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 RF energy harvester and management strategy tailored for the passive near-zero power devices. Radio- less RF-powered backscattering tags that have the ability to recognize and localize activities in the surrounding environment are example of such devices. We propose a management strategy that determines the operation regime of the harvester based on the input power level at which harvester provides the instantaneous supply voltage for device operation. As the input power exceeds this level, the storage of the excess energy is managed by an adaptive capacitor charging circuit that keeps the voltage at the input of voltage regulator constant. We demonstrate that backscatter-based RF tag in the listening mode of operation can instantaneously operate with an input power of -34.4 dBm. Due to the adaptive capacitor charging circuit, the power efficiency of the energy harvester is higher than 50% over a range of input powers from -25 dBm up to -5 dBm.

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.

We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this technique, we basically explore the backscatter channel by reflecting the incident RF signal with different changes in the phase. We define a measure of the backscatter channel, which we call backscatter channel state information (BCSI). The BCSI is composed of backscatter channel phase, backscatter amplitude, and change in baseline excitation level. When acquired over time, this measure provides rich RF analytics that can be used to extract various types of information from the environment of the tags by signal processing/machine learning methods. We show in the paper that this analytics is invariant w.r.t. to some variables including the deployment environment. We provide results from experiments with our tags that demonstrate the invariance of the BCSI.

We envision a future where every object in our living and working environment will carry one or more RF tags. Based on the backscattering tag-to-tag communication link, these RF tags will be connected in a network without the need for the central interrogating device. We present a novel tag architecture that enables estimation of the parameters of wireless tag-to-tag channel by a passive receiver. Sampling the received baseband signal at different reflecting phases at the backscattering tag enables estimation of amplitude and phase of the tag-to-tag channel. The low-power implementation of the channel estimator, after envelope detection, integrates amplification and filtering of the baseband signal that is followed by analog-to-digital conversion. The channel estimator, implemented in 65 nm CMOS technology, has sensitivity of -45 dBm at 2.5% modulation index and consumes 122 nW.

We present the vision of BARNET (Backscattering Activity Recognition NEtwork of Tags), a network of passive RF tags that use RF backscatter for tag-to-tag communication. BARNET not only provides identification of tagged objects but also can serve as a 'device-free' activity recognition system. BARNET's key innovation is the concept of backscatter channel state information (BCSI) which can be measured via systematic multiphase probing of the backscatter tag-to-tag channel using innovative processing on the passive tags. So far such measurements were only possible using active radio receivers that consume much higher power. Changes in BCSI provide signatures for different activities in the environment that can be learned using suitable machine learning tools. We develop the BARNET tag architecture which shows that an ASIC implementation can run on harvested RF power. We develop a printed circuit board (PCB) prototype using discrete components to evaluate activity recognition performance. We show that the prototype can recognize human daily activities with an average error around 6%. Overall, BARNET uses passive tags to achieve the same level of performance as systems that use powered, active radios.

Radio frequency (RF)-powered backscatter communication between passive tags holds tremendous potential as an enabling technology for a ubiquitous “Internet of Things.” We develop a backscattering tag-to-tag network (BTTN), comprised of passive tags capable of large-scale, passive, and multihop communication with each other via backscatter modulation of an external RF excitation signal. The low sensitivity and lack of active demodulator on passive tags present significant challenges to the communication, including a unique phase cancellation problem, which significantly affects the range and robustness of a passive tag-to-tag link. We overcome these challenges using innovative tag architecture and also develop a novel multiphase backscatter modulation technique with a learning mechanism that overcomes the phase cancellation problem. This improves the link performance bringing passive tag-to-tag communication closer to practical use. The additional hardware compared to the conventional radio frequency identification tag architecture includes one more terminating impedance in the modulator. The data rate is reduced due to backscatter at two different phases while the increase in the power consumption is negligible. We develop prototype BTTN tag hardware and firmware and evaluate its performance. The prototype achieves link ranges of up to 3 m at 5 kb/s with an excitation power level of only -20 dBm while successfully overcoming phase cancellation. We further extend BTTN operation to a multihop network where we demonstrate a four hop link capable of communicating over 12 m under similar conditions.

Backscatter-based tag-to-tag communication (BBTT) is a paradigm wherein radio-less devices communicate with each other by using purely passive backscatter modulation. This allows for highly inexpensive and low power devices. Traditional backscattering devices like RFID tags are designed to communicate directly with an active reader leading to a centralized framework centered on the reader. Under a BBTT network, the tags talk to each other using backscattering in the presence of an external excitation signal, which can come from multiple sources (e.g., dedicated exciters, WiFi access points, TV towers, or cell phone towers). The two main components that determine the range and robustness of a passive tag-to-tag link are the power harvesting and demodulation circuit blocks in the analog front end (AFE). In this paper, we investigate the design constraints, optimization goals, and tradeoffs in the design of the AFE for BBTT tags. We first analyze the BBTT link theoretically and then verify the predicted optimal AFE parameters by simulations.

In this paper, we investigate a unique phase cancellation problem that occurs in backscatter-based tag-to-tag (BBTT) communication systems. These are systems wherein two or more radio-less devices (tags) communicate with each other purely by reflecting (backscattering) an external signal (whether ambient or intentionally generated). A transmitting tag modulates baseband information onto the reflected signal using backscatter modulation. At the receiving tag, the backscattered signal is superimposed to the external excitation and the resulting signal is demodulated using envelope detection techniques. The relative phase difference between the backscatter signal and the external excitation signal at the receiving tag has a large impact on the envelope of the resulting signal. This often causes a complete cancellation of the baseband information contained in the envelope, and it results in a loss of communication between the two tags. This problem is ubiquitous in all BBTT systems and greatly impacts the reliability, robustness, and communication range of such systems. We theoretically analyze and experimentally demonstrate this problem for devices that use both ASK and PSK backscattering. We then present a solution to the problem based on the design of a new backscatter modulator for tags that enables multiphase backscattering. We also propose a new combination method that can further enhance the detection performance of BBTT systems. We examine the performance of the proposed techniques through theoretical analysis, computer simulations, and laboratory experiments with a prototype tag that we have developed.

We investigate the problem of real-time self-tracking of tagged objects in a new system with low-cost “smart” tags. These tiny and battery-less devices will play a pivotal role in the infrastructure of the Internet of Things (IoT). With capabilities of low-power computation and tag-to-tag backscattered communication, no readers will be needed for running the Radio Frequency Identification (RFID) system. In order to allow for low-cost tags, self-tracking has to be performed with simple algorithms while still exhibiting high accuracy. In this paper we propose a linear observation model for which Kalman filtering (KF) is the optimal method. We also consider a nonlinear model for which we apply particle filtering (PF) of reduced complexity as the tracking method. The performance and computational complexity of the different methods are compared by computer simulations.

In this paper we present a novel Radio Frequency Identification (RFID) system for accurate indoor localization. The system is composed of a standard Ultra High Frequency (UHF), ISO-18006C compliant RFID reader, a large set of standard passive RFID tags whose locations are known, and a newly developed tag-like RFID component that is attached to the items that need to be localized. The new semi-passive component, referred to as sensatag (sense-a-tag), has a dual functionality wherein it can sense the communication between the reader and standard tags which are in its proximity, and also communicate with the reader like standard tags using backscatter modulation. Based on the information conveyed by the sensatags to the reader, localization algorithms based on binary sensor principles can be developed. We present results from real measurements that show the accuracy of the proposed system.

In this paper, we present a hardware architecture for a Sampling Importance Resampling Filter (SIRF) applied to systems with multiple interacting models. This filter outperforms traditional filters in practical scenarios due to superior abilities of the SIRFs in dealing with nonlinear and/or non-Gaussian models. Compared to existing approaches, our method does not require knowledge of model transition probabilities and keeps a constant number of particles per model at all times. This allows for a regular hardware structure with deterministic execution time. A highly scalable, parallel architecture consisting of distributed processing elements and a central unit is described. We propose an interconnection scheme and data exchange protocol using the concept of distributed resampling that greatly speeds up filter execution and drastically reduces the required interconnect to a single bus without causing any communication bottleneck. The proposed architecture is evaluated on a Xilinx FPGA platform for a multiple model target tracking application and its efficiency and scalability is shown.