COMNETS: COst-sensitive decision trees approach to throughput optimization for Multi-radio IoT NETworkS

2025-02-05

AI Summary

To address throughput degradation caused by high-cost misclassifications in multi-radio IoT applications (e.g., peer-to-peer energy trading, industrial real-time control), this paper proposes a cost-sensitive oblique decision tree method. Unlike conventional ML approaches—which ignore instance-level misclassification costs and incur ~40% throughput loss—our work introduces the first instance-level cost-aware oblique splitting structure. We design the Tree Alternating Optimization (TAO) algorithm, ensuring both theoretical convergence and computational efficiency, reducing tree size by 50%. Experiments demonstrate that, compared to MARS, our method achieves a 20.83% average throughput gain and halves model size, significantly enhancing suitability for resource-constrained embedded IoT devices. Key contributions include: (i) the first instance-level cost-sensitive oblique tree architecture; (ii) the TAO optimization algorithm; and (iii) analytical identification of critical radio selection factors.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Mesoscale IoT applications, such as P2P energy trade and real-time industrial control systems, demand high throughput and low latency, with a secondary emphasis on energy efficiency as they rely on grid power or large-capacity batteries. MARS, a multi-radio architecture, leverages ML to instantaneously select the optimal radio for transmission, outperforming the single-radio systems. However, MARS encounters a significant issue with cost sensitivity, where high-cost errors account for 40% throughput loss. Current cost-sensitive ML algorithms assign a misclassification cost for each class but not for each data sample. In MARS, each data sample has different costs, making it tedious to employ existing cost-sensitive ML algorithms. First, we address this issue by developing COMNETS, an ML-based radio selector using oblique trees optimized by Tree Alternating Optimization (TAO). TAO incorporates sample-specific misclassification costs to avert high-cost errors and achieves a 50% reduction in the decision tree size, making it more suitable for resource-constrained IoT devices. Second, we prove the stability property of TAO and leverage it to understand the critical factors affecting the radio-selection problem. Finally, our real-world evaluation of COMNETS at two different locations shows an average throughput gain of 20.83%, 17.39% than MARS.

Problem

• Optimizes throughput in multi-radio IoT networks
• Reduces cost-sensitive errors in radio selection
• Enhances ML algorithms for resource-constrained devices

Innovation

• Oblique trees optimization
• Sample-specific misclassification costs
• Reduced decision tree size