Double-Edge Embedding Based Provenance Recovery for Low-Latency Applications in Wireless Networks

Harshan Jagadeesh, Amogh Vithalkar, Manthan Kabra, Naman Jhunjhunwala, Prafull Manav, Yih Chun Hu

Research output: Contribution to journalArticlepeer-review


A number of applications in next-generation multi-hop networks impose low-latency requirements on data transmission thereby necessitating the underlying relays to introduce negligible delay when forwarding the packets. While traditional relaying techniques such as amplify-and-forward protocols may help the packets to satisfy latency-constraints, such strategies do not facilitate the destination in learning the provenance of the packets, which in turn could be used for either learning the topology of the network or detecting security threats on the network. With potential applications to vehicular networks, we address the design of provenance embedding algorithms that reduce the delays on the packets and yet assist the destination in determining their provenance. We propose double-edge (DE) embedding techniques, wherein a subset of the relay nodes in the path strategically skip the provenance embedding process to reduce the delays on the packets. Using bloom filters as tools to implement double-edge embedding, first, we derive upper bounds on the error-rates of the DE embedding techniques, and subsequently present experimental results on a test bed to demonstrate the efficacy of the proposed techniques. We show that the DE embedding techniques offer latency benefits up to 17 % along with remarkable reduction in error-rates in comparison with the baselines

Original languageEnglish (US)
JournalIEEE Transactions on Dependable and Secure Computing
StateAccepted/In press - 2020


  • Delays
  • Network topology
  • Provenance
  • Relays
  • Security
  • Spread spectrum communication
  • Upper bound
  • Wireless sensor networks
  • bloom filters
  • low-latency
  • multi-hop networks
  • security

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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