SenseChain: A blockchain-based crowdsensing framework for multiple requesters and multiple workers

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

In this paper, we propose a decentralized crowdsensing framework for multiple requesters with multiple workers built on Ethereum blockchain- SenseChain. Crowdsensing frameworks utilize workers’ sensing capabilities to fulfill requesters’ sensing tasks. While crowdsensing is typically managed by a centralized platform, the centralized management entails various challenges such as reliability in workers’ selection, fair evaluation for payment distribution, potential breach of users’ privacy, and high deployment cost. The proposed solution, SenseChain, is a decentralized crowdsensing framework developed to run on Ethereum blockchain to mitigate said challenges while increasing users’ engagement, with reasonable cost. SenseChain is developed around three smart contracts: (1) User Manager Contract (UMC), (2) Task Manager Contract (TMC), and (3) Task Detailed Contract (TDC). These contracts implement the platform features such as maintaining users’ information, publishing tasks from multiple requesters, accepting reservations and solutions from multiple workers, and evaluating solutions to calculate proportional payments. The framework is implemented using Solidity and Web3.js, where a real publicly available dataset is used. The framework performance is compared to a centralized greedy selection framework to demonstrate its comparability while mitigating tackled challenges. The results in terms of solutions quality, time cost, and workers traveled distance confirm its viability as a solution for crowdsensing.

Original languageBritish English
Pages (from-to)650-664
Number of pages15
JournalFuture Generation Computer Systems
Volume105
DOIs
StatePublished - Apr 2020

Keywords

  • Blockchain
  • Crowdsensing
  • Decentralized
  • Reputation
  • Smart contracts

Fingerprint

Dive into the research topics of 'SenseChain: A blockchain-based crowdsensing framework for multiple requesters and multiple workers'. Together they form a unique fingerprint.

Cite this