Auction-based Adaptive Resource Allocation Optimization in Dense and Heterogeneous IoT Networks

TL;DR

This work tackles resource allocation in ultra-dense IoT networks under incomplete information by marrying STFS-enabled transmission with Bayesian game theory. It proposes the modified Simultaneous Ascending Auction (mSAA) and a dispersion-matrix optimization to distribute computational load and minimize interference, outperforming FPSB, SPSB, and VCG in terms of surplus, revenue, and energy efficiency. The approach demonstrates robustness to heterogeneous node capabilities and strategic misreporting, achieving near-optimal allocations relative to exhaustive search while maintaining scalability. The results have practical implications for edge computing in 5G/6G contexts, enabling fair, energy-aware, distributed resource management in densely deployed IoT environments.

Abstract

Efficient and reliable resource allocation within densely-deployed massive IoT networks remains a key challenge due to resource constraints among low-size, weight, and power (SWaP) IoT devices and within the network and limitations of conventional centralized methods under incomplete information. We propose a novel auction-based framework for adaptive resource allocation, combining space-time-frequency spreading (STFS) techniques with Bayesian Game approaches. We introduce novel modified Simultaneous Ascending Auction (mSAA) mechanism tailored to densely-deployed and low-complexity IoT networks, enabling distributed computation and reduced power consumption. By incorporating Bayesian game-based bidding strategies and optimizing dispersion matrices for signal transmission, the proposed approach ensures enhanced channel throughput and energy efficiency. Comparative analysis against traditional auction types, including First-Price and Second-Price Sealed-Bid Auctions, as well as the Vickery-Clarke-Groves (VCG) mechanism, demonstrates the superiority of mSAA in terms of surplus maximization, revenue efficiency, and robustness in risk-prone bidding environments. Simulation results validate the model's adaptability to heterogeneous IoT nodes and its potential for dense deployment across different environments and verticals.

Figures (12)

• Figure 1: Representation of Auction Game model-based simulators for the Co-Design systems of Resource allocation Optimization in Dense IoT network
• Figure 2: (a), (b)Variation of optimal BNE bidding strategy with valuation vector $\left(\mathcal{B}^{BNE}\text{ v/s }\mathcal{V}\right)$, (c)Variation of logarithmic mean absolute error density $\left(\rho_{MAE}(dB)~\text{ v/s }~\mathcal{V}_{i}\right)$ between analytical: \ref{['Eq: BNE_FPSB_Vh_Vg']} and numerical: algorithm \ref{['Alg: numerical_fpsb']} in FPSB auctions with distinct valuation distributions corresponding to $\mathcal{V}=\alpha \mathcal{V}^{H} + \beta \mathcal{V}^{G}$ and $\alpha=1$, $\beta=1$, $K=5, N=1$.
• Figure 3: $(a)$ Convergence patterns, and $(b)$ State graph of indicator metric $c_{\mathcal{K}}^{\mathcal{N}}$ for $K~=~5$ IoT nodes auctioning $N=3$ STFS resource segments over $I=44$ processing iterations.
• Figure 4: The convergence pattern from a random initial point $\textbf{z}_{k}$, toward the global stationery $\textbf{z}_{k}^{*}$, along the dimensions of phase and gain while occurring optimal dispersion vector $\textbf{a}_{k}$ for each IoT device $k \in \mathcal{K}$.
• Figure 5: The strength and time/frequency deviation of each uplink $k$ at the gateway with and without pre-compensation utilizing optimal dispersion metric $\textbf{A}$ for $K=12$ nodes.

Theorems & Definitions (4)

• Lemma 1
• Definition 1
• Lemma 2
• Definition 2