In-packet Bloom filters: Design and networking applications

Rothenberg, Christian Esteve; Macapuna, Carlos Alberto Bráz; Magalhães, Mauricio Ferreira; Verdi, Fábio L.; Wiesmaier, Alexander

doi:10.1016/j.comnet.2010.12.005

Cited by 33 publications

(14 citation statements)

References 26 publications

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“…Purposes Gains Scenarios Purposes Gains Caching [9] Content summarization CQ, AR Akamai [14] Content summarization CQ, AR Wireless ad hoc [38] Content summarization CQ, AR Wireless net. [39] Content summarization CQ, AR IP lookup [40] [41] Representing routing table CQ, SS Multicast [42] Representing multicast tree CQ, SS MANETs [46] [47] Representing topology info. CQ, SS NDN [44] [45] Representing content names CQ, SS Wireless NDN [2] Data dissemination CQ, SS WSNs [49] Routing packets CQ, SS Cog.…”

Section: Scenariosmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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Section: Scenariosmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

View full text Add to dashboard Cite

show abstract

“…Consider k number of hash functions, m as the bloom filter size (16bit in Fig. 1) and maximum number of elements in D is P [16].…”

Section: Constructing Bloom Filtersmentioning

confidence: 99%

“…A distributed algorithm is used to encode provenance in an in-packet bloom filter [16], and a centralized algorithm is used by the base station to decode the provenance [10]. A unique sequence identification number is attached to the forwarded packet along with data value and an in-packet bloom filter which holds the provenance also transmitting the provenance graph vertices over an in-packet bloom filter.…”

Section: Provenance Collection and Encodingmentioning

confidence: 99%

Safe Diffusion of Provenance in Wireless Sensor Networks Using in-Packet Bloom Filter Provenance Encoding Technique

Lal¹,

Prathap²

2018

IJET

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Wireless Sensor Networks (WSN) comprises of tiny wireless sensor nodes for continuous observation of physical or environmental conditions. Sensor networks are increasingly deployed in decision-making infrastructures. They are widely used for battlefield monitoring systems and Supervisory Control and Data Acquisition (SCADA) systems. Making decision makers aware of the trust worthiness of the collected data is crucial. WSNs are used by Business Applications. These applications depend on trustworthy sensor data to control business processes. It is important to ensure the trustworthiness of the data generated from sensor nodes so that effective decisions can be made. Making decision makers aware of the trustworthiness of the collected data is crucial. WSNs are used by Business Applications. These applications depend on trustworthy sensor data to control business processes. We have proposed a different approach for provenance diffusion for WSN using Bloom filters. The major security attributes of the scheme are freshness, confidentiality and integrity. Experimental characteristics and results evaluating the scheme output the efficiency of the provenance encoding and its transmission.

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“…Under the CAPTRA scheme, when a node n i sends a packet p to n j , such information is not only recorded by n i and n j , but by the nearby nodes too. All nodes use the Bloom Filter [19] data structure to compactly store such provenance and provenance witness information. The provenance information is stored at the nodes in the packet path; the provenance witness information is recorded by the nearby nodes who have witnessed the packet transmission at some nodes in the packet path.…”

Section: Captramentioning

confidence: 99%

Provenance for Wireless Sensor Networks: A Survey

2016

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In wireless sensor networks (WSNs), provenance records the data source, forwarding, and aggregating information of data packets on their way to the base station. Provenance is critical for assessing the trustworthiness of the received data, diagnosing network failures, detecting early signs of attacks, etc. However, because the provenance size expands rapidly with the increase in packet transmission hops, the provenance schemes developed for use in wired computer networks are not generally applicable to WSNs. Therefore, specific provenance techniques have been developed for WSNs that take into account the constrained resources of sensor nodes. In this paper, we survey such techniques. Special focus in the paper is devoted to a systematic and comprehensive classification of the solutions proposed in the literature. We review each solution by highlighting its pros and cons. Finally, we discuss recent trends in provenance encoding schemes for WSNs.

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In-packet Bloom filters: Design and networking applications

Cited by 33 publications

References 26 publications

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Safe Diffusion of Provenance in Wireless Sensor Networks Using in-Packet Bloom Filter Provenance Encoding Technique

Provenance for Wireless Sensor Networks: A Survey

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