Search Results (157)

Mobile Radio Frequency Identification (RFID) systems are emerging as a fundamental part of modern smart environments, enabling automatic identification, tracking, and data exchange among different mobile platforms. While these systems are increasingly being adopted, they have a major drawback: an RFID tag has very little computational power, and the wireless communication channels can be attacked by adversaries. Several authentication and key management mechanisms to protect data and provide secure access have been proposed to solve these problems. In this study, we propose a new scheme that improves system security through explicit three-party mutual authentication, epoch-based pseudonym indexing for $O\left(1\right)$ server lookup, and comprehensive resiliency against replay, impersonation, and man-in-the-middle attacks. An in-depth security analysis, along with performance evaluation, substantiates that the proposed protocol improves privacy and resilience without losing compatibility with low-cost RFID tags equipped only to perform lightweight cryptographic functions. This protocol also provides epoch-based unlinkability and is well suited for large-scale deployments, as found in healthcare, logistics, and Internet of Things (IoT) applications. Full article

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation. Full article

Accurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods. This paper proposes CVIWM (Coupled Visual-Inertial-Wheel Odometry with Markers), a tightly coupled state estimation method that fuses visual, inertial measurement unit (IMU), wheel odometry (WO), and fiducial marker observations within a factor graph optimization framework. Wheel odometry preintegration suppresses IMU horizontal drift and provides absolute scale, while sparse AprilTag markers (10 m spacing) periodically reset accumulated errors. Experiments in an 80 m corridor of a commercial caged chicken house at 0.116 m/s and 0.232 m/s showed that CVIWM achieves average positioning errors of 2.402 cm and 3.253 cm. This high precision ensured reliable image acquisition (image shift $<83$ pixels), enabling 95.7% dead hen detection and 98.9% egg detection accuracy. CVIWM offers a low-cost, easy-to-deploy, high-accuracy solution for automated poultry house inspection, supporting smart livestock farming. Full article

The purpose of this research is to determine which factors contribute to extending the read range of transponders equipped with different coupling-circuit topologies operating within selected RFID frequency bands. The analysis covered transponders that varied in both the configuration of their coupling circuits and their geometric dimensions. To accomplish this, transponder models were created using the EMCoS Studio electromagnetic simulation environment. Each model was subjected to simulations that yielded the mutual inductance and the voltage induced at the chip terminals. This study examines how the impedance of the embroidered antenna, the impedance of the chip’s coupling circuit, and the magnetic flux density affect the resulting chip voltage. In several of the investigated configurations, the peak chip voltage appeared outside the frequency range normally associated with RFID systems. The frequency at which this maximum occurred was dependent on the mutual inductance value. Understanding how individual parameters influence mutual inductance makes it possible to shift the voltage peak into a target operating band. Numerical simulation results, combined with the transponder’s mathematical model, enabled the calculation of the mutual inductance and the terminal voltage—quantities that directly determine the achievable read range. This study focuses on factors such as the resonant frequencies of the antenna and coupling circuit, their impedances, and the characteristics of the magnetic field. The findings show that tuning these parameters can affect not only the location of the voltage maximum, but also its amplitude. This effect introduces additional complexity in designing and selecting suitable transponder configurations. Full article

With the rapid development of smart devices for body area networks and smart packaging, there is a significant demand for low-waste and low-impact electronic systems in industries such as healthcare and transportation. We demonstrate that the dielectric material from capacitors in resistor-inductor-capacitor (RLC) wireless, chipless, resonant temperature sensors can be successfully recovered from flexible PCBs, with pristine sensors re-introduced to the tag’s sensor loading. First, we demonstrate that replacing the dielectric in a parallel plate capacitor with a pristine component, with recycled electrodes and sub-miniature-A (SMA) adaptor, results in only a 3% change in broadband capacitance. An identical substitution of the sensing element in an RLC circuit tuned to resonate at 21.0 MHz, with recycled parallel plates, a resistor, and an inductive PCB coil, results in a change of only 7.6% in the resonant frequency of the tag to 19.4 MHz. This work demonstrates the recyclability of chipless tags for temperature sensing for the first time, offering sustainability gains in smart packaging applications, with the potential to be expanded to other sensing tags for pH, humidity, and chemical analytes, towards chipless product passports. Full article

Embedded devices in modern power systems offer increased connectivity and remote reprogrammability/reconfigurability. These features along with interconnections between Information Technology (IT) and Operational Technology (OT) networks enable greater agility, reduced operator workload, and enhanced power system performance and capabilities, as well as expanding the cyber-attack surface. This increased cyber-attack surface, as well as increasingly complex, diverse, and potentially untrustworthy software/hardware supply chains, increases the need for robust real-time monitoring in power systems, and more generally in cyber–physical systems (CPS). We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed framework enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into a time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate the efficacy of our methodology on a hardware in the loop (HITL) testbed under several attack scenarios. The HITL testbed includes multiple physical power system devices (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units—PMUs, relays, Phasor Data Concentrators—PDCs), all interfaced to a dynamic power system simulator. Full article

Addressing the pressing need to extend the communication range of RF RFID tag antennas, this paper introduces a novel UHF RFID tag antenna technology based on resonant superstrate regulation using a Split-Ring Resonator (SRR). First, a finite element model of the UHF RFID folded dipole antenna was constructed based on the tag chip’s port impedance. Subsequently, a Two-element SRR resonant superstrate was employed to enhance the dipole antenna’s gain through “resonance and near-field coupling” technology. A folded dipole antenna gain-enhancing SRR resonant superstrate unit was designed, and a multi-parameter joint optimization method was adopted to obtain the optimal SRR resonant superstrate configuration for regulating the dipole antenna. Near-field coupling technology was used to design SRR resonant superstrate elements that enhance the folded dipole antenna’s gain. A multi-parameter joint optimization method was employed to obtain the optimal structural parameter set for the SRR resonant superstrate-controlled dipole antenna. Finally, simulations and experimental measurements of the RFID antenna performance revealed that: within the 920–925 MHz band, the maximum measured forward reading distance enhancement reached 62.1%. The research findings significantly enhance the practical performance of UHF RFID tags in complex environments, enabling more stable and efficient long-range identification in applications such as logistics tracking, asset management, and smart warehousing. Full article

This work presents a curated dataset of multimodal sensor measurements from Internet of Things (IoT) units mounted on waste collection vehicles. Each unit records multiple data streams including GPS position, vehicle velocity, radar-based container presence, accelerometer readings of the lifting arm, and RFID tag identifiers of the bins. The dataset provides two complementary forms of annotation: (1) algorithmically generated events that were manually cleaned through visual inspection of sensor signals, offering large-scale coverage across 5 vehicles over a total of 25 collection days, and (2) manually validated events derived from synchronized video recordings, representing ground truth for 3 vehicles over 8 collection days. In total, the dataset contains 12,391 annotated waste collection events. The dataset spans diverse operational conditions with varying container sizes and includes both RFID-equipped and non-RFID bins. It can be used to train and evaluate machine learning models for event detection, anomaly recognition, or explainability studies, and to support practical applications such as Pay-as-you-throw (PAYT) waste management schemes. By combining multimodal sensor signals with reliable annotations, the dataset represents a unique resource for advancing research in smart waste collection and the broader field of IoT-enabled urban services. Full article

In this study, an improved particle swarm optimization (PSO) algorithm is designed to construct a weighting model for line-of-sight (LOS) and non-line-of-sight (NLOS) channels in an ultra-wideband (UWB) indoor positioning system. In the proposed algorithm, the particle position represents candidate weight vectors, and the fitness function is defined by the 3D positioning error over multiple test points. An optimized weight modeling framework is proposed for a multi-anchor, three-dimensional UWB indoor positioning system under LOS and NLOS channels. First, the three-dimensional positioning problem is formulated as a multilateration model, and the tag coordinates are estimated via a linearized matrix equation solved by the least-squares method, which explicitly links anchor geometry and ranging errors to the positioning accuracy. To evaluate the proposed method, extensive ranging and positioning experiments are conducted in a realistic indoor environment using up to eight anchors with different LOS/NLOS configurations, including dynamic scenarios with varying numbers of NLOS anchors. The results show that, compared with the conventional unweighted multi-anchor scheme, the PSO-based weighting model can reduce the average 3D positioning error by more than 30% in typical LOS-dominant settings and significantly suppress error bursts in severe NLOS conditions. These findings demonstrate that the combination of mathematical modeling, least-squares estimation, and swarm intelligence optimization provides an effective tool for designing intelligent engineering positioning systems in complex indoor environments, which aligns with the development of smart factories and industrial Internet-of-Things (IIoT) applications. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

Smart polyester materials with embedded near-infrared (NIR) functionalities offer a promising pathway for low-cost, covert tagging, and object identification. In this study we present the development and characterization of polyester packaging surfaces printed with spectrally matched twin dyes that are invisible under visible light but selectively absorbed in the NIR region. The dye patterns were applied using a Direct-to-Film transfer (DTF) method onto polyester substrates. To validate their optical behavior, we applied a dual measurement approach. Laboratory grade NIR absorbance spectroscopy was used to characterize the spectral profiles of the twin dyes in the 400–900 nm range. A custom photodiode-based detection system was constructed to evaluate the feasibility of low-cost, embedded NIR absorbance sensing. Results from both methods show correlation in absorbance contrast between the dye pairs, confirming their suitability for spectral tagging. The developed materials were evaluated in a real-world detection scenario using commercially available NIR cameras. Under dark field conditions with edge illuminated planar lighting, the twin dye patterns were successfully recognized through custom software, enabling non-contact identification and spatial localization of the NIR codes. This work presents a low-cost, scalable approach for smart packaging applications based on optical detection of actively illuminated twin dyes using accessible NIR imaging systems. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service. Full article