A backscatter tag-to-tag network enables battery-less communication by harvesting energy and reflecting wireless signals between tags, making it ideal for energy-efficient IoT applications such as asset tracking, structural health monitoring, and environmental sensing. Accurate localization is crucial for these applications. While RSSI-based (Received Signal Strength Indicator) localization is the most common method for RF localization—estimating distance based on the received signal strength—it is often dependent on the position and power of the excitation source. We present a novel distance estimation method based on the estimation of the channel path loss and phase between tags, which is independent of the excitation source’s position and power. The experimental results demonstrate millimeter-level accuracy in 67% of cases and 99% accuracy within 17 cm for tag-to-tag distances up to 2.4 meters at 915 MHz.

Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.

In the past three decades, we have witnessed unprecedented progress in wireless implantable medical devices (IMDs) that can interface with the nervous system. To provide an even more stable, safe, and distributed interface, a new class of implantable devices is being developed; single-channel sub-mm scale wireless devices. In this research, we describe a new and simple technique for the fabrication and assembly of a sub-mm wirelessly powered stimulating implant. The fabricated implant is composed of an ASIC that measures$\mathbf{900} \times \mathbf{450}\times \mathbf{80}\ \boldsymbol{\mu} \mathbf{m}^{\mathbf{3}}$, two PEDOT-coated microelectrodes, an SMD inductor, and a SU-8 coating. The electrodes and the SMD are directly mounted onto the ASIC. The ultra-small device is powered via electromagnetic (EM) in near-field using a 2-coil inductive link and shows a power transfer efficiency (PTE) of 0.17% in the air with coil separation of 0.5 cm.

Item consumption and social connection, as common user behaviors in many web applications, have been extensively studied. However, most current works separately perform either item consumption or social link prediction tasks, possibly with the help of the other as an auxiliary signal. Moreover, they merely consider the behaviors in a holistic manner yet neglect the multi-faceted motives behind them. For example, the intention of watching a movie could be killing time or watching it with friends; Likewise, one might connect with others due to friendships or colleagues. To fill this gap, we propose to Disentangle the multi-faceted Motives in each network (i.e., the user-item interaction network and social network) defined respectively by the two types of behaviors, for mutually-enhanced Joint Prediction (DMJP). Specifically, we first learn the disentangled user representations driven by motives of multi-facets in both networks. Thereafter, the mutual influence of the two networks is subtly discriminated at the facet-to-facet level. The fine-grained mutual influence is then exploited asymmetrically to help refine user representations in both networks, with the goal of achieving a mutually-enhanced joint item and social link prediction. Empirical studies on three public datasets showcase the superiority of DMJP over state-of-the-arts (SOTAs) on both tasks.

Large scale networks of intelligent sensors that can function without any batteries will have enormous implications in applications that range from smart spaces to structural and environmental monitoring. RF tags present an amenable platform for sensor integration as the backscatter communication offers low energy cost of communication. Current RF tags either use extremely low-power sensors or perform tasks of tag localization and identification based on the strength of the backscatter signal. We present a technique for estimation of amplitude and phase of the tag-to-tag channel that can be performed with very limited computational and energy resources. This enables monitoring of the interactions between tagged objects and activities around tags, as well as assessment of a variety of engineering structures. Experimental results demonstrate high resolution in the amplitude and phase channel measurement at a distances ranging from 22 cm to 1.34 m.

We present a wake-up receiver amenable to integration in a node of RF backscattering tag-to-tag network. A high input impedance of a passive envelope detector (ED) is accomplished by backward bias that improves the passive voltage gain. Two differential outputs are ac-coupled to a baseband amplifier that operates in the subthreshold region. We develop a closed-form model of the passive ED in order to predict the output and ripple voltages and therefor the receiver’s sensitivity. The wakeup receiver is implemented in 180 nm CMOS technology and consumes 2 nW with 0.8 V supply voltage while demodulating 915 MHz amplitude-shift keying (ASK) signal with data rate of 10 kbps. The receiver demonstrates -67.98 dBm sensitivity in resolving ASK modulated signal.

Social networks have proven to be effective for high-quality item recommendation. Most social recommenders, however, are insufficient to capture the user preferences over items, as they largely neglect the multi-facet social relations latent in the social network, such as classmates, colleagues, and families. We, therefore, propose a novel disentangled social recommendation (DSR) framework to exploit the multifacet social relations for enhanced item recommendation. Specifically, DSR explicitly disentangles the social relations into multiple facets, and encodes the social influence under each facet into disentangled user embeddings in the social network. The multiple user embeddings are then aggregated via a facet-level attention mechanism, which distinguishes the effective facets for better inferring user interests over items. Extensive experiments show the superiority of DSR against the state-of-the-art methods and its potential in alleviating the data sparsity issue.

Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.

In this paper, we present a technique whereby passive tags can track each other in a backscattering tag-to-tag network (BTTN). In such a network, passive tags without any on-board radio transceivers communicate directly with each other by backscattering an external excitation signal. First, we explain how the tags determine their distances to other communicating tags in their proximity and then how they can track nearby tags. Our technique is based on multiphase backscattering, more specifically, on the ability of backscattering tags to systematically change the phase offset of the signal that is being backscattered. A passive receiving tag with an envelope detector can then examine the received signal amplitude over the multiple backscattering phases and can draw inferences about the inter-tag distance. We demonstrate our method and show its accuracy on tags that we have built in our lab. Experiments show that our passive tags can measure Doppler shifts with approximately the same accuracy as that achieved by active conventional RFID readers. Our median tracking error based on data from two tags is only about 2.5 cm.

Free floating sub-mm and mm sized brain implants can communicate through a backscatter-based link in a presence of the EM field generated by the external coil. This link reduces the bandwidth requirement in the uplink communication of these implants to the external coil and enables a close-loop operation of the distributed implant system through reduced latency. The critical challenge in the link design stems from the low modulation index in the incident signal at the receiving coil. This calls for the design of the ASK demodulator that can resolve signals with low modulation index. We propose a demodulator design comprising a self-biased common-source based envelope detector that provides sufficient conversion gain and at the same time operates with a low power consumption. With 90 MHz carrier frequency and 50-kbps data rate, the ASK demodulator, implemented in 65 nm CMOS technology, resolves input RF signal with 1% modulation index consuming less than 100 nW when amplitude of the input RF signal is 200 mV.