A Geolocation-Based Framework for Discovering Extreme Edge Devices

Traditional cloud computing architectures, despite their massive centralized capacity, remain inadequate for latency-sensitive and mission-critical applications due to inherent communication delays, bandwidth limitations, and the risks associated with centralized control. Applications such as autonomous systems, real-time analytics, and immersive media demand computing infrastructures that are more responsive, adaptive, and resilient. Extreme edge computing (XEC) has emerged as a promising paradigm to address these challenges by shifting computation to the periphery of the network and harnessing underutilized resources on user-owned devices such as laptops, smartphones, and IoT sensors. By enabling ultra-low latency processing, localized responsiveness, and greater fault tolerance in environments where connectivity may be intermittent, XEC reduces reliance on distant cloud infrastructures while unlocking new forms of participatory, decentralized computation. As part of this exploration, our work developed a real-time geolocation dashboard to demonstrate how edge devices can be discovered and monitored for their suitability as computing nodes. The framework integrates browser-based geolocation services with geohash encoding to organize devices spatially, while WebSocket communication and a live InstantDB backend enable continuous synchronization of status information. Metadata, including IP address, operating system, CPU cores, and battery status, is captured in real time to support dynamic assessments of device availability and capability. A React-based interface visualizes devices on an interactive map, offering proximity grouping and dynamic status indicators to assist in the identification of optimal nodes for task offloading. This work contributes an enabling tool that demonstrates the feasibility of real-time edge device monitoring and coordination. By making the process of discovering and evaluating edge devices more transparent and accessible, the framework lays groundwork for future systems that aim to operationalize extreme edge computing and move further beyond dependence on centralized cloud infrastructures.