A Geolocation-Based Framework for Discovering Extreme Edge Devices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".