Search Results (5,383)

Accurate branch prediction is crucial for achieving high instruction throughput and minimizing control hazards in modern pipelines. This paper presents a novel LXOR (Local eXclusive-OR) branch predictor, which enhances prediction accuracy while reducing hardware complexity and memory usage. Unlike traditional predictors (GAg, GAp, PAg, PAp, Gshare, Gselect) that rely on large Pattern History Tables (PHTs) or intricate global/local history combinations, the LXOR predictor employs complemented local history and XOR-based indexing, optimizing table access and reducing aliasing. Implemented and evaluated using the MARSS-RISCV simulator on a 64-bit in-order RISC-V core, the LXOR’s performance was compared against traditional predictors using Coremark and SPEC CPU2017 benchmarks. The LXOR consistently achieved competitive results, with a prediction accuracy of up to 83.92%, lower misprediction rates, and instruction flushes as low as 5.83%. It also attained an IPC rate of up to 0.83, all while maintaining a compact memory footprint of approximately 2 KB, significantly smaller than current alternatives. These findings demonstrate that the LXOR predictor not only matches the performance of more complex predictors but does so with less memory and logic overhead, making it ideal for embedded systems, low-power RISC-V processors, and resource-constrained IoT and edge devices. By balancing prediction accuracy with simplicity, the LXOR offers a scalable and cost-effective solution for next-generation microprocessors. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

The Internet of Medical Things (IoMT) comprises the application of traditional Internet of Things (IoT) technologies in the healthcare domain. IoMT ensures seamless data-sharing among hospitals, patients, and healthcare service providers, thereby transforming the medical environment. The adoption of IoMT technology has made it possible to provide various medical services such as chronic disease care, emergency response, and preventive treatment. However, the sensitivity of medical data and the resource limitations of IoMT devices present persistent challenges in designing authentication protocols. Our study reviews the overall architecture of the IoMT and recent studies on IoMT protocols in terms of security requirements and computational costs. In addition, this study evaluates security using formal verification tools with Scyther and SVO Logic. The security requirements include authentication, mutual authentication, confidentiality, integrity, untraceability, privacy preservation, anonymity, multi-factor authentication, session key security, forward and backward secrecy, and lightweight operation. The analysis shows that protocols satisfying a multiple security requirements tend to have higher computational costs, whereas protocols with lower computational costs often provide weaker security. This demonstrates the trade-off relationship between robust security and lightweight operation. These indicators assist in selecting protocols by balancing the allocated resources and required security for each scenario. Based on the comparative analysis and a security evaluation of the IoMT, this paper provides security guidelines for future research. Moreover, it summarizes the minimum security requirements and offers insights that practitioners can utilize in real-world settings. Full article

The widespread integration of low-power electronic devices in IoT, biomedical, and sensing applications has intensified the demand for energy-autonomous solutions. Radio Frequency Energy Harvesting (RFEH) offers a promising alternative by leveraging ambient RF signals available in both indoor and outdoor environments. Despite its conceptual appeal, practical deployment still faces major challenges. This systematic literature review (SLR) examines 25 recent studies, following the PRISMA methodology, to provide a comprehensive overview of current RFEH architectures. It focuses on three critical components: receiving antennas, impedance matching circuits (IMCs), and RF-to-DC rectifiers. Design strategies are reviewed and compared across antenna types, matching techniques, and rectifier configurations. The review also highlights persistent challenges and outlines directions for the development of compact, efficient, and robust energy-harvesting systems for next-generation wireless technologies. Full article

Federated learning (FL) enables distributed model training across sensor-equipped edge devices while preserving data privacy. However, its performance is often hindered by statistical heterogeneity among clients and system heterogeneity in dynamic wireless networks. To address these challenges, we propose TCS-FEEL, a topology-aware client selection framework that jointly considers user distribution, device-to-device (D2D) communication, and statistical similarity of client data. The proposed approach integrates randomized client sampling with an adaptive tree-based communication structure, where user devices not only participate in local model training but also serve as relays to exploit efficient D2D transmission. TCS-FEEL is particularly suited for sensor-driven edge intelligence scenarios such as autonomous driving, smart city monitoring, and the Industrial IoT, where real-time performance and efficient resource utilization are crucial. Extensive experiments on MNIST and CIFAR-10 under various non-IID data distributions and mobility settings demonstrated that TCS-FEEL consistently reduced the number of training rounds and shortened per-round wall-clock time compared with existing baselines while maintaining model accuracy. These results highlight that integrating topology control with client selection provides an effective solution for accelerating privacy-preserving and resource-efficient FL in dynamic, sensor-rich edge environments. Full article

Background/Objectives: Digital twins are emerging as a transformative technology in diabetes management, promising a shift from standardized protocols to highly personalized care. This scoping review aims to systematically map the current landscape of digital twin applications in diabetes, synthesizing evidence on their implementation architectures, analytical models, performance metrics, and clinical integration strategies to identify key trends and critical gaps. Methods: A systematic search was conducted across five electronic databases in accordance with PRISMA-ScR guidelines to identify empirical studies on digital twins for diabetes. Data from the selected articles were extracted to analyze bibliographic characteristics, population data, technological components, performance outcomes, and integration levels. A narrative synthesis was performed to map the evidence. Results: Seventeen studies were included, revealing a rapid increase in publications since 2022, with a notable concentration of research in India. The technological architecture shows a convergence toward machine learning models (e.g., LSTM) powered by data from IoT devices and wearables. Certain interventional studies have reported significant clinical impacts, including HbA1c reductions of up to 1.9% and T2DM remission rates as high as 76.5% in one trial. However, major implementation barriers were identified, including fragmented interoperability standards and low rates of full integration into clinical workflows (35.3%). Conclusions: Digital twins are emerging as powerful tools that show the potential to drive significant clinical outcomes in diabetes care. However, to translate this promise into widespread practice, future efforts must focus on overcoming the critical challenges of standardized interoperability and deep clinical integration. Rigorous, independently validated, long-term trials in diverse populations are essential to confirm these promising findings. Full article

This paper presents the development of a modular and low-cost IoT (Internet of Things) system for remote monitoring of essential parameters during wine fermentation, designed for small and medium-sized wineries—Winnie. The system combines distributed embedded sensing units with centralized colorimetric analysis and real-time data transmission to a remote server. Barrel-mounted devices measure wine and cellar parameters (temperature, humidity, and CO₂ concentration), while a central hub performs colorimetric SO₂ analysis using an RGB color sensor and automated fluid handling. Communication between the Barrel and Hub device relies on the RS-485 protocol, providing robustness in harsh winery conditions. All measurements are securely transferred via Wi-Fi. A hash-based integrity check ensures continuous and reliable data collection. The modular design, simple installation, and user-friendly web interface make the system accessible to winemakers. This technology provides a scalable method for digitalizing conventional winemaking processes by reducing the cost and complexity of wine quality monitoring. Full article

When energy harvesting is not feasible or fails to provide sufficient power, the energy buffer of battery-powered Internet of Things (IoT) devices inevitably depletes. The proper disposal and/or replacement of depleted and end-of-life (EoL) batteries is challenging, especially in rural IoT deployments, where human intervention is cumbersome. When batteries are left in nature, they can pose a significant environmental risk, leaking harmful chemicals into the soil. This work proposes a novel contactless battery solution for longevity and recyclability, providing automated battery replacement using a short-range wireless power transfer (WPT) link instead of a direct battery-to-IoT node contact-based connection for powering the IoT device. It facilitates battery recovery at EoL by, e.g., an unmanned vehicle (UV), reducing the need for manual intervention. Unlike complex mechanical solutions or contacts prone to corrosion, a contactless approach enables easy replacement and improves reliability and longevity in harsh environments. A technical challenge is the need for an efficient contactless solution to enable the IoT node to get energy from the battery. This work elaborates an efficient wireless connection between the battery and IoT node, which ensures robustness in harsh environments. In addition, it examines the sustainability aspects of this approach. The WPT system is applied in two IoT node applications: polling-based and interrupt-based systems. The proposed solution achieves a transmitter-to-receiver efficiency of 72% and has an additional environmental impact of 2.34 kgCO2eq. However, its key advantage is the ease of battery replacement, which could significantly reduce the expected long-term environmental impact. Full article

This work presents the design and prototyping of two reconfigurable BLE-based devices developed to overcome the limitations of commercial platforms in terms of configurability, data transparency, and energy efficiency. The first is a wearable smart wristband integrating inertial and biometric sensors, while the second is a configurable beacon (ASIA Beacon) able to dynamically adjust key transmission parameters such as channel selection and power level. Both devices were engineered with energy-aware components, OTA update support, and flexible 3D-printed enclosures optimized for residential environments. The firmware, developed under Zephyr RTOS, exposes data through standardized interfaces (GATT, MQTT), facilitating their integration into IoT architectures and research-oriented testbeds. Initial experiments carried out in an anechoic chamber demonstrated improved RSSI stability, extended autonomy (up to 4 months for beacons and 3 weeks for the wristband), and reliable real-time data exchange. These results highlight the feasibility and potential of the proposed devices for future deployment in ambient assisted living environments, while the focus of this work remains on the hardware and software development process and its validation. Full article

Nanogenerators (NGs), especially triboelectric nanogenerators (TENGs), represent an emerging technology with great potential for self-powered lumbar motion monitoring. Conventional wearable systems for assessing spinal kinematics are often limited by their reliance on external power supplies, hindering long-term and real-time clinical applications. NGs can convert biomechanical energy from lumbar motion into electrical energy, providing both sensing and power-generation capabilities in a single platform. This review summarizes the fundamental working mechanisms, device architectures, and current progress of NG-based motion monitoring technologies, with a particular focus on their applications in lumbar spine research and clinical rehabilitation. By enabling high-sensitivity, continuous, and battery-free monitoring, NG-based systems may enhance the diagnosis and management of low back pain (LBP) and postoperative recovery assessment. Furthermore, the integration of NGs with wearable electronics, the Internet of Things (IoT), and artificial intelligence (AI) holds promise for developing intelligent, self-sustaining monitoring platforms that bridge biomedical engineering and spine medicine. Full article

The End–Edge–Cloud (EEC) paradigm hierarchically orchestrates Internet of Things (IoT) devices, edge nodes, and cloud, optimizing system performance for both delay-sensitive data and compute-intensive processing tasks. Securing IoT data sharing in the EEC-driven paradigm while maintaining data traceability poses critical challenges. In this paper we propose STDSM, a symmetry-enhanced secure and traceable data sharing model for the EEC-driven data sharing paradigm. STDSM enables IoT data owners to share data securely by attaching symmetric security labels (for secrecy and integrity) to their data. This mechanism symmetrically controls both data outflow and inflow. Furthermore, STDSM can also track data user identity. Subsequently, the security properties of STDSM, including data confidentiality, integrity, and identity traceability, are formally verified; the verification takes 280 ms, using a novel approach that combines High-Level Petri Net modeling with the satisfiability modulo theories library and the Z3 solver. In addition, our experimental results show that STDSM reduces time overhead by up to 15% while providing enhanced traceability. Full article

Botnet attacks on Internet of Things (IoT) devices are escalating at the 5G/6G multi-access edge, yet most federated learning frameworks for IoT malware detection (FL-IMD) still hinge on a central aggregator, enlarging the attack surface, weakening privacy, and creating a single point of failure. We propose a two-tier, fully decentralized FL architecture aligned with MEC’s Proximal Edge Server (PES)/Supplementary Edge Server (SES) hierarchy. PES nodes train locally and encrypt updates with the Cheon–Kim–Kim–Song (CKKS) scheme; SES nodes verify ECDSA-signed provenance, homomorphically aggregate ciphertexts, and finalize each round via an Algorand-style committee that writes a compact, tamper-evident record (update digests/URIs and a global-model hash) to an append-only ledger. Using the N-BaIoT benchmark with an unsupervised autoencoder, we evaluate known-device and leave-one-device-out regimes against a classical centralized baseline and a cryptographically hardened but server-centric variant. With the heavier CKKS profile, attack sensitivity is preserved (TPR $\ge 0.99$), and specificity (TNR) declines by only 0.20 percentage points relative to plaintext in both regimes; a lighter profile maintains TPR while trading 3.5–4.8 percentage points of TNR for about 71% smaller payloads. Decentralization adds only a negligible per-round overhead for committee finality, while homomorphic aggregation dominates latency. Overall, our FL-IMD design removes the trusted aggregator and provides verifiable, ledger-backed provenance suitable for trustless MEC deployments. Full article

The rapid proliferation of the Internet of Things (IoT) has created an urgent need for on-device intelligence that balances high computational demands with stringent energy constraints. Existing edge inference frameworks struggle to deploy generative artificial intelligence (AI) models efficiently on low-power devices, often sacrificing fidelity for efficiency or lacking adaptability to dynamic conditions. To address this gap, we propose a generative AI edge inference framework integrating lightweight architecture compression, adaptive quantization, and energy-aware scheduling. Extensive experiments on CIFAR-10, Tiny-ImageNet, and IoT-SensorStream show that our method reduces energy consumption by up to 31% and inference latency by 27% compared with state-of-the-art baselines, while consistently improving generative quality. Robustness tests further confirm resilience under noise, cross-task, and cross-dataset conditions, and ablation studies validate the necessity of each module. Finally, deployment in a hospital IoT laboratory demonstrates real-world feasibility. These results highlight both the theoretical contribution of unifying compression, quantization, and scheduling, and the practical potential for sustainable, scalable, and reliable deployment of generative AI in diverse IoT ecosystems. Full article

The rapid and ongoing adoption of smart home products, coupled with the increasing integration of artificial intelligence (AI), particularly in these products, is an undeniable reality. However, as both technologies converge, they also give rise to a range of significant concerns. The EU’s recent AI Act specifically addresses the challenges associated with the use of AI technology. In this study, we examine three AI-integrated products with toy capabilities that are sold in Spain, serving as a case study for the EU market of smart home devices that incorporate AI. Our research aims to identify potential compliance issues with both the AI Act and the General Data Protection Regulation (GDPR). Our results reveal a clear and worrying gap between the existing legislation and the functionalities of these devices. Using a normal user’s approach, we find that the privacy policies for these products, whose features make them high-risk AI systems, AI systems with systemic risk, or both as per the AI Act, fail to provide any information about AI usage, particularly of ChatGPT, which they all integrate. This raises significant concerns, especially as the market for such products will continue to grow. Without rigorous enforcement of existing legislation, the risk of misuse of sensitive personal information becomes even greater, making strict regulatory oversight essential to ensure user protection. Full article

Energy efficiency in shared environments, such as offices and laboratories, is hindered by a lack of individual accountability. Traditional smart metering provides aggregated data but fails to attribute consumption to specific users, limiting the effectiveness of behavioral change initiatives. This paper introduces the “authenticated energy consumption” paradigm, an innovative approach that directly links energy use to an identified user. We present PlugID, a low-cost, open-protocol IoT platform designed and built to implement this paradigm. The PlugID platform comprises a custom smart plug with RFID-based authentication and a secure, cloud-based data analytics backend. The device utilizes an ESP8266 microcontroller, Tasmota firmware, and the MQTT protocol over TLS for secure communication. Seven PlugID units were deployed in a small office environment to demonstrate the system’s feasibility. The main contribution of this work is the design, implementation, and validation of a complete, end-to-end system for authenticated energy monitoring. We argue that by making energy consumption an auditable and attributable event, the PlugID platform provides a powerful new tool to enforce energy policies, foster user awareness, and promote genuine efficiency. Full article