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1. 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))

   null (Ed.)

   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. 

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2. Defense against Black Hole Attacks in Wireless Sensor Network with Anomaly Report Cycling

   Vieira, Marcel; Liu, Hong (May 2024, 2024 International Wireless Communications and Mobile Computing (IWCMC))

   Wireless Sensor Network (WSN) becomes the dominate last-mile connection to cyber-physical systems and Internet-of-Things. However, WSN opens new attack surfaces such as black holes, where sensing information gets lost during relay towards base stations. Current defense mechanisms against black hole attacks require substantial energy consumption, reducing the system's lifetime. This paper proposes a novel approach to detect and recover from black hole attacks using an improved version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy-efficient routing protocol for groups of battery-operated sensor nodes in hierarchy. A round of selection for cluster heads is scheduled in a set time. We propose to improve LEACH with Anomaly Report Cycling (ARC-LEACH), tradeoff between security strength and energy cost. ARC-LEACH absorbs an attack when it occurs by rotating cluster heads to reestablish communication and then sending a message from the base station to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH for stronger resistance to blackhole attacks. ARC-LEACH can provide more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only minor increase in energy consumption. 

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3. On the Accuracy of Network Synchronization Using Persistent Hourglass Clocks

   https://doi.org/10.1145/3362053.3363497  

   Çürük, Eren; Yıldırım, Kasim Sinan; Pawelczak, Przemyslaw; Hester, Josiah (January 2019, Proceedings of the 7th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems)

   Batteryless sensor nodes compute, sense, and communicate using only energy harvested from the ambient. These devices promise long maintenance free operation in hard to deploy scenarios, making them an attractive alternative to battery-powered wireless sensor networks. However, complications from frequent power failures due to unpredictable ambient energy stand in the way of robust network operation. Unlike continuously-powered systems, intermittently-powered batteryless nodes lose their time upon each reboot, along with all volatile memory, making synchronization and coordination difficult. In this paper, we consider the case where each batteryless sensor is equipped with a hourglass capacitor to estimate the elapsed time between power failures. Contrary to prior work that focused on providing a continuous notion of time for a single batteryless sensor, we consider a network of batteryless sensors and explore how to provide a network-wide, continuous, and synchronous notion of time. First, we build a mathematical model that represents the estimated time between power failures by using hourglass capacitors. This allowed us to simulate the local (and continuous) time of a single batteryless node. Second, we show--through simulations--the effect of hourglass capacitors and in turn the performance degradation of the state of the art synchronization protocol in wireless sensor networks in a network of batteryless devices. 

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4. Dispersed passive RF-sensing for 3D structural health monitoring

   https://doi.org/10.52953/ZQYZ8264  

   Ahmad, Abeer; Sha, Xiao; Athalye, Akshay; Das, Samir R.; Caylor, Kelly; Glisic, Branko; Stanacevic, Milutin; Djuric, Petar M. (January 2022, ITU Journal on Future and Evolving Technologies)

   We propose a sensing system comprising a large network of tiny, battery-less, Radio Frequency (RF)-powered sensors that use backscatter communication. The sensors use an entirely passive technique to 'sense' the parameters of the wireless channel between themselves. Since the material properties influence RF channels, this fine-grain sensing can uncover multiple material properties both at a large scale and fine spatial resolution. In this paper, we study the feasibility of the proposed passive technique for monitoring parameters of material in which the sensors are embedded. We performed a set of experiments where the sensor-to-sensor wireless channel parameters are well-defined using physics-based modeling, and we compared the theoretical and experimentally obtained values. For some material parameters of interest, like humidity or strain, the relationship with the observed wireless channel parameters have to be modeled relying on data-driven approaches. The initial experiments show an observable difference in the sensor-to-sensor channel phase with variation in the applied weights. 

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5. Team Assignment for Heterogeneous Multi-Robot Sensor Coverage through Graph Representation Learning

   https://doi.org/10.1109/ICRA48506.2021.9561082  

   Reily, Brian; Zhang, Hao (January 2021, International Conference on Robotics and Automation)

   Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an area. Robots also exhibit multiple forms of relationships (e.g., communication connections or spatial distribution) that need to be considered when assigning robot teams for sensor coverage. To address this problem, in this paper we introduce a novel formulation of sensor coverage by multi-robot systems with heterogeneous relationships as a graph representation learning problem. We propose a principled approach based on the mathematical framework of regularized optimization to learn a unified representation of the multi-robot system from the graphs describing the heterogeneous relationships and to identify the learned representation’s underlying structure in order to assign the robots to teams. To evaluate the proposed approach, we conduct extensive experiments on simulated multi-robot systems and a physical multi-robot system as a case study, demonstrating that our approach is able to effectively assign teams for heterogeneous multi-robot sensor coverage. 

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