Batoul Sulaiman

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About

Batoul Sulaiman’s research lies at the intersection of indoor localization, robotics, and machine learning, with a focus on developing scalable solutions for real-world spatial intelligence. Her most-cited work, “Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation” (2023, 8 citations), addresses a critical bottleneck in artificial neural network (ANN) and machine learning research: the scarcity of high-quality, reproducible datasets for indoor positioning. By designing an autonomous, low-cost robot to systematically collect WiFi-beacon data in 3D environments, Sulaiman created a foundational resource that enables more accurate and efficient training of localization models. This contribution is particularly impactful for applications in smart buildings, autonomous navigation, and IoT systems, where reliable indoor location estimation remains a challenge. Her work underscores the importance of bridging hardware and software innovation—demonstrating how accessible robotic platforms can democratize dataset generation for the broader research community. Sulaiman’s approach not only advances the field of indoor positioning but also sets a precedent for integrating robotics with machine learning pipelines, making her a promising voice in the ongoing effort to build smarter, more autonomous indoor environments.