MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems

TL;DR

The paper introduces MOHAF, a distributed multi-objective auction framework for IoT resource allocation that balances cost, QoS, energy, and fairness, demonstrating superior efficiency and fairness in large-scale experiments.

Contribution

It presents a novel hierarchical auction mechanism with theoretical guarantees and real-time dynamic pricing for scalable, fair, and efficient IoT resource management.

Findings

MOHAF outperforms baseline auctions in efficiency (0.263 vs. 0.185, 0.138, 0.101)

Achieves perfect fairness with Jain's index of 1.000

Demonstrates near-linear scalability and strong empirical performance

Abstract

The rapid growth of Internet of Things (IoT) ecosystems has intensified the challenge of efficiently allocating heterogeneous resources in highly dynamic, distributed environments. Conventional centralized mechanisms and single-objective auction models, focusing solely on metrics such as cost minimization or revenue maximization, struggle to deliver balanced system performance. This paper proposes the Multi-Objective Hierarchical Auction Framework (MOHAF), a distributed resource allocation mechanism that jointly optimizes cost, Quality of Service (QoS), energy efficiency, and fairness. MOHAF integrates hierarchical clustering to reduce computational complexity with a greedy, submodular optimization strategy that guarantees a (1-1/e) approximation ratio. A dynamic pricing mechanism adapts in real time to resource utilization, enhancing market stability and allocation quality. Extensive experiments on the Google Cluster Data trace, comprising 3,553 requests and 888 resources, demonstrate MOHAF's superior allocation efficiency (0.263) compared to Greedy (0.185), First-Price (0.138), and Random (0.101) auctions, while achieving perfect fairness (Jain's index = 1.000). Ablation studies reveal the critical influence of cost and QoS components in sustaining balanced multi-objective outcomes. With near-linear scalability, theoretical guarantees, and robust empirical performance, MOHAF offers a practical and adaptable solution for large-scale IoT deployments, effectively reconciling efficiency, equity, and sustainability in distributed resource coordination.