Trustworthy mobile edge caching: a blockchain approach to mitigate malicious nodes and incentivize cache sharing

• URL: http://arxiv.org/abs/2608.20145v1
• Date: Thu, 20 Aug 2026 15:05:22 GMT
• Title: Trustworthy mobile edge caching: a blockchain approach to mitigate malicious nodes and incentivize cache sharing
• Abstract summary: This paper proposes a blockchain-based trust management mechanism to accurately identify trustworthy edge servers and mobile nodes.<n>To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing.<n>We show that the proposed method can accurately distinguish between honest and malicious servers, even with a 10% noise in data.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capacity limitations, helper mobile nodes can contribute their cache resources. However, due to their selfish behavior, an incentive mechanism is necessary to encourage resource sharing. Additionally, these helper nodes can also be malicious. This paper proposes a blockchain-based trust management mechanism that addresses these challenges by accurately identifying trustworthy edge servers and mobile nodes. The proposed mechanism calculates both direct and indirect trust using smart contracts, ensuring that malicious nodes are effectively filtered out. Trustworthiness is determined based on mobile node satisfaction with the quality of service, and trust data is securely stored on the blockchain. To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing. Furthermore, a blockchain-based authentication mechanism protects against node impersonation. Our approach optimizes trust, cache capacity, and cost efficiency while considering mobile node mobility, energy consumption, and computational power constraints during the consensus process. Simulation results show that the proposed method can accurately distinguish between honest and malicious servers, even with a 10% noise in data.

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