enables the device to send critical data
needed i.e. a secret nonce to EAP server. EAP-NOOB protocol
architecture includes RADIUS which is used to encode EAP messages and
constrained Application Protocol, CoAP which is a simplified HTTP.
CoAP is used in transporting the nonce. EAP-NOOB requires AAA server
and user account on the server, i.e. human interaction.
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When compared to a fully certificate-based or secure key
infrastructure based authentication, however, a mechanism relying on
WiFi sensing gesture detection does not require the user to know any
key, identifier, or password for the device to be authenticated. A
pre-defined type of access to the device (e.g., physical,
photographic or video representation, unique description in terms of
parameters, etc.) shall be sufficient for authentication.
7. IANA Considerations
TBD.
8. Security Considerations
This document raises no new security concerns but tries to identify
how to increase security in future IoT by discussing the issues of
robust but easy to apply authentication mechanisms.
9. Acknowledgements
Discussions with Jan Janak, Henning Schulzrinne helped us improve the
draft.
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
10.2. Informative References
[Al-Qaness]
Al-Qaness, M.A.A., Abd Elaziz, M., Kim, S., Ewees, A.A.,
Abbasi, A.A., Alhaj, Y.A., and A. Hawbani, "Channel State
Information (CSI) from Pure Communication to Sense and
Track Human Motion: A Survey", Sensors 2019, 19(15),
3329 , July 2019.
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[Axente] Axente, M.-S., Dobre, C., Ciobanu, R.-I., and R.
Purnichescu-Purtan, "Gait Recognition as an Authentication
Method for Mobile Devices", Sensors 2020, 20, 4110 , July
2020.
[Bai] Bai, L., Zhu, L., Liu, J., Choi, J., and W. Zhang,
"Physical Layer Authentication in Wireless Communication
Networks: A Survey", Journal of Communications and
Information Networks Vol.5, No.3, September 2020.
[BFSFD] IEEE, "Institute of Electrical and Electronics Engineers,
IEEE P802.11 - TASK GROUP BF (WLAN SENSING) 11-21/0504r2
"Specification Framework for TGbf"", July 2021.
[BFUseCases]
IEEE, "Institute of Electrical and Electronics Engineers,
IEEE P802.11 - TASK GROUP BF (WLAN SENSING) 11-20/1712r2
"WiFi Sensing Use Cases"", January 2021.
[FURQAN] Furqan, H.M., Solaija, M.S.J., Tuerkmen, H., and H.
Arslan, "Wireless Communication, Sensing, and REM: A
Security Perspective", IEEE Open Journal of the
Communications Society Vol. 2 , January 2021.
[Guo] Guo, L., Wang, L., Liu, J., Zhou, W., and B. Lu, "HuAc:
Human Activity Recognition Using Crowdsourced WiFi Signals
and Skeleton Data", Hindawi Wireless Communications and
Mobile Computing, Volume 2018 , February 2021.
[Henning] Schulzrinne, H., "Do We Still Need Wi-Fi in the Era of 5G
(and 6G)?", February 2021.
[Henning2] Jan Janak, Luoyao Hao and Henning Schulzrinne, ., "How do
we program the Internet of Things at scale?", September
2021.
[I-D.irtf-t2trg-secure-bootstrapping-00]
Sethi, M., Sarikaya, B., and D. Garcia-Carrillo, "Secure
IoT Bootstrapping: A Survey", Work in Progress, Internet-
Draft, draft-irtf-t2trg-secure-bootstrapping-00, 7 April
2021, <https://www.ietf.org/archive/id/draft-irtf-t2trg-
secure-bootstrapping-00.txt>.
[IEEE802.11]
IEEE, "IEEE Std. 802.11-2016", December 2016,
<https://standards.ieee.org/findstds/
standard/802.11-2016.html>.
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[IEEE802.1X]
IEEE, "Institute of Electrical and Electronics Engineers,
"802.1X - Port Based Network Access Control"", January
2020.
[Liao] Liao, R.-F., Wen, H., Wu, J., Pan, F., Xu, A., Jiang, Y.,
Xie, F., and M. Cao, "Deep-learning-based physical layer
authentication for industrial wireless sensor networks",
Sensors 2019, 19(11), 2440 , May 2019.
[Liao2] Liao, R.-F., Wen, H., Wen, H., Xie, F., Pan, F., Pan, F.,
and F. Xie, "Multiuser Physical Layer Authentication in
Internet of Things With Data Augmentation", IEEE Internet
of Things Journal, vol. 7, no. 3, pp. 2077-2088 , March
2020.
[Lins18] Linssen, A., "Secure Authentication of Remote IoT Devices
Using Visible Light Communication: Transmitter Design and
Implementation", Columbia University , 2018,
<https://www.cs.columbia.edu/~hgs/papers/
Lins18_Secure.pdf>.
[Lui] Liu, J., Wang, L., Fang, J., Guo, L., Lu, B., and L. Shu,
"Multi-Target Intense Human Motion Analysis and Detection
Using Channel State Information", Sensors 2018, 18(10),
3379 , October 2018.
[Ma] Ma, Y., Arshad, et al, S., and , "Location-and Person-
Independent Activity Recognition with WiFi, Deep Neural
Networks, and Reinforcement Learning,", 2021.
[Ma2] Ma, Y. and G. Zhou, et al, "WiFi Sensing with Channel
State Information: A Survey,", ACM Computing Surveys
(CSUR), , vol. 52, no. 3, pp. 1-36, 2019.
[Oden18] Odental, H., "Secure Authentication of Remote IoT Devices
Using Visible Light Communication: Receiver Design and
Implementation", Columbia University , 2018,
<https://www.cs.columbia.edu/~hgs/papers/
Oden18_Secure.pdf>.
[P1918] IEEE Standards Working Group 1918.1, "Tactile Internet",
July 2016.
[Pahlavan] Pahlavan, K. and P. Krishnamurthy, "Evolution and Impact
of Wi Fi Technology and Applications: A Historical
Perspective", Springer Science+Business Media, LLC, part
of Springer Nature 2020 , November 2020.
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[Qian] Xian, K. and C. Wu, et al, "Widar: Decimeter-level Passive
Tracking via Velocity Monitoring with Commodity WiFi,",
Proc. of ACM MobiCom, , 2017.
[Rest21] Restuccia, F., "IEEE 802.11bf: Toward Ubiquitous Wi-Fi
Sensing", arXiv preprint arXiv:2103.14918 7 pages, March
2021.
[Restuccia]
Restuccia, F. and T. Melodia, "Deep Learning at the
Physical Layer: System Challenges and Applications to 5G
and Beyond,", IEEE Communications Magazine, , vol. 58,
no. 10, pp. 58-64, 2020.
[RFC6749] Hardt, D., Ed., "The OAuth 2.0 Authorization Framework",
RFC 6749, DOI 10.17487/RFC6749, October 2012,
<https://www.rfc-editor.org/info/rfc6749>.
[RFC8576] Garcia-Morchon, O., Kumar, S., and M. Sethi, "Internet of
Things (IoT) Security: State of the Art and Challenges",
RFC 8576, DOI 10.17487/RFC8576, April 2019,
<https://www.rfc-editor.org/info/rfc8576>.
[RFC8995] Pritikin, M., Richardson, M., Eckert, T., Behringer, M.,
and K. Watsen, "Bootstrapping Remote Secure Key
Infrastructure (BRSKI)", RFC 8995, DOI 10.17487/RFC8995,
May 2021, <https://www.rfc-editor.org/info/rfc8995>.
[RFC9140] Aura, T., Sethi, M., and A. Peltonen, "Nimble Out-of-Band
Authentication for EAP (EAP-NOOB)", RFC 9140,
DOI 10.17487/RFC9140, December 2021,
<https://www.rfc-editor.org/info/rfc9140>.
[Tian] Tian, Q., Lin, Y., Guo, X., Wang, J., AlFarraj, O., and A.
Tolba, "An Identity Authentication Method of a MIoT Device
Based on Radio Frequency (RF) Fingerprint Technology",
Sensors 2020, 20(4), 1213 , February 2020.
[Wang] Wang, X. and C. Yang, et al, "TensorBeat: Tensor
Decomposition for Monitoring Multiperson Breathing Beats
with Commodity WiFi,", ACM Transactions on Intelligent
Systems and Technology (TIST), , vol. 9, no. 1, pp. 1-27,
2017.
[Wang2] Wang, X. and C. Yang, et al, "PhaseBeat: Exploiting CSI
Phase Data for Vital Sign Monitoring with Commodity WiFi
Devices,", Proc. of IEEE ICDCS, , 2017.
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[Wang3] Wang, H., Lymberopoulos, D., and J. Liu, "Sensor-Based
User Authentication", EWSN 2015, LNCS 8965, 168 , 2015.
[Zhu] Xiao, et al, F., "R-TTWD: Robust device-free through-the-
wall detection of moving human with WiFi,", IEEE Journal
on Selected Areas in Communications, , vol. 35, no. 5,
pp. 1090-1103, 2017.
Acknowledgements
Authors' Addresses
Dirk von Hugo
Deutsche Telekom
Deutsche-Telekom-Allee 9
64295 Darmstadt
Germany
Email: Dirk.von-Hugo@telekom.de
Behcet Sarikaya
Email: sarikaya@ieee.org
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