DCS: Distributed Caching Strategy at the Edge of Vehicular Sensor Networks in Information-Centric Networking

• Main Authors: Yahui Meng, Muhammad Ali Naeem, Rashid Ali, Yousaf Bin Zikria, Sung Won Kim
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Sensors
• Subjects: information-centric networking (icn); client-cache (cc); video on demand (vod); vehicular sensor network (vsn)
• Online Access: https://www.mdpi.com/1424-8220/19/20/4407

Information dissemination in current Vehicular Sensor Networks (VSN) depends on the physical location in which similar data is transmitted multiple times across the network. This data replication has led to several problems, among which resource consumption (memory), stretch, and communication latency due to the lake of data availability are the most crucial. Information-Centric Networking (ICN) provides an enhanced version of the internet that is capable of resolving such issues efficiently. ICN is the new internet paradigm that supports innovative communication systems with location-independent data dissemination. The emergence of ICN with VSNs can handle the massive amount of data generated from heterogeneous mobile sensors in surrounding smart environments. The ICN paradigm offers an in-network cache, which is the most effective means to reduce the number of complications of the receiver-driven content retrieval process. However, due to the non-linearity of the Quality-of-Experience (QoE) in VSN systems, efficient content management within the context of ICN is needed. For this purpose, this paper implements a new distributed caching strategy (DCS) at the edge of the network in VSN environments to reduce the number of overall data dissemination problems. The proposed DCS mechanism is studied comparatively against existing caching strategies to check its performance in terms of memory consumption, path stretch ratio, cache hit ratio, and content eviction ratio. Extensive simulation results have shown that the proposed strategy outperforms these benchmark caching strategies.