Abstract:
This paper proposes a privacy-preserving publish–subscribe-based decentralized framework for MCS systems named “Pub-SubMCS”. The framework allows data sharing, where requesters can subscribe to an existing data request (task) if their requirements match. Otherwise, they can create a new task with specific requirements on considered parameters. Incorporating the publish–subscribe (pub–sub) service model in a decentralized MCS system saves system entities’ sensing and computing resources and the cost of acquiring the data by the requesters. However, the pub–sub service model makes the curse of sensing issues more severe. Pub-SubMCS handles the curse of sensing issues by performing access control using smart contracts, which impose restrictions on data collectors (workers) to publish the data and identify and penalize the malicious workers early. To ensure data privacy and validation simultaneously over blockchain, we perform data transformation enabling the validation algorithm to run over transformed data and thus enhancing trust among the system entities. In particular, we use the normalization technique to transform data and the Pearson correlation coefficient measure to compare the similarity in the collected sensor data. Pub-SubMCS is implemented on the Ethereum blockchain, and solidity programming language is used to create smart contracts. The security analysis and experiment results show the proposed system’s scalability, usability, and feasibility. We also demonstrate the effectiveness of the publish–subscribe model against the requester–worker model.