23 research outputs found
Channel Impulse Response-based Distributed Physical Layer Authentication
In this preliminary work, we study the problem of {\it distributed}
authentication in wireless networks. Specifically, we consider a system where
multiple Bob (sensor) nodes listen to a channel and report their {\it
correlated} measurements to a Fusion Center (FC) which makes the ultimate
authentication decision. For the feature-based authentication at the FC,
channel impulse response has been utilized as the device fingerprint.
Additionally, the {\it correlated} measurements by the Bob nodes allow us to
invoke Compressed sensing to significantly reduce the reporting overhead to the
FC. Numerical results show that: i) the detection performance of the FC is
superior to that of a single Bob-node, ii) compressed sensing leads to at least
overhead reduction on the reporting channel at the expense of a small
( dB) SNR margin to achieve the same detection performance.Comment: 6 pages, 5 figures, accepted for presentation at IEEE VTC 2017 Sprin
Countering Active Attacks on RAFT-based IoT Blockchain Networks
This paper considers an Internet of Thing (IoT) blockchain network consisting
of a leader node and various follower nodes which together implement the RAFT
consensus protocol to verify a blockchain transaction, as requested by a
blockchain client. Further, two kinds of active attacks, i.e., jamming and
impersonation, are considered on the IoT blockchain network due to the presence
of multiple {\it active} malicious nodes in the close vicinity. When the IoT
network is under the jamming attack, we utilize the stochastic geometry tool to
derive the closed-form expressions for the coverage probabilities for both
uplink and downlink IoT transmissions. On the other hand, when the IoT network
is under the impersonation attack, we propose a novel method that enables a
receive IoT node to exploit the pathloss of a transmit IoT node as its
fingerprint to implement a binary hypothesis test for transmit node
identification. To this end, we also provide the closed-form expressions for
the probabilities of false alarm, missed detection and miss-classification.
Finally, we present detailed simulation results that indicate the following: i)
the coverage probability improves as the jammers' locations move away from the
IoT network, ii) the three error probabilities decrease as a function of the
link quality
Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm
In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g.,
software-defined radios (SDR)-based methods have emerged as promising
candidates for intelligent remote sensing of human vitals, and could help in
containment of contagious viruses like covid19. To this end, this work utilizes
the universal software radio peripherals (USRP)-based SDRs along with classical
machine learning (ML) methods to design a non-contact method to monitor
different breathing abnormalities. Under our proposed method, a subject rests
his/her hand on a table in between the transmit and receive antennas, while an
orthogonal frequency division multiplexing (OFDM) signal passes through the
hand. Subsequently, the receiver extracts the channel frequency response
(basically, fine-grained wireless channel state information), and feeds it to
various ML algorithms which eventually classify between different breathing
abnormalities. Among all classifiers, linear SVM classifier resulted in a
maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner,
data was collected by doing real-time experiments on 4 subjects in a lab
environment. For label generation purpose, the breathing of the subjects was
classified into three classes: normal, fast, and slow breathing. Furthermore,
in addition to our proposed method (where only a hand is exposed to RF
signals), we also implemented and tested the state-of-the-art method (where
full chest is exposed to RF radiation). The performance comparison of the two
methods reveals a trade-off, i.e., the accuracy of our proposed method is
slightly inferior but our method results in minimal body exposure to RF
radiation, compared to the benchmark method
Non-Contact Monitoring of Dehydration using RF Data Collected off the Chest and the Hand
We report a novel non-contact method for dehydration monitoring. We utilize a
transmit software defined radio (SDR) that impinges a wideband radio frequency
(RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a
subject who sits nearby. Further, another SDR in the closed vicinity collects
the RF signals reflected off the chest (or passed through the hand) of the
subject. Note that the two SDRs exchange orthogonal frequency division
multiplexing (OFDM) signal, whose individual subcarriers get modulated once it
reflects off (passes through) the chest (the hand) of the subject. This way,
the signal collected by the receive SDR consists of channel frequency response
(CFR) that captures the variation in the blood osmolality due to dehydration.
The received raw CFR data is then passed through a handful of machine learning
(ML) classifiers which once trained, output the classification result (i.e.,
whether a subject is hydrated or dehydrated). For the purpose of training our
ML classifiers, we have constructed our custom HCDDM-RF-5 dataset by collecting
data from 5 Muslim subjects (before and after sunset) who were fasting during
the month of Ramadan. Specifically, we have implemented and tested the
following ML classifiers (and their variants): K-nearest neighbour (KNN),
support vector machine (SVM), decision tree (DT), ensemble classifier, and
neural network classifier. Among all the classifiers, the neural network
classifier acheived the best classification accuracy, i.e., an accuracy of
93.8% for the proposed CBDM method, and an accuracy of 96.15% for the proposed
HBDM method. Compared to prior work where the reported accuracy is 97.83%, our
proposed non-contact method is slightly inferior (as we report a maximum
accuracy of 96.15%); nevertheless, the advantages of our non-contact
dehydration method speak for themselves.Comment: 8 pages, 9 figures, 2 table
On the Effective Capacity of IRS-assisted wireless communication
We consider futuristic, intelligent reflecting surfaces (IRS)-aided
communication between a base station (BS) and a user equipment (UE) for two
distinct scenarios: a single-input, single-output (SISO) system whereby the BS
has a single antenna, and a multi-input, single-output (MISO) system whereby
the BS has multiple antennas. For the considered IRS-assisted downlink, we
compute the effective capacity (EC), which is a quantitative measure of the
statistical quality-of-service (QoS) offered by a communication system
experiencing random fading. For our analysis, we consider the two widely-known
assumptions on channel state information (CSI) -- i.e., perfect CSI and no CSI,
at the BS. Thereafter, we first derive the distribution of the signal-to-noise
ratio (SNR) for both SISO and MISO scenarios, and subsequently derive
closed-form expressions for the EC under perfect CSI and no CSI cases, for both
SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no
CSI, it turns out that the EC could be maximized further by searching for an
optimal transmission rate , which is computed by exploiting the iterative
gradient-descent method. We provide extensive simulation results which
investigate the impact of the various system parameters, e.g., QoS exponent,
power budget, number of transmit antennas at the BS, number of reflective
elements at the IRS etc., on the EC of the system
Preventing Identity Attacks in RFID Backscatter Communication Systems: a Physical-Layer Approach
This work considers identity attack on a radio-frequency identification (RFID)-based backscatter communication system. Specifically, we consider a singlereader, single-tag RFID system whereby the reader and the tag undergo two-way signaling which enables the reader to extract the tag ID in order to authenticate the legitimate tag (L-tag). We then consider a scenario whereby a malicious tag (M-tag)—having the same ID as the Ltag programmed in its memory by a wizard—attempts to deceive the reader by pretending to be the L-tag. To this end, we counter the identity attack by exploiting the non-reciprocity of the end-to-end channel (i.e., the residual channel) between the reader and the tag as the fingerprint of the tag. The passive nature of the tag(s) (and thus, lack of any computational platform at the tag) implies that the proposed light-weight physical-layer authentication method is implemented at the reader. To be concrete, in our proposed scheme, the reader acquires the raw data via two-way (challenge-response) message exchange mechanism, does least-squares estimation to extract the fingerprint, and does binary hypothesis testing to do authentication. We also provide closed-form expressions for the two error probabilities of interest (i.e., false alarm and missed detection). Simulation results attest to the efficacy of the proposed method