Radio frequency identification (RFID) is a technology for automated identification of objects and people. RFID technology is expected to find extensive use in applications related to the Internet of Things, and in particular applications of Internet of Battlefield Things. Of particular interest are passive RFID tags due to a number of their salient advantages. Such tags, lacking energy sources of their own, use backscattering of the power of an RF source (a reader) to communicate. Recently, passive RFID tag-to-tag (T2T) communication has been demonstrated, via which tags can directly communicate with each other and share information. This opens the possibility of building a Network of Tags (NeTa), in which the passive tags communicate among themselves to perform data processing functions. Among possible applications of NeTa are monitoring services in hard-to-reach locations. As an essential step toward implementation of NeTa, we consider a novel multi-hop network architecture; in particular, with the proposed novel turbo backscattering operation, inter-tag distances can be significantly increased. Due to the interference among tags’ transmissions, one of the main technical challenges of implementing such the NeTa architecture is the routing protocol design. In this paper, we introduce a design of a routing protocol, which is based on a solution of a non-linear binary optimization problem. We study the performance of the proposed protocol and investigate impacts of several network factors, such as the tag density and the transmit power of the reader.
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Backscatter Communications with Passive Receivers: From Fundamentals to Applications
The principle of backscattering has the potential to enable a full realization of the Internet of Things. This paradigm subsumes massively deployed things that have the capability to communicate directly with each other. Based on the types of excitation and receivers, we discriminate four types of backscattering systems: (i) Dedicated Exciter Active Receiver systems, (ii) Ambient Exciter Active Receiver systems, (iii) Dedicated Exciter Passive Receiver systems, and (iv) Ambient Exciter Passive Receiver systems. In this paper, we present an overview of bacskscattering systems with passive receivers which form the foundation for Backscattering Tag-to-Tag Networks (BTTNs). This is a technology that allows tiny batteryless RF tags attached to various objects to communicate directly with each other and to perform RF-based sensing of the communication link. We present an overview of recent innovations in hardware architectures for backscatter modulation, passive demodulation, and energy harvesting that overcome design challenges for passive tag-to-tag communication. We further describe the challenges in scaling up the architecture from a single link to a distributed network. We provide some examples of application scenarios enabled by BTTNs involving object-to-object communication and inter-object or human-object dynamic interactions. Finally, we discuss key challenges in present-day BTTN technology and future research directions.
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- Award ID(s):
- 1763627
- NSF-PAR ID:
- 10280100
- Date Published:
- Journal Name:
- ITU Journal
- Volume:
- 1
- Issue:
- 1
- ISSN:
- 2616-8375
- Page Range / eLocation ID:
- 1 - 11
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for both parties. RFI detection and mitigation will remain a prerequisite for these radiometers and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance.more » « less
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Abstract The D‐region ionosphere (60
90 km) plays an important role in long‐range communication and response to solar and space weather; however, it is difficult to directly measure with currently available technology. Very low frequency (VLF) radio remote sensing is one of the more promising approaches, using the efficient reflection of VLF waves from the D‐region. A number of VLF beacons can therefore be turned into diagnostic tools. VLF remote sensing techniques are useful and can provide global coverage, but in practice have been applied to a limited area and often on only a small number of days. In this work, we expand the use of a recently introduced machine learning based approach (Gross & Cohen, 2020, https://doi.org/10.1029/2019JA027135 ) to observe and model the D‐region electron density using VLF transmitting beacons and receivers. We have extended the model to cover nighttime in addition to daytime, and have applied it to track D‐region waveguide parameters, h’ and, over 400 daytimes and 150 nighttimes on up to 21 transmitter‐receiver paths across the continental US. Using an exponential fit, h’ represents the height of the ionosphere and represents the slope of the electron density. Using this data set, we quantify diurnal, daily and seasonal variations of the D‐region ionosphere for both daytime and nighttime D‐region ionosphere. We show that our model identifies expected variations, as well as producing results in line with other previous studies. Additionally, we show that our daytime predictions exhibit a larger autocorrelation at higher time lags than our nighttime predictions, indicating a model with persistence may perform better.
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Accepted Manuscript1.0
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- The DOI is not currently available.
