Search Results (813)

Wi-Fi security analysis and testing tools are vital to ensure the safety of wireless networks. Specialised hardware and software are needed to examine the underlying technology that connects our devices wirelessly. This article explores the feasibility of utilising the ESP32-S3 microcontroller as the basis for a low-cost, open-source, portable Wi-Fi penetration testing tool. By developing and evaluating the Deauther32 firmware, the project demonstrates key functionalities such as capturing and injecting frames to execute common Wi-Fi attacks, like beacon flooding and deauthentication. The developed HackHeld32 design complements the firmware by offering a compact and extendable handheld device, making the tool standalone and portable. These prototypes build upon previous work, the ESP8266 Deauther and the HackHeld Vega, by introducing significant improvements in functionality, usability, and hardware capabilities. This establishes a strong foundation for future development by demonstrating the potential of microcontroller-based solutions. These tools bridge the gap between accessibility for beginners and functionality for professionals by offering a cost-effective and portable solution for Wi-Fi security testing and beyond. Full article

WiFi sensing has gained widespread attention as a promising technology, owing to its non-intrusiveness, strong privacy-preserving characteristics, and cost-effective deployment, enabling diverse application scenarios. In addition, the stable spatial characteristics and symmetry-related patterns exhibited by human body postures in WiFi signal propagation provide new possibilities for robust person identification. In traditional WiFi-based person identification technologies, although gait recognition has achieved certain success, it is complex to operate and limited in application scenarios, increasing the constraints on recognition. This issue becomes more pronounced in large-scale user scenarios, where the system performance tends to degrade and exhibit instability. To overcome these challenges, we introduce a new person identification system called WiPID. The WiFi signals extracted from the static postures of users are treated as a “biometric fingerprint” for identity verification. An end-to-end deep learning framework is utilized by WiPID to process WiFi signals, and a convolutional autoencoder is adopted to preprocess the signals directly, effectively reducing redundant information and greatly simplifying the WiFi data processing. Furthermore, the integration of a multi-scale feature extraction module improves the system’s ability to capture discriminative features. The proposed system not only reduces operational complexity but also extends its applicability to a wider range of scenarios, thereby enhancing recognition performance. In an experiment involving 50 volunteers, WiPID achieved an average recognition accuracy of up to 98%, demonstrating the method’s suitability for large-scale person identification scenarios. In addition, a real-time identification experiment has been conducted on PCs and commercial WiFi devices. Experiments have proven that WiPID can achieve real-time person identification on Internet of Things devices, further validating its feasibility and stability in practical applications. Full article

This paper proposes a compact axial-ratio-enhanced wideband circularly polarized rectenna for ambient RF energy harvesting. The proposed rectenna is designed to operate across the mainstream Wi-Fi (2.45 GHz) and 5G (2.6 GHz and 3.5 GHz) communication bands, achieving efficient RF energy capture and effective direct current (DC) conversion. From a design perspective, the proposed approach is developed based on parasitic-element-enabled current redistribution for broadband circular polarization and nonlinear-aware multi-stage impedance matching for wideband rectification. The receiving antenna is based on a crossed-dipole configuration integrated with quarter-ring elements. By employing techniques such as slotting and incorporating additional parasitic patches, a fractional 3-dB axial ratio bandwidth (ARBW) of 52.7% (2.39–4.10 GHz) is achieved, with a peak radiation efficiency of 90% and an average efficiency of 76% within the operating band. To realize wideband impedance matching with the receiving antenna, the rectifying circuit adopts a single-shunt diode half-wave topology, combining L-type and T-type matching networks to significantly extend the operating bandwidth. Experimental results demonstrate that at input power levels of 7 dBm, 7 dBm, and 9 dBm, the rectifier achieves peak conversion efficiencies of 56.7%, 59.8%, and 56.3% at the three target frequencies (2.45 GHz, 2.6 GHz, and 3.5 GHz), respectively. Furthermore, the rectifier exhibits stable rectification performance across a wide input power dynamic range from −15 dBm to 7 dBm. Consequently, the proposed rectenna holds significant application value for passive IoT nodes, low-power sensors, and self-sustainable electronic devices. Full article

Aiming to address the challenge of the high-precision monitoring of underground coal and rock fractures, this paper proposes and verifies a roadway full-section synchronous monitoring method utilizing a Wi-Fi wireless sensor network. To address the inherent difficulties of detecting complex rock mass fractures through surface sensors, our methodology employs a synchronized array of surface-mounted vibration sensors covering key mechanical structural points. The feasibility of this approach is technically substantiated through the strict implementation of rigid coupling techniques—utilizing industrial-grade epoxy resin and customized metal mechanical fixtures—combined with hardware low-pass filtering to eliminate air gap attenuation and maximize the signal-to-noise ratio. Using this validated setup, we successfully extracted and manually verified 63 high-fidelity rupture events. The data reliability is further demonstrated through a comprehensive Python-based processing pipeline that calculates 17-dimensional time–frequency characteristics. Statistical analysis confirms that the extracted data strictly conforms to the physical laws of rock fracture, evidenced by a significant negative correlation between maximum amplitude and dominant frequency (r = −0.84, p < 0.001). Unsupervised clustering of these signals reveals excellent inter-class separability. By transparently substantiating the data acquisition and verification process, this study provides a publicly shared pilot dataset and methodology for algorithm evaluation and preliminary dynamic disaster mechanism exploration. Full article

This work proposes a polarization-insensitive scalable wide-angle metasurface array for highly efficient ambient energy harvesting in the 5.8 GHz Wi-Fi band. Inspired by dipole antenna principles, we design an asymmetric planar orthogonal dipole-based metasurface featuring monolithic integration of Schottky diodes (HSMS-2860) at unit cell feed gaps. This novel direct-impedance-matching strategy eliminates conventional matching networks, reducing energy conversion losses while enabling 99% radiation-to-AC efficiency across all polarization angles at 5.8 GHz. The coplanar architecture interconnects metasurface unit cells via inductors, simultaneously establishing low-loss DC channels and suppressing RF leakage. Fabricated as a 5 × 5 array, the prototype achieves 77.9% peak RF-to-DC efficiency with polarization insensitivity at an incident power of 25 dBm. Furthermore, with incident powers of 15 dBm and 20 dBm, the proposed metasurface array attained RF-to-DC conversion efficiencies exceeding 40% and 60%, respectively. This result indicates that the array is capable of achieving high energy harvesting efficiency across a broad power range. This scalable, drill-free, and polarization-insensitive design demonstrates strong potential for harvesting ambient RF energy in real-world multipath environments. Full article

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), and SHT35 (relative humidity sensor), an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi. The neural controller, implemented using the Arduino Neurona library, regulates the dryer temperature in real time, enabling drying kinetics analysis under ANN-based thermal control to investigate its capability to maintain thermal stability. Three initial loads (2, 4, and 6 kg) were analyzed to determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1390 kJ with a thermal efficiency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes. Full article

Passive Wi-Fi localization for critical-infrastructure security operations centers (SOCs) faces three interconnected limitations. First, many existing methods produce single-point coordinate estimates without calibrated uncertainty, making them unsuitable for automated SOC response. Second, localization pipelines often lack an evidentiary chain of custody, limiting reliable post-incident auditability. Third, SOC automation cannot safely rely on uncalibrated confidence values because erroneous high-impact actions and missed escalations carry asymmetric operational costs. This study presents a Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization framework for heterogeneous sensor networks composed of stationary sensors, mobile receivers, and UAV-assisted collection nodes. Instead of producing a single coordinate estimate, the method derives a posterior spatial distribution with calibrated uncertainty from monitor-mode observations, including RSSI aggregates, management/control frame features, channel occupancy indicators, and receiver logs. The framework combines three tightly coupled components: (i) Bayesian coordinate estimation with robust loss functions and range-dependent error modeling; (ii) uncertainty calibration that converts posterior confidence into operational SOC response modes (AUTO, VERIFY, and OBSERVE) via empirical coverage metrics and reliability diagrams; and (iii) a permissioned evidentiary logging layer that anchors integrity-relevant metadata and policy labels on-chain while keeping raw telemetry off-chain for tamper-evident auditability and scalability. The coupling between layers is explicit: calibrated confidence scores govern smart-contract gating conditions, and smart-contract policy thresholds feed back into the calibration stage. Field validation shows that localization performance degrades markedly beyond approximately 40 m, indicating a practical boundary for confident automated action. The proposed framework integrates passive sensing, uncertainty-aware localization, and blockchain-based evidentiary trust for secure critical-infrastructure sensor networks. Its key contributions are: (1) a posterior-distribution-based passive localization pipeline; (2) empirical coverage metrics for calibrating SOC response thresholds; (3) a hybrid on-chain/off-chain architecture linking localization outputs to a permissioned ledger; and (4) field validation establishing the 40 m operational validity boundary. Full article

Next-generation heterogeneous wireless networks (HetNets) comprising LTE macro-cells, 5G New Radio (NR) small cells, and WiFi 6 access points aim to provide seamless connectivity under diverse mobility scenarios. However, vertical handover (VHO) remains a performance bottleneck because of the highly variable radio environments, dynamic user mobility, stringent quality of service (QoS) requirements, and the coexistence of multi-tier access technologies. Existing handover approaches based on deep learning and deep reinforcement learning (DRL) suffer from limitations: deep learning models lack decision-making capabilities, whereas DRL models, particularly deep Q-network (DQN)-based policies, face Q-value overestimation and unstable convergence. To overcome these limitations, this paper introduces a Hybrid deep double-Q networks (DDQN)–bidirectional long short-term memory (Bi-LSTM) Framework that integrates bi-directional mobility prediction and DRL-based adaptive decision-making. The Bi-LSTM module captures forward and backward temporal dependencies and predicts future Received Signal Strength (RSS) trajectories, mobility dynamics, and cell-edge transitions. The DDQN module stabilizes the action value estimation, mitigates overestimation bias, and enables context-aware handover decisions. A multi-tier simulation environment consisting of LTE, 5G NR, and WiFi 6 networks was developed using realistic path loss, shadowing, interference, and mobility models. Extensive evaluations demonstrated substantial improvements in mobility prediction accuracy, handover stability, radio link reliability, throughput efficiency, and latency reduction compared to conventional RSS-based and DQN-based schemes. The findings highlight the effectiveness of integrating predictive intelligence with reinforcement learning for reliable mobility management in 5G-Advanced and emerging 6G networks. Full article

Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was performed to extract the design equations, followed by simulation and optimization to enhance impedance matching and output-port isolation over a broad bandwidth. Finally, the proposed divider was fabricated using microstrip-line technology, and experimental measurements were conducted using the Agilent E5071C vector network analyzer. The simulation and measurement results showed efficient wideband operation over the 1–4 GHz frequency range. Specifically, the measured return loss at the input port was <−10 dB; the corresponding return loss at the output ports was <−15 dB. The measured insertion loss was −3.73 ± 0.42 dB. The isolation between the output ports was <−10 dB, reaching approximately −30 dB at 2.1 GHz and −25 dB at the center operating frequency (f₀ = 2.5 GHz). The amplitude and phase imbalances were 0 ± 0.2 dB and 0^o ± 0.8^o, respectively. Furthermore, the overall size of the proposed wideband Wilkinson power divider was 0.35λg × 0.21λg. Compared to previous designs, the divider proposed in this study exhibits an improved and more symmetric frequency response, as well as a substantially reduced size, making it suitable for several modern wireless technologies such as Wi-Fi, Bluetooth, GPS, DCS, WCDMA, and sub-6 GHz 5G communication systems. Full article

The purpose of this research is to introduce a scalable system of an improved security for devices connected to a wireless network using the Advanced Encryption Standard with a Post-Quantum Cryptography standard FIPS203, Module Lattice–Key Encapsulation Mechanism (ML-KEM). This implementation is to address concerns regarding compromised network security or bad actors sniffing packets through a data bus to collect unintended compromised data. The ML-KEM is used to create a shared secret that is used as the symmetric key that will enable the encryption and decryption method for the ciphertexts between the client and the host. This research provides a baseline implementation of added security against Quantum Computers by using an encapsulation method for key pairs, digital signatures for data integrity, and added difficulties for side-channel attacks from unauthorized users. Devices that are older than the WiFi6-compliant standard also have additional vulnerability of not having the WiFi Protected Access (WPA) third-generation security, which this work addresses. This paper proposes an added layer of encryption security that is sufficient to protect information within the network that has been compromised by an unauthorized user. Based on the findings, new features, utilities and improvements are recommended that can modernize the needs of the industry. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40 m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

One of the challenges in managing mobility in a heterogeneous network domain remains a significant challenge in Software-Defined Networking (SDN). While SDN has effectively facilitated intra-domain mobility, inter-domain mobility has been a major issue, leading to service interruptions, packet loss, and unstable communication sessions. This article presents a new concept in mobility management: a hybrid SDN-LISP network that facilitates inter-domain communication by integrating SDN with the Locator/Identifier Separation Protocol (LISP). The main idea is to introduce a new event-based orchestration model that uses OpenFlow Packet-In messages to provide instantaneous updates to Endpoint Identifiers-to-Routing Locators (EID-to-RLOC) mappings, unlike traditional LISP, which relies on timers for updates. The proposed framework has been implemented and evaluated on a Mininet-WiFi testbed under various mobility conditions. The results obtained from the experimental evaluation reveal that packet loss is reduced by 92.32% when using the proposed framework over the conventional SDN Mobility approach. Although there is a slight increase in jitter overhead due to LISP encapsulation of 0.628 ms, the framework does not compromise Transmission Control Protocol (TCP) session continuity. In addition, the control plane synchronization time is also minimized to 277.5 ms. This reveals that the proposed framework is a stable mobility solution that does not depend on any conventional IP mobility solutions and can be used in future network environments requiring seamless inter-domain connectivity. Full article

This study presents a leader–follower flight control architecture for a small-scale UAV swarm, demonstrated using a three-UAV system built on heterogeneous autopilots, GPS positioning, Raspberry Pi 3B+ units, and Wi-Fi communication. The follower UAVs autonomously maintain predefined formations while tracking the leader’s trajectory. During flight, each Raspberry Pi establishes inter-UAV communication via a Wi-Fi network using the UDP protocol, enabling real-time data exchange and attitude adjustments. An outer-loop proportional–integral control design implemented on the Raspberry Pi generates corrective commands to the corresponding autopilot to reduce the followers’ position errors. Under the tested conditions, the framework achieves formation tracking with horizontal and vertical errors of approximately 60 and 20 cm, respectively, providing initial experimental validation in a small-scale setting. In addition, a simulation environment based on pre-recorded UAV and environmental data with 3D visualization is developed to support behavior prediction, performance evaluation, and control tuning prior to real-world deployment, although its applicability beyond the tested scenarios remains to be established. Full article

WSN node energy forecasting contributes to improving network efficiency, extending network lifespan, and providing energy management strategies. In this study, a deep-learning-based wireless sensor network (WSN) node energy forecasting model based on Long Short-Term Memory (LSTM) and stacked-LSTM was developed across different wireless communication technologies in both static and dynamic WSN setups. The performance of the deep-learning-based models was compared with traditional forecasting techniques such as Exponential Smoothing and Prophet. The results showed the superiority of LSTM and stacked-LSTM in terms of root mean square error and mean absolute error, with consistently lower values compared with the traditional forecasting techniques. The results also show that the models perform best with Long Range technology. The deep learning-based model also demonstrates its ability to perform better in both static and dynamic WSN scenarios. These results demonstrate the potential of deep-learning-based models in WSN node energy management, which can result in an optimal energy efficiency and prolong the network lifetime. Future research is needed to explore hybrid approaches to further improve the prediction performance of deep learning-based models by combining their strengths with statistical or traditional forecasting techniques. Full article