BLOOM FILTER APPLICATIONS IN NAMED DATA NETWORK: A COMPREHENSIVE REVIEW

Internet was designed to provide source to destination communication and it had shown good resilience over the time. In today’s world, evolution of content-centric applications has led to failure of internet as it was designed to interact over a pre-established communication channel. Named Data Networking (NDN) came up as a solution to this which works with content specific requests and returns the contents to its requester regardless of its location. It uses different data structures where it stores information about various content and perform a search among these when a request encounters. As the requests and content size increases it leads to increase in complexity of query as well as memory consumption. So main challenge is to find the solution to efficiently store, query and retrieve large number of entries related to content names in real time interactions, Probabilistic Data Structures (PDS) came up as the solution for this as they are suitable for large data processing, approximate predications, fast retrieval and storing unstructured data, thus improving space and search complexity in Big data processing. Bloom filter is a PDS which is used for approximate membership query. Many variants of BF have been already successfully employed in various domains in NDN like, scalable forwarding and routing, caching and security. In this paper, we try to discuss the study of applications of BF in different NDN domains in depth. We conclude our survey by identifying a set of open challenges in NDN which should be addressed by using PDS.