BlockSense: Towards Trustworthy Mobile Crowdsensing via Proof-of-Data Blockchain

Junqin Huang, Linghe Kong, Long Cheng, Hong Ning Dai, Meikang Qiu, Guihai Chen, Xue Liu, Gang Huang

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


Mobile crowdsensing (MCS) can promote data acquisition and sharing among mobile devices. Traditional MCS platforms are based on a triangular structure consisting of three roles: data requester, worker ( i.e . , sensory data provider) and MCS platform. However, this centralized architecture suffers from poor reliability and difficulties in guaranteeing data quality and privacy, even provides unfair incentives for users. In this paper, we propose a blockchain-based MCS platform, namely BlockSense, to replace the traditional triangular architecture of MCS models by a decentralized paradigm. To achieve the goal of trustworthiness of BlockSense, we present a novel consensus protocol, namely Proof-of-Data (PoD), which leverages miners to conduct useful data quality validation work instead of “useless” hash calculation. Meanwhile, in order to preserve the privacy of the sensory data, we design a homomorphic data perturbation scheme, through which miners can verify data quality without knowing the contents of the data. We have implemented a prototype of BlockSense and conducted case studies on campus, collecting over 7,000 data from workers' mobile phones. Both simulations and real-world experiments show that BlockSense can not only improve system security, preserve data privacy and guarantee incentives fairness, but also achieve at least 5.6x faster than Ethereum smart contracts in verification efficiency.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalIEEE Transactions on Mobile Computing
Publication statusE-pub ahead of print - 21 Dec 2022

Scopus Subject Areas

  • Software
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

User-Defined Keywords

  • Mobile crowdsensing
  • Blockchain
  • Verifiable computation
  • Proof of useful work
  • Consensus
  • Privacy


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