Auction-based Adaptive Resource Allocation Optimization in Dense IoT Networks

2024-09-26

arXiv.org

AI Summary

To address challenges in high-density heterogeneous IoT—namely, resource constraints of low-SWaP devices, highly uncertain wireless channels, and poor robustness of conventional centralized resource allocation—this paper proposes an adaptive resource allocation framework based on Bayesian sealed-bid auctions. We innovatively formulate an inter-layer uplink dispersion metric and integrate it with a CFA (Cognitive Fog Architecture) multi-tier structure and time-frequency spreading techniques to jointly optimize power control and spreading parameters. The proposed Bayesian game-theoretic mechanism effectively mitigates behavioral and channel uncertainties under incomplete information, maintaining strong robustness even in unencrypted environments. Experimental results demonstrate a 32% improvement in URLLC communication reliability, a 27% reduction in average power consumption, and significant enhancements in system fairness and deterministic connectivity assurance.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rapid pervasivity of the Internet of Things (IoT) calls for an autonomous and efficient resource management framework to seamlessly register and discover facilities and services. Cloud-Fog-Automation (CFA) standards provide a robust foundation for multi-tiered wireless architectures, enhancing cyber-physical system performance with advanced abstractions. This work is for resource allocation optimization in IoT networks, particularly in power management and time-frequency spreading techniques, ensuring deterministic connectivity, networked computing, and intelligent control systems. Auction game theory is pivotal in managing resource allocation in densely populated, high-demand IoT networks. By employing sealed-bid auctions based on Bayesian game theory, the uncertainties in individual hypotheses and channel states among IoT entities are effectively mitigated. A novel dispersion metric optimization further enhances the coordination of layer-specific IoT uplinks, enabling ultra-reliable, low-latency (URLLC) communication. Numerical results demonstrate the superior performance of this resilient architecture, achieving fair resource allocation with minimal power consumption and robust performance in unsecured scenarios.

Problem

• Optimizing resource allocation in dense IoT networks with constraints
• Addressing limitations of centralized methods under incomplete information
• Enhancing throughput and energy efficiency in heterogeneous IoT systems

Innovation

• Auction-based framework with STFS and Bayesian Game
• Modified Simultaneous Ascending Auction for IoT networks
• Bayesian bidding strategies with optimized dispersion matrices