Electrical substation grounding integrity is fundamental to power system safety, yet its long-term performance is strongly influenced by soil conditions that are difficult to monitor continuously. Existing underground sensing approaches are largely ineffective due to severe signal attenuation in soil, strong electromagnetic interference, and frequent exposure to high-voltage strikes. In this work, we present SoilCapsule, a distributed, battery-free, capsule-style acoustic backscatter sensing system for substation grounding assessment. SoilCapsule harvests ultrasonic energy for computation, sensing, and communication, making it inherently resilient to high-voltage strikes. We design, prototype, and experimentally evaluate the complete end-to-end system. Experimental results show that the proposed grid-to-soil power delivery approach can energize sensors buried at a depth of 100 cm while requiring only 6.17 ppm of the transmit power needed by conventional surface-to-soil schemes to achieve comparable depth. Finally, a field deployment of 20 SoilCapsule sensors in an operational medium-sized electrical substation demonstrates the feasibility of long-term grounding monitoring in real-world environments.

Recent research has advanced the transmission rate and quality of video streaming in backscatter communications. However, these studies often overlook the challenge of deploying such systems in environments without dedicated excitation signals. To address this, we propose VideoBack, a high-quality video backscatter system using ambient WiFi signals for easy monitoring. VideoBack employs JPEG compression on high-resolution images to reduce transmission payload, enabling adaptation to the lower rates of WiFi backscatter. We design a customized packet structure for tags to backscatter image data using multiple uncontrolled ambient WiFi packets. To ensure power efficiency, we incorporate envelope detection with energy harvesting. To enable single-receiver deployment, we design a pilot-subcarrier-based recovery method to reconstruct the original WiFi signal. Our system maintains video reliability through a low bit error rate (BER) decoding strategy that includes phase error tracking and self-correction with multi-subcarriers. We prototype VideoBack using a commercial WiFi adapter, achieving a transmission rate of nearly 250 kbps with a BER below 0.05% up to 9 meters. VideoBack can send one frame of an image after just 2 seconds of energy harvesting within 3 meters.

Currently, a major issue of WiFi-based sensing technologies is how to adapt to changes in the surrounding environment. The extreme sensitivity ofChannel State Information(CSI) makes many WiFi sensing arts frustrated when applied to the complex and unknown real world. To solve this problem, in this paper, we proposefreeEnvdesigned to automatically identify the micro-environmental changes (even tiny movements of the laptop) using WiFi devices, which can coexist with other WiFi sensing tasks with zero effort. To achieve automatic identification of micro-environmental changes, we quantify micro-environmental changes based on the physical propagation laws of WiFi signals and the main factors that affect CSI measurements. Then, we design a micro-environmental changes identification method, which determines whether the environment has changed by calculating theEarth Mover's Distance(EMD) of theProbability Density Function(PDF) of continuous CSI, without requiring training data. To remove the influence of dynamic human behaviors, we design a human dynamic detection scheme, which is achieved by obtaining the average inter-cluster distance of performingGaussian Mixture Model(GMM) clustering on CSI. We evaluatefreeEnvin real-world scenarios with six different hardware, four different scenarios, and twenty-four ways of micro-environmental changes. The results show that our method is robust to different devices and scenarios, and can achieve the average precision of 96.1% and 93.2% for micro-environmental changes identification and human dynamic behavior detection. By testing on a case study of threshold-based human presence detection,freeEnvcan effectively improve the detection performance.

Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings plays a vital role in optimizing the efficiency of wireless vision systems. This article introduces a framework for adaptive adjustment in BLE-based low-power vision systems. To reduce power consumption, we model BLE power usage and design a dynamic BLE parameters and resolution adjustment method for low-power IoT vision system. Additionally, we design an edge-end combined approach that delegates the task of key region prediction to the receiving device to achieve adaptive intraframe JPEG encoding. Our approach leverages commercially available BLE devices without requiring physical layer modifications, ensuring compatibility with standard devices. We design a wireless hardware prototype using off-the-shelf CMOS sensors and BLE Systems on a Chip to evaluate our framework. Extensive experiments demonstrate a 66.9% increase in energy efficiency compared to traditional fixed parameter methods.

Wi-Fi backscatter communication is a passive wireless technology that relies on modulating Wi-Fi signals through reflection. However, due to the limited availability of interference-free channels, interference and collisions between tags can occur, especially as the number of tags increases. Existing solutions employ static channel allocation methods, which restrict scalability and result in higher data packet loss rates. To address these challenges, we propose WiB-MAC. WiB-MAC utilizes a request-and-allocation mechanism to prevent collisions, supports dynamic tag networks, and is scalable for large-scale backscatter communication. Our protocol efficiently utilizes the exciter channel for tag channel requests and employs a Bloom filter-inspired approach to resolve conflicts when multiple tags apply for channels simultaneously. Using this approach, we ensure that only one tag uses a channel at a time, thereby avoiding collisions between tags. We further enhance network throughput by implementing a priority-based scheduling algorithm that reduces the impact of errors caused by tags. Finally, we designed our tag and tested the scenario where three tags simultaneously apply for the channel, demonstrating the feasibility of the protocol. We also compare our protocol to current backscatter communication networks as a baseline. In a network with 64 tags, WiB-MAC achieves 2.75 times the throughput of the baseline. In a network with 500 tags, the baseline’s throughput drops to 0, while WiB-MAC’s throughput only decreases by 5%.

WiFi backscatter communication demonstrates significant potential for the upcoming era of low-power wireless networks. Nevertheless, due to the low-power requirement of backscatter tags, there are limitations in their capacity to eliminate conflicts, posing a significant challenge for WiFi backscatter communication in environments with ambient interference. To address that, we introduce SlickScatter, an interference-insensitive WiFi backscatter system that can retrieve WiFi backscatter signals even in the presence of unknown ambient interference. The core strategy of SlickScatter involves designating a portion of the tag data symbols as pilot symbols. This approach enables the detection of uninterfered subcarriers and the estimation of channel state information and phase errors for all symbols within a packet, facilitating the demodulation of packets affected by interference. We have prototyped and evaluated SlickScatter with 802.11g OFDM WiFi signals, demonstrating its robustness and effectiveness against unknown ambient interference. Compared with a state-of-the-art solution, SlickScatter significantly decreases the frame error rate by 50% and improves the throughput by 1.96× at a distance of 12 m.

Despite the potential for throughput enhancement with multiple tags, existing WiFi backscatter systems have been limited by inband interference among various tags. In response, we propose MultiRider, the first WiFi backscatter system that can tame in-band interference and support multi-tag parallel communication on commercial OFDM protocol. The principle behind MultiRider lies in its ability to demodulate and reconstruct tag data using just one uncorrupted subcarrier in the spectrum domain. To address the inherent challenges of preamble corruption and data collision due to in-band interference, we design three modules: 1) preamble recovery based on a concurrency-driven backscatter packet structure; 2) subcarrier-level demodulation using uncorrupted subcarriers; and 3) iterative interference cancellation for multiple tags. We prototype and evaluate MultiRider under 802.11g OFDM WiFi signals with commercial adapters and software-defined radios. Comprehensive evaluations illustrate that MultiRider can efficiently solve in-band interference. Notably, it can expand the network capacity of WiFi backscatter by 4× and use 8 channels in the 2.4GHz WiFi band for concurrent communication. Further results reveal that MultiRider can gain 10× network capacity in 35MHz bandwidth and reach 2.29 Mbps system throughput.

Low-power perception technologies, notable for their micro-watt-Ievel energy consumption, offer the capability for years of continuous data sensing and communication without the necessity for battery replacement. These characteristics make them extremely viable for large-scale deployment and position them as a pivotal trend in the future development of the Internet of Things. Low-power video transmission is a critical application. Low-power devices often have weaker signals, which can lead to consecutive packet loss, resulting in severe image defects at the receiver end. To address this, we propose a novel image transmission method at the transmitter side, which transmitting image blocks or rows using a specific sequence, significantly reducing the risk of consecutive block loss under severe channel interference or at low signal-to-noise ratio conditions, thereby en-hancing system stability under continuous packet loss conditions. Therefore, We adjust the deep learning model for real-world scenarios, achieving better image restoration in environments with high packet loss rates. We constructe a prototype based on backscatter communication cameras. Experiments conducte on this platform demonstrate that we can recover the majority of image information, even with a packet loss rate of 75%, by using traditional algorithms or deep learning approaches. This significantly enhances the visibility of low-power visual perception systems.

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object’s movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation’s mean error is under 7 degrees. The system’s accuracy in detecting the target object’s trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we proposeSpray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype theSpraytag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps.

Recent works have achieved considerable success in increasing the transmission rate of video streaming backscatter communications. However, they do not consider the ease of deployment in real-world environments where these dedicated excitations are not readily available. In addition, these works are transmitted with lower image resolution, and the images received by users are not eye-friendly. In this paper, we propose VideoBack, a high quality video backscatter system with commercial WiFi excitation for easy-to-use monitoring. The key idea is to perform JPEG compression on high-pixel images to adapt to intermittent ambient WiFi transmission. This approach allows ambient commercial routers to be used for excitation. Furthermore, our work enables high-quality video transmissions by devising a low bit error rate decoding scheme. We build a prototype of VideoBack and use a commercial WiFi adapter to excite the tag to realize the transmission and reception of video. The prototype uses RF signals to charge and store energy. Our evaluations show that VideoBack can transmit images at a throughput of nearly 250 kbps with bit error rate below 0.0005 within 9 meters, while the images only have minor distinctions. VideoBack can support sending one frame of image after just 2 seconds of power acquisition within 3m.

In this paper, we present a novel object tracking system, named STABack, that utilizes backscatter tags and accelerometer sensors. The system is designed to support high-speed movement tracking with high accuracy. One of the challenges faced in this system is the instability of the received signal due to the motion and rotation of the backscatter tag. To address this issue, we propose an amplitude stabilization algorithm to eliminate the interference caused by the tag's motion. The algorithm uses envelope detection to remove the impact of high-frequency motion on the signal, and a dynamic threshold output to further reduce the bit error rate of backscatter demodulation. Additionally, we perform outliers removal and interpolation on the three-axis accelerometer data and compute the attitude angle using 3D geometric quadrants. Finally, we implement the prototype of STABack and evaluate its performance. Our method improves BER up to 0.3757 compared with the regular demodulation method. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm. The mean error of the attitude angle estimation is less than 7 degrees, and the average relative error of three-axis acceleration tracking is only 0.097. The system's accuracy in detecting the target object's trajectory is as high as 98%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of backscatter communication is$38.54\ \mu\mathrm{W}$. Based on our experimental results, Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.