Auction-based Adaptive Resource Allocation Optimization in Dense and Heterogeneous IoT Networks
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arXiv
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| Format: | Preprint |
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2024
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| author | Wickramasinghe, Nirmal D. Dooley, John Pesch, Dirk Dey, Indrakshi |
| author_facet | Wickramasinghe, Nirmal D. Dooley, John Pesch, Dirk Dey, Indrakshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Auction-based Adaptive Resource Allocation Optimization in Dense and Heterogeneous IoT Networks Wickramasinghe, Nirmal D. Dooley, John Pesch, Dirk Dey, Indrakshi Computer Science and Game Theory 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. |
| title | Auction-based Adaptive Resource Allocation Optimization in Dense and Heterogeneous IoT Networks |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2409.17843 |