More Like this

1. Towards scalable soft e-skin: Flexible event-based tactile-sensors using wireless sensor elements embedded in soft elastomer

   https://doi.org/10.1109/BioRob49111.2020.9224353  

   Slepyan, Ariel ; Thakor, Nitish ( November 2020 , IEEE BIOROB Conference)

   null (Ed.)

   Scalable, high-density electronic skins (e-skins) are a desirable goal of tactile sensing. However, a realization of this goal has been elusive due to the trade-off between spatial and temporal resolution that current tactile sensors suffer from. Additionally, as tactile sensing grids become large, wiring becomes unmanageable, and there is a need for a wireless approach. In this work, a scalable, event-based, passive tactilesensing system is proposed that is based on radio-frequency identification (RFID) technology. An RFID-based tactile sensing hand is developed with 19 pressure sensing taxels. The taxels are read wirelessly using a single ‘hand-shaped’ RFID antenna. Each RFID tag is transformed into a pressure sensor by disconnecting the RFID chip from its antenna and embedding the chip and antenna into soft elastomer with an air gap introduced between the RFID chip and its antenna. When a pressure event occurs, the RFID chip contacts its antenna and receives power and communicates with the RFID reader. Thus, the sensor is transformed into a biomimetic event-based sensor, whose response is activated only when used. Further, this work demonstrates the feasibility of constructing event-based, passive sensing grids that can be read wirelessly. Future tactile sensing e-skins can utilize this approach to become scalable and dense, while retaining high temporal resolution. Moreover, this approach can be applied beyond tactile sensing, for the development of scalable and high-density sensors of any modality. 

   more » « less
2. mReader: Concurrent UHF RFID Tag Reading

   https://doi.org/10.1145/3565287.3610256  

   Pirayesh, Hossein ; Zhang, Shichen ; Zeng, Huacheng ( October 2023 , ACM MOBIHOC 2023)

   UHF RFID tags have been widely used for contactless inventory and tracking applications. One fundamental problem with RFID readers is their limited tag reading rate. Existing RFID readers (e.g., Impinj Speedway) can read about 35 tags per second in a read zone, which is far from enough for many applications. In this paper, we present the first-of-its-kind RFID reader (mReader), which borrows the idea of multi-user MIMO (MU-MIMO) from cellular networks to enable concurrent multi-tag reading in passive RFID systems. mReader is equipped with multiple antennas for implicit beamforming in downlink transmissions. It is enabled by three key techniques: uplink collision recovery, transition-based channel estimation, and zero-overhead channel calibration. In addition, mReader employs a Q-value adaptation algorithm for medium access control to maximize its tag reading rate. We have built a prototype of mReader on USRP X310 and demonstrated for the first time that a two-antenna reader can read two commercial off-the-shelf (COTS) tags simultaneously. Numerical results further show that mReader can improve the tag reading rate by 45% compared to existing RFID readers. 

   more » « less
3. RTSense: passive RFID based temperature sensing

   https://doi.org/https://doi.org/10.1145/3384419.3430729  

   Swadhin Pradhan, Lili Qiu ( November 2020 , ACM SenSys)

   null (Ed.)

   Passive radio-frequency identification (RFID) tags are attractive because they are low cost, battery-free, and easy to deploy. This technology is traditionally being used to identify tags attached to the objects. In this paper, we explore the feasibility of turning passive RFID tags into battery-free temperature sensors. The impedance of the RFID tag changes with the temperature and this change will be manifested in the reflected signal from the tag. This opens up an opportunity to realize battery-free temperature sensing using a passive RFID tag with already deployed Commercial Off-the-Shelf (COTS) RFID reader-antenna infrastructure in supply chain management or inventory tracking. However, it is challenging to achieve high accuracy and robustness against the changes in the environment. To address these challenges, we first develop a detailed analytical model to capture the impact of temperature change on the tag impedance and the resulting phase of the reflected signal. We then build a system that uses a pair of tags, which respond differently to the temperature change to cancel out other environmental impacts. Using extensive evaluation, we show our model is accurate and our system can estimate the temperature within a 2.9 degree centigrade median error and support a normal read range of 3.5 m in an environment-independent manner. 

   more » « less
4. Reinforcement Learning for RFID Localization

   https://doi.org/10.1109/RFID62091.2024.10582639  

   Chen, Weitung ; Boroushaki, Tara ; Perper, Isaac ; Adib, Fadel ( June 2024 , IEEE)

   We present RL2, a robotic system for efficient and accurate localization of UHF RFID tags. In contrast to past robotic RFID localization systems, which have mostly focused on location accuracy, RL2 learns how to jointly optimize the accuracy and speed of localization. To do so, it introduces a reinforcement learning-based (RL) trajectory optimization network that learns the next best trajectory for a robot-mounted reader antenna. Our algorithm encodes the aperture length and location confidence (using a synthetic-aperture-radar formulation) from multiple RFID tags into the state observations and uses them to learn the optimal trajectory. We built an end-to-end prototype of RL2 with an antenna moving on a ceiling-mounted 2D robotic track. We evaluated RL2 and demonstrated that with the median 3D localization accuracy of 0.55m, it locates multiple RFID tags 2.13x faster compared to a baseline strategy. Our results show the potential for RL-based RFID localization to enhance the efficiency of RFID inventory processes in areas spanning manufacturing, retail, and logistics. 

   more » « less
5. UHF RFID tag localization using pattern reconfigurable reader antenna

   https://doi.org/10.1109/WAMICON53991.2022.9786069  

   Hossain, Md Shakir ; Tajin, Md Abu ; Dandekar, Kapil R. ( April 2022 , 2022 IEEE 22nd Annual Wireless and Microwave Technology Conference (WAMICON))

   Passive ultra high frequency (UHF) radio frequency identification (RFID) tags have the potential to find ubiquitous use in indoor object tracking, localization, and contact tracing. We propose a machine learning-based method for RFID indoor localization using a pattern reconfigurable UHF RFID reader antenna array. The received signal strength indicator (RSSI) values (from 10,000 tags) recorded at the reader antenna units are used as features to evaluate the machine learning models with a train-test split of 75%-25%. The training and testing data is generated by a wireless ray tracing simulator. Five machine learning models: random forest regressor, decision tree regressor, Nu support vector regressor, k nearest regressor, and kernel ridge regressor are compared. Random forest regressor has the lowest localization error both in terms of average Euclidean distance (AED) and root-mean-square error (RMSE). For random forest regressor, localization error results show that 90% of the tags are within 1 meter of their true position, and 67% are within 50 cm of their true position based on Euclidean distance. 

   more » « less