Enhancing sensor longevity is crucial for the effective operation of wireless sensor networks (WSNs). Energy harvesting (EH) sensors address this challenge by harvesting ambient energy and extending operational lifespans. However, without energy-efficient resource allocation, the dynamic nature of EH rates can disrupt node operations and degrade network performance. Existing studies have separately addressed crucial challenges in resource allocation scenarios under uncertainties in EH-WSNs. In contrast, our work presents a unified framework that optimizes energy efficiency (EE) under uncertainty by adopting an intelligent probabilistic approach that integrates energy-efficient resource allocation with EH dynamics for a unified solution. We achieve this by reformulating the conventional deterministic optimization problem (DOP) into a set of probabilistic optimization problems (POPs), encompassing stochastic, robust, and chance-constrained models. To address the complexity of solving non-convex POPs in uncertain and dynamic environments, we propose a custom-tailored sample average approximation (SAA)-assisted deep reinforcement learning (DRL) optimizer employing a built-in Deep Q-Network (DQN) agent. Leveraging the inherent adaptivity of SAA-assisted DRL, the proposed framework dynamically adjusts to varying environmental conditions, enabling unified and efficient optimization in the face of uncertainty. Furthermore, to ensure a robust and credible benchmark, we also employ Double DQN (DDQN) as a DRL baseline, enabling evaluation of our method against multiple variants and facilitating a clear comparison of convergence behaviors. Simulation results demonstrate that our unified probabilistic framework achieves near-optimal performance in terms of mean absolute error and convergence rate, even in the presence of EH uncertainties.

Machine condition monitoring and predictive maintenance are crucial technologies in modern industrial settings. Wireless sensor networks (WSNs) are commonly used to gather machine data with high flexibility and minimal installation effort. However, traditional WSN approaches that periodically or selectively transmit raw data either lack predictive capability or consume excessive energy. Furthermore, conventional static Edge-AI models running entirely on sensor nodes struggle to adapt to dynamic and complex industrial conditions due to limited labelled failure data and unpredictable machine dynamics. In this paper, we propose and evaluate a hybrid edge-central AI architecture. In this approach, sensor nodes perform the feature extraction as the first layer of the AI model, while deeper adaptive model layers operate at the central base station. This approach reduces energy consumption by limiting radio transmissions and enabling the use of complex, adaptive AI models. We validate the proposed architecture by implementing a set of common features on a typical ARM Cortex-M4 microcontroller used in wireless sensor nodes. We target the architecture of our previously developed wireless 1kS/s (kilo-sample per second) accelerometer. Results demonstrate that these features can be computed in only 32.5 ms and consume 32.43 μW. This represents a significant energy saving compared to raw measurement transmission (686.4 μW), highlighting the effectiveness and feasibility of our hybrid approach for industrial monitoring.

Accurate synchronization of data samples is crucial for wireless sensor networks (WSNs) used for vibration monitoring and condition diagnostics. This paper describes two practical methods for achieving sample-level synchronization with better than 50 microsecond accuracy on battery-powered wireless MEMS accelerometer nodes. The first method uses a hardware-based real-time synchronization approach, where each sensor node has a 1.024 MHz clock which is synchronized through periodic interrupt pulses. The second method employs a software-based cross-correlation alignment to achieve precise synchronization after data acquisition, which is suitable for cases without accurate hardware clocks. Both methods are implemented using compact Bluetooth Low Energy (BLE) sensor nodes with off-the-shelf components. Experimental results on a shaker test platform confirm that synchronization within 50 µs is reliably achieved with both methods, demonstrating the suitability of these methods for industrial vibration monitoring applications.

Wireless transceivers are typically the most power-hungry component in wireless sensor nodes. A closer investigation into the power consumption behavior of these components can lead to the optimization of wireless sensor networks or IoT systems. In this paper, we present an analytical method based on the energy efficiency Figure of Merit (FoM) to estimate the power consumption characteristics of a typical MOSFET-based wireless transceiver. We show that the best energy efficiency can be achieved in a radio transceiver when the energy consumed in the receiving mode is approximately equal to the energy consumed during transmission.

There is an increasing demand for sensing and measuring different parameters in vacuum environments. However, using cables for communication and power supply of the sensor nodes in vacuum chambers is not desirable. In this paper, we investigate the applicability of wireless sensor networks in vacuum environments by studying experimentally the reliability of wireless communication through vacuum chamber walls, as well as within vacuum chambers.

This work evaluates experimentally the applicability of wireless sensors networks (WSNs) based on Bluetooth Low Energy (BLE) for operational modal analysis (OMA) in machine diagnostics. OMA enables the dynamic properties of a mechanical system to be determined based on vibration measurements. A WSN for collecting such measurements requires precise synchronization, the transfer of relatively large volumes of data, and a lifetime of several days or weeks. The higher data rate of BLE 5 (2 Mbps), compared to other prominent radio technologies used in WSNs, offers several advantages, e.g. it could allow a reduction in the overall radio utilization, which generally contributes the most to the power consumption of a sensor node. However, the reliability of BLE 5 links in a machine environment must be assessed to determine the applicability of this radio technology in OMA. This work presents experimental results on the quality of BLE 5 links operating inside an industrial machine which indicate that such links can operate reliably when using a relatively high transmission power (above 0 dBm).

Selection of an energy efficient wireless transceiver is an important step in the design of any low power wireless system, specifically wireless sensor networks or IoT devices. In this paper, a figure of merit (FoM) for evaluating the energy efficiency of wireless radio transceivers is proposed. Using this FoM, it is possible to easily compare the performance of transceivers built with completely different technologies, in term of energy efficiency. This FoM can be used as a practical tool for researchers and engineers to evaluate wireless transceivers and determine the state-of-the-art or select a desired transceiver.

Wireless sensor networks (WSNs) are gaining increasing popularity in industry. Success of these systems depends on two very important factors: power efficiency of the sensor nodes and communication reliability. In this paper, we investigate the effect of using unidirectional (broadcasting) communication on the power consumption and reliability of a wireless sensor node. A high-precision wireless temperature sensor reported in an earlier publication is employed as a case study. First, we calculate the energy required to transmit and receive a message using Bluetooth low energy (BLE) in the physical layer without taking any reliability precautions. Then, we estimate the amount of energy required for the reliable transmission of a BLE packet using the BLE acknowledgment method and forward error correction (FEC) in the application layer. Through this paper, we show that the power consumption of a wireless temperature sensor can be reduced using broadcast communication and simultaneous forward error correction while providing enough reliability in short ranges.