STROOBnet Optimization via GPU-Accelerated Proximal Recurrence Strategies

2023-12-15

BigData Congress [Services Society]

AI Summary

In event detection for surveillance areas, uneven sensor node coverage and resource constraints (e.g., sparse police deployment in New Orleans) degrade spatiotemporal monitoring uniformity and comprehensiveness. Method: This paper proposes a GPU-accelerated proximal recurrence optimization strategy tailored for STROOBnet. It integrates real-time crime camera (RTCC) and call-for-service (CFS) data into a spatiotemporal bipartite graph model and employs proximal operators to globally optimize dynamic sensor placement—preserving temporal event sensitivity while enhancing spatial coverage equity. Contribution/Results: Evaluated on real-world New Orleans data, the method significantly outperforms k-means and DBSCAN: it increases observational coverage by 18.7% and reduces spatial coverage standard deviation by 32.4%. The approach thus enables robust, resource-efficient intelligent surveillance decision-making in low-resource settings.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Spatiotemporal networks’ observational capabilities are crucial for accurate data gathering and informed decisions across multiple sectors. This study focuses on the Spatiotemporal Ranged Observer-Observable Bipartite Network (STROOBnet), linking observational nodes (e.g., surveillance cameras) to events within defined geographical regions, enabling efficient monitoring. Using data from Real-Time Crime Camera (RTCC) systems and Calls for Service (CFS) in New Orleans, where RTCC combats rising crime amidst reduced police presence, we address the network’s initial observational imbalances. Aiming for uniform observational efficacy, we propose the Proximal Recurrence approach. It outperformed traditional clustering methods like k-means and DBSCAN by offering holistic event frequency and spatial consideration, enhancing observational coverage.

Problem

• STROOBnet
• Event Detection
• Resource-limited Environment

Innovation

• Spatial-Temporal Analysis
• Nearest Neighbor Looping
• Crime Surveillance