@proceedings {963,
	title = {Bridging Indoor RFID Localization and Long-Range Sensing: Exploring Energy-Efficient Backscatter Positioning},
	year = {2025},
	month = {03/2025},
	publisher = {HamSCI},
	address = {Newark, NJ},
	abstract = {<p>The evolution of backscattering communications has significantly advanced RFID-based localization, particularly in scenarios where cost-effectiveness and low power consumption in harsh environments are critical. Originally introduced during World War II with the concept of corner reflectors, backscatter-based localization has since progressed into ubiquitous indoor tracking and Internet of Things (IoT) applications. However, interference, energy harvesting limitations, and communication range constraints limit its full potential. Recent advancements in RFID-based localization have focused on techniques that optimize power allocation, extend range, and mitigate interference. One approach involves integrating sensing and communication (ISAC) systems with RFID, where Zero-Forcing and Convex Optimization minimize interference while enhancing power allocation. However, as tags move closer to user antennas, interference challenges remain. Another key development in backscattering communications has demonstrated long-range RFID localization by decoupling the carrier frequency from the receiver, achieving distances up to 3.4 km at 868 MHz with only 70 μW power consumption. However, this technique introduced unintended interference with nearby communication bands. More recent methods have addressed this issue by employing frequency shift keying (FSK) modulation, significantly extending reader-tag distances. Building upon these insights, we propose an auxiliary approach: integrating energy-harvesting receiving antennas within RFID localization networks, transforming them into self-powered sensor nodes. This approach opens opportunities for radio applications, where distributed low-power RFID beacons could support experimental localization networks. With backscatter localization techniques progressing in extended range, interference mitigation, and energy efficiency, this study explores their potential for passive RF sensing and citizen science applications. By bridging RFID localization and radio science, we investigate their feasibility in long-range sensing scenarios.</p>
},
	author = {Gamal Zayed and Ahmed Saeed and Yehea Ismail}
}
@proceedings {964,
	title = {Enhancing Lidar-GPS Fusion for Localization: Integrating Ionospheric Modeling with NeQuick G and V2V Implementation},
	year = {2025},
	month = {03/2025},
	publisher = {HamSCI},
	address = {Newark, NJ},
	abstract = {<p>Lidar (Light Detection and Ranging) provides high-resolution mapping and odometry for localization but faces limitations when used alone. Conversely, GPS positioning is impaired by ionospheric irregularities{\textemdash}such as Total Electron Content (TEC) variations, scintillation, and gravity waves{\textemdash}which distort satellite signals and reduce accuracy. This work explores a multi-modal fusion approach that integrates Lidar and GPS to optimize localization by compensating for ionospheric-induced errors. In the first phase, we focus on enhancing the GPS component of the Lidar-GPS fusion using the Galileo-based NeQuick G model, a mathematical framework for simulating ionospheric variations. Utilizing NASA{\textquoteright}s Archive of Space Geodesy Data, we analyze the spatio-temporal distribution of Vertical TEC (VTEC). By applying Particle Swarm Optimization (PSO), we identify global VTEC maxima at different altitudes, shedding light on worst-case ionospheric disturbances. For example, our analysis predicts that on January 1, 2027, at 12:00 AM UTC, the highest VTEC values occur at coordinates (-9.8, -88) near Peru, a critical region where GPS accuracy may degrade. This phase aids in forecasting GPS performance fluctuations in high-disturbance zones and informs the optimization of Lidar-GPS fusion for precision applications, including autonomous vehicles and V2V communication. The second phase is an aspirational extension aimed at implementing a Vehicle-to-Vehicle (V2V) communication model using Lidar sensors. This planned work will address challenges such as third-vehicle occlusion and background visible light noise. By integrating Lidar landmark matching, SLAM, and 3D point cloud processing, we intend to enhance localization reliability in dynamic multi-vehicle environments. Ultimately, this study bridges atmospheric science and vehicular localization, demonstrating the synergy between Lidar-based SLAM, ionospheric modeling, and the potential for robust Lidar-driven V2V networking to improve autonomous navigation and road safety.</p>
},
	author = {Gamal Zayed and Ahmed Saeed and Yehea Ismail}
}
