The vision of sustainable 6G connectivity infrastructure, from space to ground, critically relies on green communication protocols that can efficiently operate within the constraints of wireless power transfer (WPT). Backscatter communication emerges as a cornerstone for such protocols, yet its potential is hindered by the inability to adapt to the dynamic spectrum and energy conditions inherent to WPT-powered networks. This paper presents ChannelDance, an edge-assisted adaptive hopping system for Bluetooth Low Energy (BLE) backscatter, architected specifically for green and sustainable connectivity in WPT-enabled environments. By leveraging real-time excitation channel intelligence from a low-latency edge server, ChannelDance dynamically configures the tag modulation clock, enabling robust and spectrally agile frequency hopping. This agility is paramount for maintaining reliable communication links amidst the interference and intermittent energy supply characteristic of integrated WPT systems. Our prototype demonstrates a median hopping success rate of 93% across 40 channels, a 3.5× goodput gain with channel optimization, and the ability to establish connections with commodity BLE devices under hopping conditions. ChannelDance thus establishes a foundational green communication primitive for future sustainable 6G networks, where seamless coexistence with energy transfer signals is not a feature but a fundamental requirement.

Existing IoT systems suffer from restricted communication distances, high deployment costs, and frequent battery replacements, making them ineffective for managing healthcare data. This paper presents Prometheus, a self-powered wristband for reporting personal health status over long distances and intelligently managing healthcare data. Prometheus backscatters ambient BLE and ZigBee signals for low-power communication while incorporating a multi-source energy harvester to convert ambient RF, light, and heat into electricity. It also features a biochemical sensor array for monitoring sweat biochemical markers. Prototyped on a flexible PCB, Prometheus demonstrates impressive efficiency, consuming only 5.8 mW for sweat sensing, with BLE and ZigBee transmission energies significantly lower than standard electrochemical workstations and commercial alternatives. Our experiments show consistent signal quality at distances up to 20 meters. In summary, Prometheus emerges as a convenient, efficient, and self-powered wristband, promising to provide ubiquitous healthcare data management in our lives.

AI-aided Financial Regulation (AIFR) is a practical and significant task, but current solutions have yet to be optimized with customized model designs. Given the privacy concerns surrounding financial data, we aim to employ Neural Architecture Search (NAS) to help non-expert end-users automatically design architectures. The genetic algorithm-based NAS stands out due to its relatively low hardware requirements and robust theoretical foundation. However, constrained by limited data, the model would undergo architecture search on a general regulatory dataset while being deployed on private one owned by each organization. The data distribution of the private dataset may vary from that of public datasets, giving rise to the challenge of data domain shift. To alleviate this problem, we propose a novel fitness evaluation method. When scoring the fitness, we take into account both the architecture’s validation accuracy and its potential for generalization by the metric of loss landscape. In addition, we improve the training paradigm for evaluation, utilizing a prototype-based training paradigm based on embedding distances for classification, allowing for rapid domain adaptation and improve performance on the distribution-shift data. We further introduce GA-TextCNN, a GA-based NAS framework specifically designed for text recognition, enhancing its suitability for text data within AIFR tasks. To demonstrate the effectiveness of our approach, we collect two related datasets and evaluate our method on it. The extensive experiments demonstrate that our method significantly improves baseline models and is effective in solving the AIFR problem.

Different from intermittent ISM signals like Bluetooth and WiFi, LTE signals are continuous in time and more pervasive in space, which makes them suitable carriers for ambient backscatter systems. However, due to the continuous LTE traffic and complex frame structures, existing ambient backscatter systems, such as HitchHike and LScatter, cannot reliably backscatter LTE signals in a standard-compatible way. We observe that the primary cause of their failures is that tags cannot accurately synchronize with LTE excitations. To address this issue, we propose SyncLTE, an LTE backscatter system that achieves high-accuracy synchronization and standard-compatible backscatter communication. The key novelty is a new tag design that uses the periodicity of LTE signals for synchronization and provides a customized single-symbol modulation scheme for LTE carriers. Our design is prototyped using FPGAs, SDR LTE eNBs and UEs. Comprehensive experiments have been done in various scenarios, including LoS, NLoS, indoor and outdoor. Results show that SyncLTE is 22.4x and 7.4x better than LScatter and Multiscatter in terms of the 80th percentiles of synchronization errors. Also, SyncLTE can deliver throughputs of up to 200 bps using BPSK and 400 bps using QPSK while other systems suffer from failures.

The use of controlled excitation makes pervasive backscatter communication difficult to achieve and the redundant modulation severely limits the performance of the system. We present a novel WiFi backscatter system that can take uncontrolled OFDM WiFi signals as excitations and efficiently embed tag data at the single-symbol rate. Specifically, we are the first to discover the fundamental reason why the previous systems have to rely on multi-symbol modulation, which makes it possible to demodulate tag data on the single-symbol level. Further, we design deinterleaving-twins decoding that can reuse any uncontrolled WiFi signals as carriers to backscatter tag data. Moreover, we present how to robustly handle high-order excitations, including different demapping rules for diverse excitations and three different bit-translation methods for decoding. To verify the effectiveness of our proposal, we prototype our solution using various FPGAs and SDRs. Comprehensive evaluations show that our solution’s maximum throughput is 3.92x and 1.97x better than FreeRider and MOXcatter. In addition, with 16QAM excitations, the decoding BERs of majority voting are around 5%, which is 10x better than subsequence matching and jaccard similarity methods. Meanwhile, the throughput of deinterleaving-level demodulation is 2x better than payload-level demodulation with 16QAM ambient traffic.

This paper presents GLAC, the first 3D localization system that enables millimeter-level object manipulation for robotics using only COTS RFID devices. The key insight of GLAC is that mobility reduces ambiguity (One-to-many mapping relationship between phase and distance) and thus improves accuracy. Unlike state-of-the-art systems that require extra time or hardware to boost performance, it draws the power of modeling mobility in a delicate way. In particular, we build a novel framework for real-time tracking using the Hidden Markov Model (HMM). In our framework, multiple Kalman filters are designed to take a single phase observation for updating mobility states, and a fast inference algorithm is proposed to efficiently process an exponentially large number of candidate trajectories. We prototype GLAC with only UHF tags and a commercial reader of four antennas. Comprehensive experiments show that the median position accuracies of x/y/z dimensions are within 1 cm for both LoS and NLoS cases. The median position accuracy for slow-moving targets is 0.41 cm, which is 2.2$\times$, 17.3$\times$, and 14.9$\times$better than TurboTrack, Tagoram, and RF-IDraw, respectively. Also, its median velocity accuracy is at least 20$\times$better than all three competitors for fast-moving targets. Besides accuracy, it achieves more than 4$\times$localization time gains over state-of-the-art systems.

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

This paper presents RapidRider, the first WiFi backscatter system that takes uncontrolled OFDM WiFi signals, e.g., 802.11a/g/n, as excitations and efficiently embeds tag data at the single-symbol rate. Such design brings us closer to the dream of pervasive backscatter communication since uncontrolled WiFi signals are everywhere. Specifically, we show that RapidRider can demodulate tag data for each OFDM symbol while previous systems rely on multi-symbol demodulation. Further, we design deinterleaving-twins decoding that enables RapidRider to use any uncontrolled WiFi signals as carriers. We prototype RapidRider using FPGAs, commodity radios, and USRPs. Comprehensive evaluations show that RapidRider's maximum throughput is 3.92x and 1.97x better than FreeRider and MOXcatter. To accommodate cases where there is only one receiver available, we design RapidRider+ that can take productive data and tag data on the same packet. Results demonstrate that it can achieve an aggregated goodput of productive and tag data around 1 Mbps on average.

The communication reliability of state-of-the-art Bluetooth Low Energy (BLE) backscatter systems is fundamentally limited by their modulation schemes because the Binary Frequency Shift Keying (BFSK) modulation of the tag does not exactly match commodity BLE receivers designed for Gauss Frequency Shift Keying (GFSK) modulated signals with high bandwidth efficiency. Gaussian pulse shaping is a missing piece in state-of-the-art BLE backscatter systems. Inspired by active BLE and applying calculus, we present IBLE, a BLE backscatter communication system that achieves full compatibility with commodity BLE devices. IBLE leverages the fact that phase shift is the integral of frequency over time to build a reliable physical layer for BLE backscatter. IBLE uses instantaneous phase shift (IPS) modulation, GFSK modulation, and optional FEC coding to improve the reliability of BLE backscatter communication to the commodity level. We prototype IBLE using various commodity BLE devices and a customized tag with FPGA. Empirical results demonstrate that IBLE achieves PERs of 0.04% and 0.68% when the uplink distances are 2 m and 14 m respectively, which are 280x and 70x lower than the PERs of the state-of-the-art system RBLE. On the premise of meeting the BER requirements of the BLE specification, the uplink range of IBLE is 20 m. Since BLE devices are everywhere, IBLE is readily deployable in our everyday IoT applications.

This paper presents GLAC, the first 3D localization system that enables millimeter-level object manipulation for robotics using only COTS RFID devices. The key insight of GLAC is that mobility reduces ambiguity and thus improves accuracy. Unlike state-of-the-art systems that require extra time or hardware to boost performance, it draws the power of modeling mobility in a delicate way. In particular, we build a novel framework for real-time tracking using the Hidden Markov Model (HMM). In our framework, multiple Kalman filters are designed to take a single phase observation for updating mobility states, and a fast inference algorithm is proposed to efficiently process an exponentially large number of candidate trajectories. We prototype GLAC with only UHF tags and a commercial reader of four antennas. Comprehensive experiments show that the median position accuracies of x/y/z dimensions are within 1 cm for both LoS and NLoS cases. The median position accuracy for slow-moving targets is 0.41 cm, which is 2.2×, 17.3×, and 14.9× better than TurboTrack, Tagoram, and RF-IDraw, respectively. Also, its median velocity accuracy is at least 20 better than all three competitors for fast-moving targets. Besides accuracy, it achieves more than 4× localization time gains over state-of-the-art systems.

We present Apollo, a self-powered wireless biochemical system that is wearable and can continuously monitor personal health states. It has three key enablers. First, we design an ultra-low-power Bluetooth backscatter module, which can effectively take BLE signals as excitations and transmit sensor data at high data rates to smartphones. Further, at the core of self-power management is a novel multi-source harvester that can transform RF, light, and thermal energies into electrical energy. Moreover, we introduce a biochemical sensor array that can accurately measure sweat metabolites and electrolytes.We prototype Apollo on a compactly integrated flexible PCB using all commercial off-the-shelf components. Through extensive experiments and field studies, we show that it is able to measure the glucose level in sweat within 5% error rate while consuming 5.8 mW for sensing and 720 pJ/bit for wireless transmissions. This translates to over 1700x lower power than standard electrochemical workstations, and 26x lower power than existing commercial BLE chipsets. We believe Apollo marks an important step towards the dream of ubiquitous healthcare for everyone as it provides a highly convenient, self-managed, and fully integrated way for health monitoring.

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a robust BLE backscatter system that works with an excitation BLE device and a single BLE receiver. First, it uses BLE signals with partial single tones as excitations, making single-bit modulation much more robust. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios, iPhones, Android phones, and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink goodput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

Backscatter communication with ambient excitations receives great attention recently as it provides a practical battery-free way to convey various IoT data. However, state-of the-art solutions are of low data rates and thus cannot serve highbandwidth applications, e.g., live streaming. This paper presents Hermit Crab, the first WiFi-backscatter system that achieves high-throughput communication for video streaming. The key contribution is a differential decoding algorithm using pilot phase. By doing so, it supports single symbol encoding, which is much faster than multi-symbol encoding of previous systems. In addition, Hermit Crab can recover the production data and tag data at the same time. Through extensive experiments, we show that it achieves throughputs of up to 960 Kbps with 802.1lg ambient signals, which is 7. 6x better than the state-of-the-art system. We also demonstrate that with such good throughputs, it can stably support live streaming of 480p videos at 30 fps.

Backscatter offers a novel low-cost and low-energy solution for tags to communicate with existing wireless devices. The latest Bluetooth standards (i.e., Bluetooth 4. x and Bluetooth 5) use the Bluetooth Low Energy technology, which is the mainstream of the current Bluetooth market. In this paper, we present multi-channel backscatter with BLE, a BLE backscatter communication system that achieves compatibility with standard BLE devices. Tag information in backscatter communication can be directly obtained from backscatter packets with a single BLE receiver. We present the first multichannel backscatter tag design and build a prototype of our tag using an FPGA and evaluate it with BLE devices. Our evaluation shows that the tag can backscatter BLE signals in a channel-hopping manner, which can be decoded by standard BLE devices. Results demonstrate that our backscatter system can achieve a goodput of up to 2.8 kbps.

Recently backscatter communication with commodity radios has received significant attention since specialized hardware is no longer needed. The state-of-the-art BLE backscatter system, FreeRider, realizes ultra-low-power BLE backscatter communication entirely using commodity devices. It, however, suffers from several key reliability issues, including unreliable two-step modulation, productive-data dependency, and lack of interference countermeasures. To address these problems, we propose RBLE, a reliable BLE backscatter system that works with a single commodity receiver. It first introduces direct frequency shift modulation with the single tone generated by an excitation BLE device, making robust single-bit modulation possible. Then it designs dynamic channel configuration that enables channel hopping to avoid interfered channels. Moreover, it presents BLE packet regeneration that uses adaptive encoding to further enhance reliability for various channel conditions. The prototype is implemented using TI BLE radios and customized tags with FPGAs. Empirical results demonstrate that RBLE achieves more than 17x uplink goodput gains over FreeRider under indoor LoS, NLoS, and outdoor environments. We also show that RBLE can realize uplink ranges of up to 25 m for indoors and 56 m for outdoors.

Recently backscatter networks have received booming interest because, they offer a battery-free communication paradigm using propagation radio waves as opposed to active radios in traditional sensor networks while providing comparable sensing functionalities, ranging from light and temperature sensors to recent microphones and cameras. While sensing data on backscatter nodes has been seen on a clear path to increasing in both volume and variety, backscatter communication is not well prepared and optimized for transferring such continuous and high-volume data. To bridge this gap, we propose a high-throughput rate adaptation scheme for backscatter networks by exploring the unique characteristics of backscatter links and the design space of the ISO 18000-6C (C1G2) protocol. Our key insight is that while prior work has left the downlink unattended, we observe that the quality of downlink is affected significantly by multipath fading and thus can degrade the uplink and overall throughput considerably. Therefore, we introduce a novel rate mapping algorithm that chooses the best rate for both the downlink and uplink. Also, we design an efficient channel estimation method fully compatible with the C1G2 protocol and a reliable probing trigger, substantially saving probing overhead. To combat interference, we further design an interference detector using clusters and lightweight countermeasures to make rate adaptation more robust. Our scheme is prototyped using commercial RFID readers and tags. The results show that we can achieve up to 2.6× throughput gain over state-of-the-art approaches across various mobility, channel, network-size, and interference conditions.

In the past few years, various backscatter nodes have been invented for many emerging mobile applications, such as sports analytics, interactive gaming, and mobile healthcare. Backscatter networks are expected to provide a high-throughput and stable communication platform for those interconnected mobile nodes. Yet, through experiments, we find state-of-the-art rate adaptation methods for backscatter networks share a fundamental limitation of accommodating the hardware diversity of nodes because the common mapping paradigm that chooses the optimal rate based on the radio signal strength indicator (RSSI) or the like is hardly adaptable to hardware-dependent RSSIs. To address this issue, we propose MobiRate (Mobility-aware Rate adaptation) that fully exploits the mobility hints from PHY information and the characteristics of backscatter systems. The key insight is that mobility-hints, like velocity and position, can greatly benefit rate selection and channel probing. Specifically, we introduce a novel velocity-based loss rate estimation method that dynamically re-weighs packets based on time and mobility. In addition, we design a mobility-assisted probing trigger and a new selective-probing mechanism, significantly saving probing time. As MobiRate is fully compatible with the current standard, it is prototyped using a COTS RFID reader and a variety of commercial tags. Our extensive experiments demonstrate that MobiRate achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

Recently backscatter networks have received booming interest because, they offer a battery-free communication paradigm using propagation radio waves as opposed to active radios in traditional sensor networks while providing comparable sensing functionalities, ranging from light and temperature sensors to recent microphones and cameras. While sensing data on backscatter nodes has been seen on a clear path to increase in both volume and variety, backscatter communication is not well prepared and optimized for transferring such continuous and high-volume data. To bridge this gap, we propose a high-throughput rate adaptation scheme for backscatter networks by exploring the unique characteristics of backscatter links and the design space of the ISO 18000-6C (C1G2) protocol. Our key insight is that while prior work has left the downlink unattended, we observe that the quality of downlink is affected significantly by multipath fading and thus can degrade the uplink and overall throughput considerably. Therefore, we introduce a novel rate mapping algorithm that chooses the best rate for both the downlink and uplink. Also, we design an efficient channel estimation method fully compatible with the C1G2 protocol and a reliable probing trigger, substantially saving probing overhead. Our scheme is prototyped using a COTS RFID reader and tags. The results show that we achieve up to 2.5x throughput gain over state-of-the-art approaches across various mobility, channel, and network-size conditions.