iot device engineers

Abstract: WiFi backscatter has emerged as a promising technology for green Internet of Things (IoT) connectivity by enabling battery-free devices to communicate through widely available WiFi signals. Despite substantial progress in recent years, a considerable gap remains between research prototypes and practical deployment. This paper provides an overview of WiFi backscatter from the perspective of practical and green IoT systems. We first introduce the fundamentals and key enabling techniques, together with potential IoT applications. We then outline recent research trends toward higher throughput, concurrent communication, simplified deployment, commercial compatibility, and joint communication and sensing. Furthermore, we identify the key challenges that still hinder practical deployment, such as limited transmitter-to-tag operating range and packet loss in frequency-shifted backscatter. We believe that addressing these challenges will be critical to enabling WiFi backscatter to become a practical communication technology for future green IoT systems.

Abstract: Magnetoresistive Random Access Memory (MRAM) technologies such as Spin-Transfer Torque (STT-MRAM) and Spin-Orbit Torque assisted (SOT-STT-MRAM) offer nonvolatility and low leakage, making them attractive for IoT systems. However, conventional MRAM read circuits face two fundamental challenges: high dynamic energy consumption and vulnerability to side-channel attacks caused by data-dependent current variations in Magnetic Tunnel Junctions (MTJs). This paper presents a Secured Adiabatic Logic Sense Amplifier (SALSA) that addresses both challenges simultaneously through circuit-device co-design. S-ALSA combines structural current balancing via a 4T-2MTJ bit cell, which eliminates read current asymmetry at the storage level, with dynamic power equalization via adiabatic charge recovery in the sensing circuit. The proposed architecture supports both STT-MRAM and SOT-STT-MRAM. Case studies using 4x4 MRAM macros shows up to 80% energy savings over conventional Pre-Charge Sense Amplifiers (PCSA) across IoT frequencies. Correlation Power Analysis (CPA) attacks on PRESENT-80 encryption confirm complete suppression of key leakage when S-ALSA is combined with a balanced bit cell. This work establishes a unified framework where energy efficiency and hardware security are achieved simultaneously, enabling secure and low-power IoT memory design.

Abstract: This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedded hardware. Conventional approaches relying on accelerometer-based activity metrics are highly susceptible to motion and vibration artifacts and are limited by strict compute and energy budgets of wearable and IoT devices. To address these challenges, a multimodal pipeline is designed and implemented on an ESP32-S3 microcontroller. The system combines inertial sensing for head movement analysis and visual pose classification. A dual-core architecture with FreeRTOS enables parallel execution of real-time data acquisition and on-device inference. Sleep detection follows a two-stage strategy: low-movement detection over a temporal window, followed by visual validation of poses. Experimental results show accuracies of 96.5% for motion-based detection and 89% for pose classification, yielding robust binary sleep-wake classification. Field tests confirmed feasibility in representative mobile scenarios. The results demonstrate that privacy-preserving, local sleep detection is achievable on edge hardware through careful co-design, while highlighting limitations in sensing intrusiveness, dataset scale, and system integration.

Abstract: Memory-efficient feature representations are increasingly important in machine learning settings where storage, transmission cost, bandwidth, or privacy constraints limit access to raw data. Bloom Filter (BF) encodings provide compact probabilistic representations of engineered features, but their behavior under structural compression and their applicability to regression tasks remain underexplored. In this work, we propose entropy-punctured Bloom Filters, a memory-aware encoding strategy that removes low-variability bit positions identified using empirical entropy. Starting from fixed-length BF encodings of quantized features, the proposed approach produces reduced representations that preserve predictive structure while improving predictive efficiency relative to encoded representation size. We evaluate the approach on diverse regression datasets, comparing raw features, Principal Component Analysis (PCA), Random Projection (RP), and Bloom Filter variants under leakage-free evaluation protocols and approximately matched representation sizes. Performance is assessed using ridge regression, XGBoost, and neural networks, with predictive efficiency measured as R2 relative to encoded representation size per sample. Results show that Bloom Filter encodings remain competitive with classical compressed representations while achieving substantial storage savings. Entropy-based puncturing further reduces representation size with minimal loss in predictive fidelity, yielding improved predictive efficiency. These findings demonstrate that entropy-punctured Bloom Filters provide an effective representation-level compression approach for memory-constrained machine learning.

Abstract: This paper proposes an innovative dual-polarized backscattering rectenna that operates in two distinct modesenergy harvesting and backscattering modulation-driven by two-bit digital control signals. By utilizing two orthogonal (co-and cross-) polarizations, the design represents a versatile candidate for IoT applications such as battery-free wireless sensing, identification, localization, and communication. The rectenna's dual functionality is validated through its integration into a proofof-concept battery-free wireless sensor, where it operates both as an energy harvester and as a dual-polarized backscattering modulator. As a proof of concept, a 16-byte AES-128 encrypted payload is backscattered over the wireless power transfer link to enhance the resilience of a battery-free Bluetooth Low Energy (BLE) wireless sensor against replay, relay, and eavesdropping attacks.