A survey on the roles of Bloom Filter in implementation of the Named Data Networking

Nayak, Sabuzima; Patgiri, Ripon; Borah, Angana

doi:10.1016/j.comnet.2021.108232

Cited by 18 publications

(11 citation statements)

References 90 publications

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“…Finally, issues and challenges in V-NDN data transmission are discussed, as well as future-oriented solutions. [59] Bloom filter implementation in NDN The role of Bloom Filter in implementing NDN is discussed in depth in this article. The paper also goes through Bloom Filter and the NDN architecture's core components, including Packet, CS, PIT and FIB.…”

Section: Yearmentioning

confidence: 99%

Named Data Networking: A Survey on Routing Strategies

et al. 2022

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Data has become an increasingly significant component of the Internet as it has increased in popularity. Where people care more about the data than the location of data. The Named Data Network (NDN) took this concept and paired it with the idea of making data a core component rather than host addresses. Because of its in-network caching capability, NDN will become a better contender than current Transmission Control Protocol and Internet Protocol (TCP/IP) based networks as data traffic grows exponentially. NDNrelated challenges are available for investigation as NDN becomes more important. Routing in NDN is another essential domain that needs to be addressed, and several approaches have been presented to address routing concerns in NDN. In this study, we discuss and highlight NDN and its routing strategies comprehensively. In addition, this research illustrates a comparison of important routing paradigms to emphasis the breadth of routing research in NDN. Also, we investigate the routing attributes of NDN and expose the latest literature on this critical topic. Finally, this study provides useful insights into the emerging areas of guidance in the NDN to assist future studies in addressing challenges and open research issues. INDEX TERMSNamed Data Networks, Open Research issues in NDN, Routing in NDN I.

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Section: Yearmentioning

confidence: 99%

Named Data Networking: A Survey on Routing Strategies

et al. 2022

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“…A detailed description of Bloom Filters may be found in [Blo70], [Mit09], however the main properties of B H k,m (A) that we incorporate are: 1) no "false negative" identification, i.e., if for any H i ∈ H it holds that F [H i (a)] == 0 then a / ∈ A; 2) the probability of "false positives" can be kept arbitrarily small by adjusting the length of the filter and the number of hash functions used, i.e., the probability of false positives is (1 − e −kn/m ) k , and 3) for fixed m and n, the number of hash functions that minimizes the false positive probability is k = m n ln 2. Bloom filter is often used to improve performance and storage requirements of algorithms, e.g., it can speed up computation of Private Set Intersection (PSI) of massive datasets [QZL + 22], and it could resolve scalability issues of IP-based Internet by enhancing the performance of Named Data Networking (NDN) [NPB21].…”

Section: Bloom Filter Based Ibs a Bloom Filtersmentioning

confidence: 99%

RFID Batch Authentication—A Usable Scheme Providing Anonymity

et al. 2022

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Businesses are slowly replacing Universal Product Code (barcodes used for tracking items) with radio-frequency identification (RFID). Unlike a barcode, RFID tag does not need to be within the line of sight of the reader, and can be interrogated from up to hundreds of meters from the reader in the case of active tag. Some readers are designed to read multiple RFID tags concurrently, which is useful in, e.g., warehouses. We address the problem of efficient methods for simultaneous authentication of multiple RFID tags -batch tag authentication. We provide a general design to build a provable secure batch tag authentication protocol from a single-tag authentication scheme using some aggregation function. Our main goal is to build a computationally-efficient batch tag authentication method that provides a reasonable level of anonymity in a model with an untrusted RFID reader. We provide an efficiency analysis of a protocol where the Bloom filter is used as the aggregation function together with a brief comparison with alternative data structures.

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“…Bloom Filter [20] is an approximate set membership filtering data structure which is defined in Definition 1. It is extremely popular in diverse domains, particularly, Big Data [34], IoT, Cloud Computing, Networking [35], Security [21,36], Database, Bioinformatics [37], and Biometrics. Bloom Filter is applied to reduce the main memory footprint.…”

Section: Bloom Filtermentioning

confidence: 99%

“…In insertion, all corresponding k bit positions of the bit array are set to 1 using k hash functions. In a query operation, all k bit positions of the bit array must be 1 to return true; otherwise, Bloom Filter returns false, which refers that the queried item is not in the set [31] .…”

Section: Bloom Filtermentioning

confidence: 99%

“…Let 𝔹 be a Bloom Filter of size m , 𝕊 = { e 1 , e 2 , e 3 , …, e n } be the set of words inserted into the Bloom Filter 𝔹, U be the universe where 𝕊 ⊂ U, k is the number of hash functions. Bloom Filter size (i.e., m ) is a prime number to reduce collision [31]. Bloom Filter calculates the optimal number of hash functions using Equation 1.…”

Section: Bloom Filtermentioning

confidence: 99%

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BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

Nayak

Patgiri

2021

Preprint

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Biological network represents the interaction or relationship between the biological entities such as proteins and genes of a biological process. A biological network with thousands of millions of vertices makes its processing complex and challenging. In this article, we have proposed a novel Bloom Filter for biological networks, called BionetBF, to provide fast membership identification of the biological network edges or paired biological data. BionetBF is capable of executing millions of operations within a second on datasets having millions of paired biological data while occupying tiny amount of main memory. We have conducted rigorous experiments to prove the performance of BionetBF with large datasets. The experiment is conducted using 12 generated datasets and three biological network datasets. BionetBF demonstrates higher performance while maintaining a 0.001 false positive probability. BionetBF is also compared with other filters: Cuckoo Filter and Libbloom, where BionetBF proves its supremacy by exhibiting higher performance with a smaller sized memory compared with large sized filters of Cuckoo Filter and Libbloom.

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A survey on the roles of Bloom Filter in implementation of the Named Data Networking

Cited by 18 publications

References 90 publications

Named Data Networking: A Survey on Routing Strategies

Named Data Networking: A Survey on Routing Strategies

RFID Batch Authentication—A Usable Scheme Providing Anonymity

BionetBF: A Novel Bloom Filter for Faster Membership Identification of Large Biological Graph

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