Design and Development of Backscatter-based Tag to Tag Networks

Background and Project Goals

The project has previously developed a prototype RF tag platform called RIBBN (Research Infrastructure for Backscatter-Based Networks) - pronounced as 'ribbon.' RIBBN is a tag plaform that uses the principle of backscatterd communication (similar to RFID), but there is no requirement of RFID readers or equivalent devices. They are powered by RF signals coming from 'exciters' specifically deployed for this purpose or ambient RF signals if present. The tags have the ability to communicate among themselves using completely passive backscatter modulation. We have developed a modular/extensible, programmable and powerful platform that will drive the future of IoT (Internet of Things).

In recently funded current effort (collaboration between Stony Brook and UT-Dallas) we are using new generations of the RIBBN tags to drive fundamental research in tag-to-tag networking paradigm, where RF tags are able to network among themselves and are able to operate in varying degrees of RF power availability with adaptive abilities. The goal is to form the foundational technology for smart spaces enabling object identification and tracking, understanding inter-object interactions and associations, sensing and distributed processing of information to support futuristic higher level applications.

We expect to make the following contributions:  1) Architectural innovations: We plan to develop an adaptive energy- aware tag architecture that will allow for high efficiency of energy harvesting over a wide range of incident RF power along with increased sensitivity and robustness of received signal demodulation in tag-to-tag link. 2) Detecting interactions and events: Measurement of channel amplitude and phase in a passive receiver tag will be enabled with novel backscatter modulation schemes and receiver architectures. This will empower the the tag network with the ability to detect dynamic interactions between tags as well as dynamic events in the environment around the tags. 3) Routing and information processing: We will develop scalable and adaptable distributed multi-hop routing for diverse topologies through novel hybrid routing approaches. We will also develop novel solutions for making inference under stressful conditions that include poor signal quality, lack of time-space coordinates, intrinsic asynchronism in receiving data, and limited computational and memory capabilities of the tags. 4) Demonstration and evaluation: A set of tags will be implemented in ASIC that will embody our research results. Few application scenarios in a realistic smart home-like setup, created by interactions between a number of objects tagged with developed prototypes, will be performed to demonstrate the robustness of the developed algorithms for network-wide inferencing.

RIBBN Overview

RIBBN overview

Pictures of RIBBN Lab and RIBBN Platform at CEWIT Labs, Stony Brook (Circa 2015-17)

    RIBBN Lab set up         RIBBN close up

Project Members

Principal Investigators
Samir R Das, Professor, CS Dept, Stony Brook University
Petar Djuric, Professor, ECE Dept, Stony Brook University
Miltutin Stanacevic, Assoc. Professor, ECE Dept, Stony Brook University
Akshay Athalye, Research Scientist, ECE Dept, Stony Brook University
Zygmunt Haas, CS Dept, UT Dallas

Graduate Students/Postdocs
Jinghui Jian , PhD graduate, ECE Dept, Stony Brook University, graduated 2015
Zhe Shen, PhD graduate, ECE Dept, Stony Brook University, graduated 2014
Jihoon Ryoo, PhD graduate, CS Dept, Stony Brook University, graduated 2017
Yasha Karimi, PhD graduate, ECE Dept, Stony Brook University graduated 2019
Abeer Ahmad, PhD student, CS Department, Stony Brook University
Yuanfei Huang, PhD student, ECE Department, Stony Brook University
Chang Liu, Postdoctoral scholar, CS department, UT-Dallas
Hung Yu Chen, Exchange Ph.D. student, CS department, UT-Dallas
Zijing Tian, Ph.D. student, CS department, UT-Dallas

Project Activities and Outcomes

The project has made advances in designing RF backscatter tag to tag link where both backscatter modulator and backscatter receiver are passive devices. An external RF signal from an exciter provides both the power as well as the carrier for backscatter. This is a significant departure from conventional RFID where one end of the link uses a powerful active device (reader). The external RF signal could be an ambient RF signal if it can provide high enough power to the tags. Otherwise, intentionally deployed exciters need to be used (more common case). The project has so far developed tag prototypes and evaluated them experimentally. The project also developed multiple innovative ideas related to tag-to-tag backscattering.

Tag Prototyping

A prototype RIBBN tag design has been completed and its performance has been evaluated. The design follows a modular approach. The excitation signal is derived form an external continuous wave (CW) signal generator operating at 915MHz. We also have tested the tags with SDR generated TV signal in this band. Much of the design and related analysis are covered in publications [1-5] below. One specific challenge we face in this design is to be able to demodulate the backscatter signals with very low modulation index using entirely passive techniques.

The major modules implemented on PCB using discrete components are below.

1. Receive Section: This module consists of a matching circuit designed to maximize power transfer, a passive envelope detector, an RC filter to cut off unwanted frequencies, leaving only baseband signal, and a comparator to digitize the analog signal. The detector is built using a multi-stage diode detector circuit. The goal is to optimize the ability of the detector to reliably detect backscatter signals from other tags in the network in the presence of external excitation signal. Various analysis and advanced ideas are discussed in publications [1,3] below.  

2. Backscatter Modulator Section: We constructed a backscatter modulator module using an RF SPDT switch and associated circuitry so that the switch can change the antenna load impedances thus varying its reflection coefficient. The choice of right load impedance levels is critical for avoiding the phase cancellation problem [4] that arises in tag-to-tag backscatter networks. Phase cancellation happens when the excitation signal and backscattered signal combine in opposite phase to produce signal nulls at the receiving tags making it very hard for a passive detection circuit to detect the backscatter. This is addressed using a technique called multiphase backscatter where the tags have the ability to change the phase of the backscattered signal by changing the antenna load impedance [2,4]. Availability of such phase diversity can help in other applications too, a topic of our current work.  

3. Digital Section: We implemented the digital section module using a low-power microcontroller. The microcontroller reads and decodes the digitized signal from the Receive Section and drives the RF switch for the Modulator Section. It also performs the necessary link and network layer functions. The microcontroller is generally in a low-power idle mode while tags are in listen mode. It wakes up only when a pilot tone is received that is inserted as a preamble to each frame. 

Prototype Implementation:

The first generation prototypes are made of 2-layer FR4 PCB in thickness of 31 mils with components on both sides. The PCB was designed using Altium designer software and was manufactured by Goldphoenix Printed Circuit Board Company in Wuhan, China. The antenna is implemented directly on a separate piece of PCB that is attached to the main PCB using SMA connector. This modular design helps future experiments with different types of antennas. See the prototype in the platform figure above. The design is available by contacting the project PIs.

Power Consumption: This depends on three components on the tag - comparator in the receive section, switch in the modulator section, and the microcontroller. Discounting the microcontroller (as is the practice in related literature), the power consumption is roughly 140 microwatts, primarily due to the comparator. While lower power comparators are possible (below 1 microwatt) with limited design changes, this will also limit the design in terms of data rate and range.  

Tag Performance

1. We are able to demonstrate tag-to-tag links up to about 3 m at 5Kbps when the available excitation power is about -20 dBm. We get a significantly better range (up to about 10 m though with some gaps) at the same rate when the power increases to about -15dBm. 

2. We are able to demonstrate multihop operation for up to 4 hops in a static routing set up, thus enabling tag-to-tag communication over about 12 m at 5 Kbps with only -20 dBm available excitation power. See [2].

3. Our work in [1] has demonstrated that simulated tags implemented using 45nm CMOS technology are able to demodulate ASK backscatters with very low modulation index (0.6%) consuming only 1.2 microwatts at a data rate of 10Kbps.

Addressing Phase Cancellation

The phase cancellation problem occurs at the receiving tag and results from the superposition of the backscatter signal from the transmit tag and the external excitation signal used for backscattering. These signals could combine in opposite phases depending on the nature of the multipath channel and tag/exciter locations leading to signal cancellation and zero or very poor modulation index [4,5]. While there are various avenues to address this, we have explored use of phase diversity at the backscatter modulator as a practical mechanism.

This is done by using a multi-port backscatter modulator that provides the tag the ability to backscatter in one of several phase channels. Each channel is characterized by a different reflection coefficient used for backscatter, and adjacent channels are separated by a fixed and deterministic phase differences. The basic idea is to have the ability to transmit a message over multiple phase channels available on the tag. Then, at the receiving tag, even if the message is canceled or severely attenuated in one channel, it will still be detected in another channel. Choosing the appropriate values for the reflection coefficient and hence the load impedances for the different phase channels is an important step in the implementation of the proposed scheme. We developed a systematic methodology for doing this and developed prototype tags capable of backscattering in up to 2 phase channels (a larger tag with more channel options is in the works). Experimental results  and demonstration of how this diversity actually improves data communication performance is available in [2].

Characterizing Backscatter Channel using Multiphase Backscatter

The amplitudes of the received signal at different phase channels could be used to characterize the wireless backscatter channel between the Tx and Rx tags. Changes in the channel due to change the surroundings or movement of tags also change this amplitude. Now, if a sizable number of phase channels are used (say, 4 to 8) the vector of these amplitudes could be considered as a fingerprint of the channel. This fingerprint (called Backscatter Channel State Information or BCSI) changes in a predictable fashion due to specific changes in the surroundings. This property makes BCSI an effective tool to recognize human activities around the tags. This observation is very powerful. In essence, BCSI provides a similar power to measure wireless channels and use it for activity recognition as regular CSI-based activity recognition that has been popular in recent literature. But the latter must use active, high power radios. Using BCSI we can do the same using passive RF tags. The overall vision is explained in our work in [6]. In the early experimental results reported in [6]  with 9 participants and 8 activities of daily living, the average error for activity recognition was only 6%.

Passive Wireless Channel Estimation

We develop passive RF tags with the ability to estimate the RF parameters of the wireless channel between pairs of communicating tags. This ability is the key to detect interactions and events. With such capability, the network of RF tags can provide a real-time, precise, fine-grained RF fingerprint of the environment. As the tags continuously sense the parameters of the RF channel (amplitude and phase) between neighboring tags, they can detect and classify activities in their environment. We have previously reported this capability in our work in ACM Mobisys 2018 [6] and IEEE ICASSP 2019 [7].More refined techniques for isolating the tag-to-tag channel has been recently developed in our work in ACM DFHS Workshop 2019 (with ACM Sensys/Buildsys) [16].

Sampling the received baseband signal at different reflecting phases at the backscattering (Tx) 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 backscattering Tx tag switches between a set of terminating impedances that represent different reflecting phases in order to compute amplitude and phase of the tag-to-tag channel. The channel estimator of Rx tag measures the amplitude of the baseband signal at each reflecting phase to determine the amplitude and phase of the tag-to-tag channel. The technical details of this available in our ACM Mobisys 2018 [6] and IEEE ICASSP 2019 [7] papers. The quantification of the baseband signal is challenging due to the low modulation index of the received RF signal and low sensitivity of envelope detector, as well as wide range of the possible input RF powers and modulation indexes.

We developed the architecture and circuit implementation of the channel estimator such that it is able to operate in a very low power budget. The expected power budget of our tags is in the order of 100s of nW. This enables the RF tag  operate with the targeted input RF power of -25 dBm. The detailed description of the design is available in our paper in IEEE ISCAS 2019 [8].

We have designed and implemented the channel estimator in 65 nm CMOS technology, has sensitivity of -45 dBm at 2.5% modulation index and consumes 122 nW. The circuit simulation results are available in the IEEE ISCAS 2019 paper [8].

Doppler Shift Measurements

We used the channel characterization approach above for Doppler shift measurements to be done entirely on the passive tags. While such measurements have been shown to be possible on self-powered RF tags, previous work used regular RFID tags and such measurements have been done on active readers. We are now able to do the same without the involvement of any active device. The method exploits the concept of multiphase probing and, from it, inferring information about changing distances between two communicating tags. We show that we can measure Doppler shifts on passive tags almost as accurately as with costly conventional RFID readers. With our experiments, we demonstrate that with two tags in an office environment the median tracking error is about 2.5cm. This is comparable to previous work that requires the use of RFID readers. The modeling approach and experimental results are reported in IEEE ICASSP 2020 [13].

Receiver Circuit Implementation

The resolution of the channel estimation is limited by the resolution of the envelope detector based receiver. The main challenge in the design of the receiver is resolving the weak received reflected signal that is superimposed on the continuous wave signal directly received from the excitation source under stringent power limit. We have explored an architecture of the receiver that includes an active amplification stage in the envelope detector. We have demonstrated that a novel self- biased common-source based envelope detector provides sufficient conversion gain and at the same time operates with a low power consumption. We have also studied the feasibility of application of the tag-to-tag link for the communication between free floating mm-sized brain implantable devices, as well as the use of the proposed demodulator in this application case. The feasibility study is reported in the IEEE UEMCON 2019 paper [14]. The circuit details and simulation results of the proposed modulator are reported in the IEEE ISCAS 2020 paper [15].

Routing, MAC, and Scheduling in RIBBN (at UT-Dallas)

Specific Objectives: In the first year of our work on this project, we have concentrated on following objectives of RIBBN: (1) design of scheduling algorithms RIBBN with different hardware capabilities of the tags; (2) design of a distributed MAC protocol for RIBBN, and (3) design of an algorithm for energy transfer protocol for RIBBN.

Significant Results: To unleash the potential of RIBBN communication, we proposed a novel backscattering operation, which allows implementation of a multihop RIBBN, improving the network coverage and the distances of the communicating tags by orders of magnitude. Due to the asymmetric communication links and interference among tags’ transmissions in a RIBBN, the routing protocol design has become one of the main technical challenges. In this work, we designed different RIBBN routing schemes for three distinct types of tags with different hardware capabilities. We evaluated the performances of the proposed protocols, and we study the impacts of several network parameters on the network capacity. We also compare the proposed routing protocols, as to investigate the performance gains due to the tags’ different hardware capabilities.

Other achievements: Due to the asymmetry of communication links in RIBBN, existing routing protocols cannot be used. To address this shortcoming, we proposed a basic protocol to identify tags and their multiple-hop uplink routing paths. Then we considered three types of passive RFID tags with different capabilities (i.e., received power measurements and transmission power attenuation), we proposed three corresponding routing protocols to schedule transmissions, as to maximize the network throughput. Computer simulations verified the effectiveness of the proposed protocols in improving the concurrency of the transmissions (i.e., the throughput) due to the added tag capabilities. As one example of the results of our study, we concluded that power measurement capability combined with transmission attenuation of the tags can significantly improve the overall network throughput especially for adaptable network topologies.

Broader Impacts

Educational Contributions: The project has provided a training ground for multiple PhD students at Stony Brook (4 from ECE and 2 from CS, so far). Several of them now graduated and found R&D level employment in high tech industry or academia. The project results have been communicated to journals and conferences, listed below. The project helped develop a laboratory for experimenting with backscattering technology in general. A high school student (Shane Kim) worked in the lab on summers and his work led to poster and workshop papers related to human activity recognition using RF signals. This experience benefited him greatly and contributed to his college applications.

At UTD, this time, the project allows 2 students and one post-doc to continue their professional development. There is a plan to add a new Ph.D. student to the research group who will be working on portions of the project agenda.

Project Impact: This project (the contributions of UTD) is expected to make the following contributions to the passive network of tags (RIBBN) for smart spaces: (a) Architectural innovations: We will develop an adaptive energy aware tag architecture that will allow for high efficiency of energy harvesting; (b) Routing and information processing: We will develop scalable and adaptable distributed multi-hop routing for diverse RIBBN -specific topologies through novel hybrid routing approaches. We will also develop novel solutions for coping with inference in tag-to-tag communications; (c) Demonstration and evaluation of RIBBN; (d) We will apply our results to application scenarios in a realistic smart home-like setup, created by interactions between a number of objects tagged with developed prototypes to demonstrate the robustness of the developed algorithms for network-wide inferencing.

Additional Broader Impacts: The development of the methodology and algorithms for optimization of routing could be used in other disciplines where route optimization is required. The development of efficient algorithms for finding Distributed Independent Set in graphs is likely to be used in numerous other fields and disciplines, as Independent Set in graphs is a generic problem that occurs in many studies. The development of energy harvesting techniques for passive tag networks will be applicable to sensor networks, especially those which are embedded in environments where battery-powering is prohibitive.

Impact on Resources: Similarly to the "physical resources that form infrastructure," we envision that objects in working (institutional) spaces will be tagged with miniature battery-less RF-powered tags, which can autonomously interact among themselves and the environment around them and communicate over low-power wireless links without the need for any centralized control. These interactions will enable "Smart Office Spaces" wherein objects can collaboratively perceive the surrounding spaces and recognize other objects, their relationships, dynamic activities and events therein. Due to their exceptionally low-power design the tags do not have an on-board radio transmitter and communicate via backscattering the same RF signal that powers them.

Related Courses

CSE570 Wireless and Mobile Networks (Stony Brook)
CS6301 Wireless Networks (UTD)

Reports & Publications

Core reports/papers:
  1. Yasha Karimi, Akshay Athalye, Samir R. Das and Petar Djuric, Design of a Backscatter Tag-to-Tag System, Proc. IEEE Int. Cong. on RFID (IEEE RFID), 2017, pp 6-12.
  2. Jihoon Ryoo, Jinghui Jian, Akshay Athalye and Samir R. Das, "Design and Evaluation of ‘BTTN’ -- A Backscattering Tag-to-Tag Network,” IEEE Internet of Things Journal, 5(4), 2018, pp 2844-2855.
  3. Akshay Athalye, Jinghui Jian, Yasha Karimi, Samir R. Das, and Petar M. Djuric " Analog Front End Design for Tags in Backscatter- Based Tag-to-Tag Communication Networks," In Proceedings of IEEE Int. Symp. on Circuits and Systems (ISCAS '16), Montreal, Canada, May 2016.
  4. Zhe Shen, Akshay Athalye, and Petar M. Djuric. "Phase Cancellation in Backscastter Based Tag-to-Tag Communication Systems," IEEE Internet of Things Journal. , Vol 3(6), 2016
  5. Jinghui Jian, " EM Field Analysis for Phase Cancellation in Backscattering Tag to Tag Systems," Wings Lab Technical Report Dec 2015.
  6. Jihoon Ryoo, Yasha Karimi, Akshay Athalye, Milutin Stanacevic, Samir R. Das, and Petar Djuric. 2018. BARNET: Towards Activity Recognition Using Passive Backscattering Tag-to-Tag Network. In Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '18), 414-427.
  7. M. Stanaćević, Y. Karimi, G. Feng, J. Ryoo, A. Athalye, S. Das, P. M. Djurić, RF-based Analytics Generated by Tag-to-tag Networks, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019.
  8. Yasha Karimi, Yuanfei Huang, Akshay Athalye, Samir Das, Petar Djurić, Milutin Stanaćević, Passive Wireless Channel Estimation in RF Tag Network, IEEE International Symposium on Circuits and Systems (ISCAS), 2019.
  9. Z.J. Haas and C. Liu, “"On the Design of Multi-Hop Tag-to-Tag Routing Protocol for Networks of Passive Tags,” submitted for publication.
  10. Z.J. Haas and H.Y. Chen, “"Efficient Distributed Algorithm for Maximum Independent Set,” submitted for publication.
  11. C. Liu and Z.J. Haas, “Multi-hop Routing Protocols for RFID Systems with Tag-to-Tag Communication,” 36th IEEE Military Communications Conference, Baltimore, MD, October 23-25, 2017.
  12. C. Liu and Z.J. Haas, “Routing Protocol Design in Tag-to-Tag Networks with Capability-enhanced Passive Tags,” IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Montreal, QC, Canada, October 8-13, 2017.
  13. Ahmad, Abeer and Huang, Yuanfei and Sha, Xiao and Athalye, Akshay and Stanacevic, Milutin and Das, Samir R. and Djuric, Petar M. (2020). On Measuring Doppler Shifts between Tags in a Backscattering Tag-to-Tag Network with Applications in Tracking. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  14. Sha, Xiao and Karimi, Yasha and Das, Samir R. and Djuric, Petar and Stanacevic, Milutin. (2019). Study of mm-sized Coil to Coil Backscatter Based Communication Link. IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).
  15. X. Sha, Y. Huang, T. Wan, Y. Karimi, S.R. Das, P. Djuric, M. Stanacevic, A Self-Biased Low Modulation Index ASK Demodulator for Implantable Devices, 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Oct 2020.
  16. Ahmad, Abeer and Athalye, Akshay and Stanacevic, Milutin and Das, Samir R. (2019) Collaborative Channel Estimation in Backscattering Tag-to-Tag Networks. DFHS'19: Proceedings of the 1st ACM International Workshop on Device-Free Human Sensing.

Other related papers:
  1. M. Dowling,  M. F. Bugallo, S. R. Das, P M. Djuric. 2018. “Tracking of Objects in a Passive Backscattering Tag-to-Tag Network.” 2018 IEEE 19th. International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018.
  2. Emre Salman, Milutin Stanacevic, Samir Das, and Petar M. Djuric. 2018. Leveraging RF Power for Intelligent Tag Networks. In Proceedings of the 2018 on Great Lakes Symposium on VLSI (GLSVLSI '18). ACM, New York, NY, USA, 329-334.
  3. Jihoon Ryoo and Samir R. Das. "Phase-based Ranging of RFID Tags with Applications to Shopping Cart Localization," In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '15), pp 245-249.
  4. Davide Dardari, Pau Closas, and Petar M. Djuric. "Indoor Tracking: Theory, Methods, and Technologies" IEEE Trans. on Vehicular Technology, 64.4 (2015): 1263-1278.
  5. M. Bolic, M. Rostamian, and P. M. Djuric, Proximity detection with RFID: A Step Toward the Internet of  Things, IEEE Pervasive Computing, Issue No.02 - Apr.-June (2015 vol.14) pp: 70-76. 
  6.  L. Geng, M. F. Bugallo, A. Athalye, and P. M. Djuric, Indoor tracking with RFID systems, IEEE  Journal of Selected Topics in Signal Processing, vol. 8(1), pp. 96-105, 2014.
  7. Z. Weng and P. M. Djuri'c, Consensus for continuous belief functions, Proceedings of the  European Signal Processing Conference, Lisbon, Portugal, 2014.
  8. Patent Application: “WAVEFORM DESIGN FOR RF POWER TRANSFER,” UNITED STATES Patent Application, Filing Date: 04/23/2018.


NSF logoNational Science Foundation