Crowd-MECS: A Novel Crowdsourcing Framework for Mobile Edge Caching and Sharing
Abstract
Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision-making of different stakeholders. In this paper, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the EDs can decide whether to cache and share contents for the CP, and the CP can decide to share a certain revenue with EDs as the incentive of caching and sharing contents. We formulate such an interaction as a two-stage Stackelberg game. In Stage I, the CP aims to maximize its own profit by deciding the ratio of revenue shared with EDs. In Stage II, EDs aim to maximize their own payoffs by choosing to be agents who cache and share contents, and meanwhile gain a certain revenue from the CP, or requesters who do not cache but request contents in the on-demand fashion. We first analyze the EDs' best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the non-atomic game theory. Then, we identify the piece-wise structure and the unimodal feature of the CP's profit function, based on which we design a tailored low-complexity one-dimensional search algorithm to achieve the optimal revenue sharing ratio for the CP in…
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.