Secure IP Mobility Management for VANET Secure IP Mobility Management for VANET
SpringerBriefs in Computer Science

Secure IP Mobility Management for VANET

    • USD 39.99
    • USD 39.99

Descripción editorial

This brief presents the challenges and solutions for VANETs’ security and privacy problems occurring in mobility management protocols including Mobile IPv6 (MIPv6), Proxy MIPv6 (PMIPv6), and Network Mobility (NEMO). The authors give an overview of the concept of the vehicular IP-address configurations as the prerequisite step to achieve mobility management for VANETs, and review the current security and privacy schemes applied in the three mobility management protocols.

Throughout the brief, the authors propose new schemes and protocols to increase the security of IP addresses within VANETs including an anonymous and location privacy-preserving scheme for the MIPv6 protocol, a mutual authentication scheme that thwarts authentication attacks, and a fake point-cluster based scheme to prevent attackers from localizing users inside NEMO-based VANET hotspots. The brief concludes with future research directions. Professionals and researchers will find the analysis and new privacy schemes outlined in this brief a valuable addition to the literature on VANET management.

GÉNERO
Informática e Internet
PUBLICADO
2013
28 de agosto
IDIOMA
EN
Inglés
EXTENSIÓN
115
Páginas
EDITORIAL
Springer International Publishing
VENDEDOR
Springer Nature B.V.
TAMAÑO
2.3
MB

Otros libros de esta serie

Practical Backscatter Communication for the Internet of Things Practical Backscatter Communication for the Internet of Things
2024
Efficient Online Incentive Mechanism Designs for Wireless Communications Efficient Online Incentive Mechanism Designs for Wireless Communications
2024
Human Digital Twin Human Digital Twin
2024
From Unimodal to Multimodal Machine Learning From Unimodal to Multimodal Machine Learning
2024
Open-Set Text Recognition Open-Set Text Recognition
2024
Applications of Game Theory in Deep Learning Applications of Game Theory in Deep Learning
2024