In this paper, we propose a novel lossless data compression framework, called Stochastic Pattern Discrete Flow (SPDFlow), designed for lightweight IoT applications. SPDFlow integrates normalizing flow models with range Asymmetric Numeral Systems (rANS) to efficiently compress high-frequency and stochastic time-series smart metering data. Building on advances in invertible flow-based distribution mapping, we design an online compression scheme optimized for electricity usage profiles sampled from tens of milliseconds to several seconds. The proposed linear and lightweight architecture enables real-time deployment on resource-constrained edge devices while maintaining robust modeling capability under unpredictable, event-driven peaks. A customized discretization strategy allows efficient encoding of wide-ranging measurement values. Comprehensive theoretical analysis and experiments across diverse datasets verify SPDFlow’s superior compression performance compared with both traditional and recent learning-based methods achieving up to 31% data volume reduction under short update intervals. Furthermore, real-world deployment on Raspberry Pi devices confirms its practical feasibility. To the best of our knowledge, this is the first work to explore lossless compression of time-series load profiles using normalizing flows.

Recently, physics-informed neural networks (PINNs) have gained attention in the scientific community for their potential to solve partial differential equations (PDEs). However, they face challenges related to resource efficiency and slow convergence. Adaptive sampling methods, which prioritize collocation points with high residuals, improve both efficiency and accuracy. However, these methods often neglect points with medium or low residuals, which can affect stability as the complexity of the model increases. In this paper, we investigate this limitation and show that high residual-based approaches require stricter learning rate bounds to ensure stability. To address this, we propose a Langevin dynamics-based Adaptive Sampling (LAS) framework that is robust to various learning rates and model complexities. Our experiments demonstrate that the proposed method outperforms existing approaches in terms of relative $L^{2}$ error, and stability across a range of environments, including high-dimensional PDEs where Monte Carlo integration-based methods typically suffer from instability.

Ride-hailing has become a vital component of urban transportation due to its flexibility and efficiency in meeting diverse passenger needs. Accurate demand estimation, particularly in identifying origin-destination (OD) pairs, is crucial for optimizing services for ride-hailing providers and drivers and ensuring efficient resource allocation. This study proposes an alternative approach to demand estimation using public transportation card data, addressing privacy concerns associated with directly collecting OD data from ride-hailing services. By analyzing hourly recorded departure and arrival data between administrative districts, the proposed statistical model estimates ride-hailing demand patterns, capturing both spatial and temporal dynamics across urban areas. Transportation data from selected urban districts in Seoul, Korea, was analyzed to validate this approach. Results demonstrate significant daily and hourly variations in demand, with distinct weekday commuting patterns and more balanced weekend flows, reflecting shifts between work-related and leisure activities. These findings highlight the model’s potential for accurately estimating demand without direct OD data, providing valuable insights for improving ride-hailing operations and supporting urban transportation planning. This approach can assist service providers in optimizing driver allocation and enhancing operational efficiency, contributing to the broader objectives of urban mobility management.

With global EV sales projected to reach 3.5 million units by 2023 and public charging stations increasing by 40%, effective management and optimization of charging infrastructure have become critical. While current research primarily focuses on models that address either users or charging stations, these approaches often overlook external factors such as weather and traffic conditions, which significantly impact driving patterns and energy consumption. The integration of these external variables into forecasting models is thus critical for enhancing prediction accuracy and infrastructure optimization. In this study, we conducted an in-depth analysis of EV charging patterns at highway rest areas. This study employed deep learning models, GRU, and LSTM architectures, trained using various data combinations. The experiments revealed that the inclusion of traffic data notably improves forecasting precision. In particular, the LSTM model demonstrated a 10.5% reduction in mean absolute percentage error(MAPE), decreasing the standard deviation from 4.95 to 3.87 when external factors were included.

In this paper, we propose a physical-layer authentication (PLA) scheme based on wireless channel responses of multiple frequency bands. Channel-based PLA has been regarded as a desirable scheme as it simplifies the authentication process by comparing received and previously acquired channel state information (CSI). However, it may fail authentication due to potential disagreements in CSI among legitimate users. Moreover, in an indoor environment, spatial correlation occasionally arises between legitimate and illegitimate channels, which can degrade the authentication reliability. To mitigate undesired CSI disagreements and spatial correlations, we propose a joint utilization of channel responses in multiple frequency bands. As spatial correlation is unlikely to arise simultaneously in different frequency bands, any correlation with illegitimate channels in one band can be compensated by the other band, providing reliable authentication even in indoor environments. Similarly, any disagreement among legitimate channels in one band can be compensated by the other band that has sufficient channel coherence time. To demonstrate the performance of our proposed scheme, we constructed a test-bed based on universal software radio peripherals. The experimental analysis shows that the proposed PLA scheme can significantly improve the authentication performance as much as orders of error reduction in failures by doubling the frequency bands in channel-based PLA.

This study proposes a novel authentication scheme that provides enhanced security for Internet of Things (IoT) applications by integrating a radio frequency (RF) physical unclonable function (PUF) with a device PUF. While traditional PUF-based authentication methods are lightweight, they are vulnerable to replay attacks because challenge-response pairs (CRPs) are exposed to adversaries over wireless channels. To prevent CRP exposure, we propose a joint scheme that integrates the physical layer features of wireless channels with those of the device PUF. Our authentication process consists of two stages: an enrollment stage and an authentication stage. During the enrollment stage, the physical features of the static random-access memory (SRAM) in the IoT devices are shared with the server, which generates a hashing model using the amplitudes of channel state information (CSI) as the RF-PUF and transfers it to the IoT device. In the authentication stage, the server and IoT devices exchange pilot signals to estimate the shared CSI. Both parties then generate the challenge information using the hashing model, which depends on the CSI. The challenge information is mapped to the response of the device PUF as the cryptographic key. If both parties are legitimate, the proposed model is updated through CSI amplitudes. We evaluated the proposed authentication protocol using a testbed based on Raspberry Pi and acquired a CSI data set and SRAM-PUF using universal software radio peripheral and Arduino, respectively. Numerical results demonstrate that our method effectively defends against diverse attacks, even in critical CRP exposure scenarios.

In this paper, we investigate a localization performance based on channel state information (CSI) of separated antenna. In multi-carrier transmission system, CSI has been considered as one of the promising signatures of radio frequency (RF) channel and employed to device localization. However, CSI can be correlated in typical indoor environment which may limit the unique representations of the signature for RF fingerprinting. To solve this, we propose a joint CSI-based device localization, exploiting the diversity of RF signatures. Even if the one CSI loses the reliability as a the fingerprint due to spatial correlation, the other CSI can support the device localization performance. For experimental evaluation, we construct the test-bed and confirmed the proposed scheme can support single CSI-based localization even if highly correlated CSI are reproduced occasionally at many locations. The test results in indoor environment show that the proposed scheme complements the single-CSI in terms of the reliability of authentication with localization of the devices.

In this paper, lossless time-series data compression scheme with bit-back asymmetric numeral systems (BB-ANS) is proposed for massive smart meter environment. As smart meters increase in deployment and connectivity, an efficient compression method is needed to transmit and save big data. Bit-back coding was introduced as a novel compression method using bayesian inference modeling. Recently, bit-back coding is combined with asymmetric numeral systems (ANS) which are stack-like structures, showing significant compression gains on several cases. ANS is an approach for entropy coding combining Huffman coding and arithmetic coding which has a first-in-last-out (FILO) form suited for bit-back coding. Compared to other compression methods, bit-back coding effectively shares priorly learned probabilistic models for encoder and decoder. In this study, variational auto-encoder (VAE) is customized to jointly learn approximate posterior and likelihood between message and latent variables. Thus, the smart meter data can be efficiently updated within finite length time-intervals. The proposed scheme is evaluated with the actual smart meter dataset and the results demonstrate the superiority of BB-ANS in compression ratios over other state-of-the-art lossless compression schemes. To the best of our knowledge, this study is the first attempt to apply bit-back coding to time-series smart metering data, enabling efficient data compression with deep generative models.

For machine type communication with random access (RA) protocol, finding optimal policy using deep reinforcement learning (DRL) is being actively investigated for various quality of service requirements. In particular, it was shown that throughput-based reward function in DRL can derive policies that outperform conventional exponential backoff (EB)-based algorithms in terms of throughput and fairness. However, age of information (AoI), which is a measure of the freshness of data, was not addressed in the process of training the DRL agent and only used as a measure of fairness. It has been theoretically proven that, even in the simplest queuing system, maximizing throughput does not guarantee minimizing AoI. In this paper, we proposed a novel DRL scheme to directly optimize AoI in a slotted ALOHA RA channel. By taking into account urgency and packet age as extra local information for a reward, AoI could be improved while preserving the throughput. Numerical simulations showed that the proposed DRL approach could achieve an improvement of 22.04% in AoI performance, with a marginal throughput loss of around 3.95%, compared to the existing throughput-based DRL method.

In this paper, we consider a compressive sensing (CS) based compression method for aggregated power signals of smart meters in advanced metering infrastructure (AMI). If detailed power signals with high sampling rates are available for energy management systems, advanced services such as demand response and power disaggregation can be provided. However, typical smart meters have limited bandwidth resources and may require aggressive data compression for supporting the intensively sampled data. In order to alleviate the overhead for the detailed power signal, a CS based compression scheme is proposed in this study to take advantage of the sparsity of power signals based on the appliance load superposition modeling. Through simulations, the CS based compression schemes demonstrate improved energy efficiency while maintaining good reconstruction performance, compared to the wavelet based compression.

In this study, a novel inter-track interference (ITI) cancelling scheme is proposed for the interlaced magnetic recording (IMR). Recently proposed IMR shows an areal density capability (ADC) gain by recording tracks in an interlaced manner with separately optimized linear densities, while the severe ITI at the double-sided squeezed tracks limits the extra ADC growth. Due to the inherent frequency offsets between the target and side tracks in IMR, ITI in the squeezed track read-back signal cannot be suppressed effectively by conventional schemes. In order to account for the frequency mismatch effect, the proposed asynchronous ITI cancellation (A-ITIC) scheme estimates and subtracts the ITI contributions from its side tracks in the higher and lower frequency domains respectively, which enables efficient ITI mitigation for extra ADC growth. To estimate ITI responses from the side track data asynchronously, the interpolation filter based timing adjustment scheme is employed for the oversampled read-back signal. Then, the ITI signal is reconstructed jointly with two side track data in the oversampled domain, and realigned to the baud rate read samples by another set of interpolation filters. By subtracting the ITI estimate from the baud rate read samples, A-ITIC provides improved bit error rate (BER) performance, particularly for off-track read, and enables additional track density push for extra ADC gain. The proposed A-ITIC performance is numerically evaluated by the grain-level magnetic recording channel simulator near 1 Tb/inch square scenario. BER off-track performance can be improved by the proposed A-ITIC, and the track density can be pushed by a factor of 1.077, which corresponds to 7.7% extra ADC gain on top of the legacy IMR gain.

For secure transmissions, we consider direct sequence/spread spectrum (DS/SS) systems where pseudo-random (PN) sequences are used for spreading. To generate PN sequences, linear feedback shift registers (LFSRs) are employed, which are also used to generate keystreams in stream ciphers. For steam ciphers that use LFSRs, there are known attacks including correlation attacks that can regenerate keystreams. Since those attacks can also be used to regenerate spreading sequences, in this paper, in order to mitigate this problem, we consider induced random (chip) flipping of spreading sequences. While perturbed spreading sequences make correlation attacks infeasible, a legitimate receiver also suffers from random flipping. To take advantage of known spreading sequences for good performances, the maximum likelihood (ML) detection can be used, which is, however, computationally prohibitive as the complexity grows exponentially with the processing gain. To avoid this difficulty, we derive an expectation-maximization (EM) algorithm to perform the ML detection with low computational complexity in this paper.

The use of array readers (i.e., multiple read elements positioned to read stored data on multiple adjacent tracks) is one of the recent concepts to improve the areal density of magnetic recording systems. Array readers provide diversity gain to deal with noise and enable the handling of inter-track interference (ITI) as well as multi-track detection. In this paper, an array-reader two-track (AR2T) detection system is studied. We present the design of 2-D equalizer that jointly processes array readback signal streams over two tracks. Such a 2-D equalizer can be designed to approximate a 1-D or a 2-D partial-response (PR) target. It is shown that 2-D PR target can result in less residual ITI. For the detection of signals corresponding to 2-D PR target, we propose a novel symbol-based detection algorithm that processes two input signal streams jointly. We also implement a pattern-dependent noise-predictive (PDNP) version of proposed detector. Simulation results show that more than 4 dB performance gain can be achieved by the AR2T detection system compared to the traditional single-reader single-track detection system under aggressive track density conditions.

In this paper, a novel iterative encoding scheme is proposed for memory systems suffering from stuck-at errors. The stuck-at errors can be efficiently managed by using side information about stuck-at memory cells during encoding, while encoding for unconstrained number of stuck-at errors is intractable due to its exponential complexity. The proposed coding scheme employs an iterative encoding algorithm using cross-entropy method, which has a polynomial time complexity. In addition, any linear block code (LBC) can be concatenated with the proposed code, to correct for both residual stuck-at errors and random (soft) errors. The proposed coding schemes are evaluated by numerical simulations using a memory channel undergoing both stuck-at and random errors. Simulation results show that the cross-entropy based coding scheme provides an improved block error rate (BLER) performance, or alternatively, a higher overall storage capacity.

In space, radiation particles can introduce temporary or permanent errors in memory systems. To protect against potential memory faults, either thick shielding or error correcting codes (ECC) are used. Thick shielding translates into increased mass and conventional ECCs designed for memories are typically capable of only correcting a single error and detecting a double error. Decoding is usually performed through hard-decisions where bits are treated as either correct or flipped in polarity. In this work, we demonstrate that low-density parity-check (LDPC) codes that are already prevalent in many communication applications can also be used to protect memories in space. We develop a channel that models memory error events in a space radiation environment. We describe how to compute soft symbol reliabilities on our channel and compare the performance of soft-decision decoding LDPC codes against conventional hard-decision decoding of Reed-Solomon (RS) codes and Bose-Chaudhuri-Hoquenghem (BCH) codes for a specific memory structure.

Two-dimensional magnetic recording (TDMR) is a data storage paradigm that has recently been proposed to record more than one channel bit to one magnetic grain in order to achieve 10 Tbit/in² of user bit density on a magnetic recording medium with 20 Tgrains/in². In addition to conventional noise in magnetic recording, the TDMR channel also suffers from write errors due to the randomness in size and placement of the grains; however aggressive two-dimensional (2D) signal processing schemes may enable reliable data retrieval. In this study, the symmetric information rate (SIR) of a TDMR channel model based on random Voronoi grains, named the ideal readback TDMR channel model, is analyzed. Using numerical simulations, a lower bound of the SIR is evaluated for a half grain per channel bit ideal readback TDMR channel. Although approximately half of the channel bits may not be recorded in this channel, the resulting lower bound of the SIR is 0.46 ~ 0.51 information bits per grain. This result indicates the potential of TDMR to achieve 10 Tbit/in² of user bit density on a magnetic medium with 20 Tgrains/in².

In synchronous timing recovery approaches often employed in magnetic recording readback channels, the timing error detector (TED) output is assumed to be linearly related to the underlying phase offset. However, the noisy readback signal samples in high-density magnetic recording systems are nonlinearly related to the underlying timing disturbances. Thus, a nonlinear state space models may describe such systems more accurately and an improved timing recovery solution may be obtained via an extended Kalman filter (EKF) approach. In this paper, EKF based timing recovery is investigated for the acquisition stage. Numerical simulations indicate that the EKF based timing recovery scheme yields smaller root mean square (RMS) error in timing estimates, which enables it to handle larger timing errors more reliably. In addition, the proposed method converges faster than the conventional timing recovery schemes, which may allow us to reduce the length of the preamble pattern used for the acquisition.