Senlin Luo
Beijing Institute of Technology
64 Papers
77 Citations
Senlin Luo is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Computer science & Vehicular ad hoc network. The author has an hindex of 11, co-authored 45 publications.
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Papers
Named Data Networking in Vehicular Ad Hoc Networks: State-of-the-Art and Challenges
Hakima Khelifi,Senlin Luo,Boubakr Nour,Hassine Moungla,Yasir Faheem,Rasheed Hussain,Adlen Ksentini +6 more
TL;DR: Inspired by the extensive research results in NDN-based VANET, this paper provides a detailed and systematic review ofNDN-driven VANet and discusses the feasibility of NDN architecture in VANets environment.
An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis
TL;DR: This paper proposes a novel Android malware detection method based on the method-level correlation relationship of application’s abstracted API calls, which achieves higher accuracy while improving detection efficiency by 15 times and is competitive in terms of classification accuracy and detection efficiency.
Cloud-based security and privacy-aware information dissemination over ubiquitous VANETs
TL;DR: This paper proposes a cloud-based security and privacy-aware information dissemination environment between vehicular nodes and cloud infrastructure, and takes on ciphertext policy attribute-based encryption (CP-ABE) to implement the access control systems and effective access policies for both cloud and VANETs.
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SVPS: Cloud-based smart vehicle parking system over ubiquitous VANETs
TL;DR: The proposed cloud-based smart vehicle parking system (SVPS) over ubiquitous VANETs offers a unique algorithm that provides an appropriate vacant parking space information along with booking and recommendation options to facilitate vehicles in an effective, real-time and precise manner.
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PIaaS: Cloud-oriented secure and privacy-conscious parking information as a service using VANETs
TL;DR: Cloud infrastructure process the Big Parking Data (BPD) and yields the most significant parking information in a dynamic, pertinent, privacy and confidentiality preserved manner and proposes a novel geo-location-based parking lock encryption mechanism for location privacy and non-frameability.
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