IMF-PR: An Improved Morton-Filter-Based Pseudonym-Revocation Scheme in VANETs.

CRL VANETs improved Morton filter pseudonym revocation

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
18 Apr 2023
Historique:
received: 14 03 2023
revised: 04 04 2023
accepted: 12 04 2023
medline: 28 4 2023
pubmed: 28 4 2023
entrez: 28 4 2023
Statut: epublish

Résumé

Vehicle ad hoc networks (VANETs) are special wireless networks which help vehicles to obtain continuous and stable communication. Pseudonym revocation, as a vital security mechanism, is able to protect legal vehicles in VANETs. However, existing pseudonym-revocation schemes suffer from the issues of low certificate revocation list (CRL) generation and update efficiency, along with high CRL storage and transmission costs. In order to solve the above issues, this paper proposes an improved Morton-filter-based pseudonym-revocation scheme for VANETs (IMF-PR). IMF-PR establishes a new distributed CRL management mechanism to maintain a low CRL distribution transmission delay. In addition, IMF-PR improves the Morton filter to optimize the CRL management mechanism so as to improve CRL generation and update efficiency and reduce the CRL storage overhead. Moreover, CRLs in IMF-PR store illegal vehicle information based on an improved Morton filter data structure to improve the compress ratio and the query efficiency. Performance analysis and simulation experiments showed that IMF-PR can effectively reduce storage by increasing the compression gain and reducing transmission delay. In addition, IMF-PR can also greatly improve the lookup and update throughput on CRLs.

Identifiants

pubmed: 37112407
pii: s23084066
doi: 10.3390/s23084066
pmc: PMC10145359
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : National Natural Science Foundation of China
ID : 52130403
Organisme : Fundamental Research Funds for the Central Universities
ID : N2017003

Auteurs

Cong Zhao (C)

Software College, Northeastern University, Shenyang 110819, China.

Jiayu Qi (J)

Software College, Northeastern University, Shenyang 110819, China.

Tianhan Gao (T)

Software College, Northeastern University, Shenyang 110819, China.

Xinyang Deng (X)

Software College, Northeastern University, Shenyang 110819, China.

Classifications MeSH