Ambient IoT (A-IoT) aims to connect hundreds of billions of ultra-low-power and battery-free devices, which has been included in the agenda for 6G standardization by 3GPP. Backscatter communication is considered the mainstream enabling technique for A-IoT; however, current state-of-the-art can hardly meet A-IoT's main technical requirements simultaneously: power consumption below 100 μW, communication ranges up to 100 m, and 100+ concurrency. This paper presents NanoScatter, the first backscatter network with each tag implemented using our customized backscatter communication ASIC. We propose a nanowatt wake-up receiver design and a sensitivity-driven downlink/uplink modulation mechanism to carry out the ASIC, which enables minimizing the tag's power consumption and long-range communication. NanoScatter supports concurrent communication of 6 IC-based tags with a subcarrier capacity of 512, achieving communication distances of 66 m indoors and 100 m outdoors. The tag consumes 1 μW in idle listening, with the core circuit using 58 nW and 43 μW during communication.

Ambient IoT (A-IoT) targets battery-free, ultra-low-power connectivity for massive devices, which has been a key focus in 6G standardization by 3GPP. While backscatter communication enables A-IoT, existing solutions struggle to meet its core demands simultaneously: power consumption below 100 μW, communication ranges up to 100 m, and 100+ concurrency. In this demo, we present NanoScatter, the first backscatter network with each tag implemented using our customized backscatter communication ASIC. We propose a nanowatt wake-up receiver design and a sensitivity-driven downlink/uplink modulation mechanism to carry out the ASIC, which enables minimizing the tag's power consumption and long-range communication. NanoScatter supports concurrent communication of 6 IC-based tags with a subcarrier capacity of 512, achieving communication distances of 66 m indoors and 100 m outdoors. The tag consumes 1 μW in idle listening, with the core circuit using 58 nW and 43 μW during communication.

Underwater bionic covert communication technology is a novel means of hiding underwater information. In order to realize hydroacoustic bionic stealth communication, this paper proposes an underwater bionic stealth communication scheme based on humpback whale tick signal grouping combination coding. The scheme constructs a bionic stealth communication sequence based on the time-frequency characteristics, autocorrelation characteristics and distribution characteristics of the individual tick signals in the humpback whale tick sequences. The effectiveness and reliability of the proposed covert communication method is verified through simulation tests.

Abstract As a popular renewable energy generation technology, wind turbine system has become a critical enabler for building the sustainable cyber‐physical system (CPS). The main shaft bearing is an important part of the wind turbine CPS and often runs under variable working conditions. Thus, the reliable bearing diagnosis method can timely discover the main shaft bearing fault, which reduces the maintenance cost of wind turbines. Inspired by the idea of domain adaptation, we combined domain adversarial neural network and residual network and proposed a novel deep domain adversarial residual neural network (DDA‐RNN) for diagnosing bearing fault and improving model performance on the unlabeled dataset. This proposed software and hardware co‐design method was evaluated by our bearing dataset, which was collected from two wind turbine CPSs from Sanmenxia in Henan Province. Besides, F1 score and accuracy are served as model metrics, which reflect the diagnosis performance. Compared with other methods, the experimental results show that DDA‐RNN can improve model performance. Meanwhile, DDA‐RNN extracts diagnosis knowledge from labeled dataset and improves the model performance on the unlabeled dataset under different working condition. Therefore, the proposed method can be potentially used to benefit many practical scenarios in the future.