CC* Integration-Small: Network-Aware Edge Computing for Real-time Wildfire Detection

Board Of Regents, Nshe, Obo University Of Nevada, Reno, Reno NV

Investigators

Abstract

The proliferation of Internet of Things (IoT) devices has facilitated the development of various scientific applications, from smart city initiatives to environmental hazard monitoring systems. In most of these applications, swift processing of data captured by IoT sensors is crucial to prevent natural disasters in a timely manner. While conventional cloud-based data processing pipelines offer cost-effective and performance-efficient solutions, it is often challenging to meet stringent performance requirements of hazard monitoring systems such as low latency and high bandwidth. Edge computing emerges as a compelling solution to bridge this gap by bringing computational processing closer to the data source. This project develops an edge computing framework tailored to address the computational requirements of time-sensitive distributed scientific applications such as wildfire monitoring. The edge computing framework has the potential to benefit other domains beyond wildfire monitoring, such as autonomous vehicles and emergency response systems. The project develops an edge computing framework that optimizes the task scheduling problem by combining high precision system monitoring with comprehensive application profiling. It develops a scalable resource monitoring system to monitor the status of compute resources (e.g., edge servers and cloud instances) and network resources using lightweight monitoring agents and P4 programmable network devices. Additionally, the project conducts application profiling to extract essential metrics regarding resource utilization and execution time of tasks across various edge server and cloud instance configurations. The scheduling is formulated as a multi-objective optimization problem, and various optimization methods such as mixed-integer linear programming, genetic algorithms, and heuristic methods are explored. Finally, the team targets a wildfire detection project (AlertWildfire) as a use case to demonstrate the effectiveness of the proposed framework. This project is jointly funded by Office of Advanced Cyberinfrastructure, the Established Program to Stimulate Competitive Research (EPSCoR), and the Division of Computer and Network Systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.