B4SDC: A Blockchain System for Security Data Collection in MANETs

Gao Liu, Huidong Dong, Zheng Yan, Xiaokang Zhou, Shohei Shimizu

Research output: Contribution to journalArticleScientificpeer-review

37 Citations (Scopus)
355 Downloads (Pure)


Security-related data collection is an essential part for attack detection and security measurement in Mobile Ad Hoc Networks (MANETs). A detection node (i.e., collector) should discover available routes to a collection node for data collection and collect security-related data during route discovery for determining reliable routes. However, few studies provide incentives for security-related data collection in MANETs. In this paper, we propose B4SDC, a blockchain system for security-related data collection in MANETs. Through controlling the scale of Route REQuest (RREQ) forwarding in route discovery, the collector can constrain its payment and simultaneously make each forwarder of control information (namely RREQs and Route REPlies, in short RREPs) obtain rewards as much as possible to ensure fairness. At the same time, B4SDC avoids collusion attacks with cooperative receipt reporting, and spoofing attacks by adopting a secure digital signature. Based on a novel Proof-of-Stake consensus mechanism by accumulating stakes through message forwarding, B4SDC not only provides incentives for all participating nodes, but also avoids forking and ensures high efficiency and real decentralization. We analyze B4SDC in terms of incentives and security, and evaluate its performance through simulations. The thorough analysis and experimental results show the efficacy and effectiveness of B4SDC.

Original languageEnglish
Pages (from-to)739 - 752
Number of pages14
JournalIEEE Transactions on Big Data
Issue number3
Early online date2020
Publication statusPublished - 1 Jun 2022
MoE publication typeA1 Journal article-refereed


  • Ad hoc networks
  • Bitcoin
  • Blockchain
  • blockchain
  • Data collection
  • incentive mechanism
  • MANETs
  • Mobile computing
  • security-related data collection
  • Task analysis


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