作者
Xin Wang,Lewis Nkenyereye,Shalli Rani,Jianhui Lyu
摘要
Internet-connected robotic systems today predominantly rely on isolated sensing, computing, and communication modules, limiting cross-layer optimizations. However, emerging applications like industrial automation and augmented reality necessitate tight coupling between complementary capabilities for versatility, precision, and autonomy improvements. To this end, this paper proposes an adaptive sensing algorithm for Internet of Robotic Things (IoT) platforms with integrated sensing, computing, and communication (as-ISCC-IoRT) capabilities. The framework leverages a model-driven methodology to dynamically harness the benefits of complementary techniques for improving localization accuracy and operational efficiency. First, three classical sensing algorithms are introduced to realize multi-target ranging and speed measurement, and the algorithms are analyzed in terms of sensing accuracy, communication performance, and computational complexity, which shows that any one of the algorithms alone cannot achieve the optimization of sensing accuracy, sensing capacity, and communication rate simultaneously. Then, combining the characteristics of different sensing algorithms, an adaptive sensing algorithm is proposed, and the receiver selects the appropriate sensing algorithm based on the ratio of the measured received signal to the interference plus noise. Extensive simulations under varying signal-to-interference-plus-noise ratio levels, number of sensors, and quality of service constraints validate the effectiveness of as-ISCC-IoRT -consistently showing the fastest convergence, lowest weighted MSE, highest communications rate gain, and minimum transmit power by adaptively switching between component algorithms. The consistent performance gains of the proposed as-ISCC-IoRT scheme across key metrics like accuracy, latency, and efficiency validate the benefits of integrating sensing, computing, and communication capabilities in Internet-connected robotic systems.