Dr. Kevin Wijaksana, 2026. "Hardware-in-the-Loop Optimization of Low-Power Wake-Up Receivers in WSN Nodes", International Journal of Electronics and Communication Engineering Research (IJECER) 1(1): 93-110.

Today, the widespread miniaturization and commercialization of electronic devices has resulted in Wireless Sensor networks (WSNs) becoming a staple part of contemporary intelligent systems massively allowing for large scale sensing, monitoring and data acquisition across numerous domains such as environmental monitoring [1], healthcare [2], industrial automation [3], smart agriculture [4], military surveillance [5] and smart city infrastructures. Energy efficiency is also an important problem in WSNs as sensor nodes are usually battery-powered and located in a remote place or inaccessible location. The absence of continuous radio operation and idle listening has a substantial impact on energy consumption resulting into limited lifetime hops with high maintenance complexity. We increasingly use extremely lightweight Wake-Up Receivers (WuRx) to allow sensor nodes to operate in ultra-low power sleep modes and the WuRx continuously listens to communication channels for a special wake-up signal.
Wake-up receivers are highly power-efficient hardware modules that operate with far lower power levels than conventional radio transceivers. Wake-up receivers greatly decrease idle listening power usage and improve the operational duration of sensor nodes by turning on the main communication mechanism when needed. Yet, optimal wake-up receivers are usually achieved by applying some trade-off between sensitivity, latency, reliability, power consumption and hardware complexity. In this paper, we address the difficulties in simulation-based optimization for hardware and software design space with challenging real-world interaction among hardware components, communication channels, and environmental conditions. The constraints faced can be smartly combated with Hardware-in-the-Loop (HIL) optimization that enables to combine a physical hardware, real-time simulation and control frameworks together.
This work considers cooperative beamforming architectures for terahertz wireless backhaul networks. It not only provides thorough insight into the principles of terahertz communication but also beamforming technologies, cooperative transmission frameworks and advanced signal processing techniques for further network enhancement. Different beamforming strategies, resource allocation techniques, synchronization methods and AI-based optimization approaches have been tested to increase the reliability and throughput of communications. The study also analyzes performance metrics such as spectral efficiency, energy efficiency, latency, coverage improvement and network scalability.
This paper explores Hardware-in-the-Loop optimization approaches around low-power wake-up receivers in Wireless Sensor Network nodes. Its principles include architectures of wake-up receiver, energy effective communication combiner and HIL-based optimization approach. To achieve high receiver sensitivity with low energy consumption, as well as reliable communications, we explore several hardware configurations, signal processing strategies, adaptive power management techniques and machine learning-assisted optimization techniques. It investigates additional performance measurements such as wake-up latency, packet detection accuracy, energy consumption, network lifespan and scalability among others.Hardware-in-the-Loop optimization is a powerful tool for improving wake-up receiver performance via experimental analysis showing that realistic testing, dynamic parameter tuning, and efficient hardware-software co-design are achievable. By employing adaptive optimization strategies and intelligent power management mechanisms, these factors will further boost system efficiency with guaranteed communication performance. Overall, the results demonstrate that HIL-based optimization offers an effective strategy for creating advanced power-efficient WSN platforms.
Hardware-in-the-Loop optimization is thus presented as a revolutionary method for the development of low-power wake-up receiver technologies, This concludes the study. In this regard, HIL frameworks leverage upon realistic experimentation, intelligent optimization and smart energy management to promote better utilization of energy resources in order to prolong network lifetime so as to facilitate sustainable wireless sensing infrastructures for future IoT and smart environment applications.

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