Auction-based adaptive resource allocation optimization in dense and heterogeneous IoT networks
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Date
2025-10-22
Authors
Wickramasinghe, Nirmal D.
Dooley, John
Pesch, Dirk
Dey, Indrakshi
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Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
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 Vickrey–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.
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Keywords
IoT networks , Auction game theory , Resource allocation , Space-time-frequency spreading
Citation
Wickramasinghe, N. D., Dooley, J., Pesch, D. and Dey, I. (2025) 'Auction-based adaptive resource allocation optimization in dense and heterogeneous IoT networks', IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2025.3624456
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© 2025, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.