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1. Multi-hop routing protocols for RFID systems with tag-to-tag communication

   https://doi.org/10.1109/MILCOM.2017.8170764  

   Liu, Chang ; Haas, Zygmunt J. ( October 2017 , IEEE Military Communications Conference)

   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 onmore »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.« less
2. Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks

   Ahmed Manavi Alam*, Ali Cafer ( January 2022 , RFI Workshop 2022)

   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 1Bmore »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.« less
3. Group Evacuation on a Line by Agents with Different Communication Abilities

   Czyzowicz, J ; Killick R. ; Kranakis, E. ; Krizanc, D. ; Narayanan, L. ; Opatrny, J. ; Pankratov, D. ; Shende, S. ( January 2021 , 32nd International Symposium on Algorithms and Computation (ISAAC 2021))

   We consider evacuation of a group of n ≥ 2 autonomous mobile agents (or robots) from an unknown exit on an infinite line. The agents are initially placed at the origin of the line and can move with any speed up to the maximum speed 1 in any direction they wish and they all can communicate when they are co-located. However, the agents have different wireless communication abilities: while some are fully wireless and can send and receive messages at any distance, a subset of the agents are senders, they can only transmit messages wirelessly, and the rest are receivers, they can only receive messages wirelessly. The agents start at the same time and their communication abilities are known to each other from the start. Starting at the origin of the line, the goal of the agents is to collectively find a target/exit at an unknown location on the line while minimizing the evacuation time, defined as the time when the last agent reaches the target. We investigate the impact of such a mixed communication model on evacuation time on an infinite line for a group of cooperating agents. In particular, we provide evacuation algorithms and analyze the resulting competitivemore »ratio (CR) of the evacuation time for such a group of agents. If the group has two agents of two different types, we give an optimal evacuation algorithm with competitive ratio CR = 3+2√2. If there is a single sender or fully wireless agent, and multiple receivers we prove that CR ∈ [2+√5,5], and if there are multiple senders and a single receiver or fully wireless agent, we show that CR ∈ [3,5.681319]. Any group consisting of only senders or only receivers requires competitive ratio 9, and any other combination of agents has competitive ratio 3.« less
4. Receiver Selectivity Limits on Bistatic Backscatter Range

   https://doi.org/10.1109/RFID49298.2020.9244826  

   Katanbaf, Mohamad ; Saffari, Ali ; Smith, Joshua R. ( September 2020 , 2020 IEEE International Conference on RFID (RFID))

   Backscatter communication has been a popular choice in low-power/battery-free sensor nodes development. However, the effect of RF source to receiver distance on the operating range of this communication system has not been modeled accurately. In this paper, we propose a model for a bistatic backscatter system coverage map based on the receiver selectivity, receiver sensitivity, and geometric placement of the receiver, RF source, and the tag. To verify our proposed model and simulations, we perform an experiment using a low-cost commercial BLE receiver and a custom-designed BLE backscatter tag. We also show that the receiver selectivity might depend on the interference level, and present measurement results to signify how this dependence relates the system bit error rate to the RF excitation power.
5. Leveraging RF Power for Intelligent Tag Networks

   https://doi.org/10.1145/3194554.3194621  

   Salman, Emre ; Stanaćević, Milutin ; Das, Samir ; Djurić, Petar M. ( May 2018 , Proceedings of the Great Lakes Symposium on VLSI)

   A novel framework and related methodologies are described to leverage RF power for building intelligent and battery-free devices with communication and computation capabilities. These passive devices are envisioned to make significant impact for the popular vision of smart dust due to extreme low power operation. The communication framework relies on tag-to-tag backscattering with very limited energy resources. The computing framework relies on a novel AC computing methodology that facilitates local data processing with an order of magnitude less power consumption. These enabling technologies, as described in this paper, revitalize the concept of smart dust with significant impact on various application domains such as smart spaces, implantable devices, and environmental/structural monitoring.