Bloom filter based data collection algorithm for wireless sensor networks

Talpur, Anum; Newe, Thomas; Shaikh, Faisal Karim; Sheikh, Adil A.; Felemban, Emad; Khelil, Abdelmajid

doi:10.1109/icoin.2017.7899458

Cited by 8 publications

(9 citation statements)

References 23 publications

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“…This algorithm also facilitates parallel broadcasting in each cluster using a small number of filter bits. The distributed bloom filter algorithm is an extension of the bloom algorithm published in the study of Talpur et al, 11 and it has reduced energy consumption by a factor of 1/10. When compared to nonbloom broadcasting algorithms, like LEBCP, 24 LERBT, 25 it reduces energy consumption by a factor of 1/100.…”

Section: Resultsmentioning

confidence: 99%

“…The extended work uses clusters to enhance the efficiency of a broadcasting for IoT applications. The enhanced work is compared with previous work, 11 and it is shown to be more efficient in terms of memory space, overhead, and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Energy consumed by IoT nodes in transmission is much greater than in reception as shown by Zhao et al 9 and Healy et al 10 Therefore, controlling rebroadcast transmissions to neighbors can significantly help in improving network performance in terms of increasing network lifetime and energy efficiency. This work is an extension of Talpur et al 11 that proposed a design of an efficient broadcasting mechanism for data collection, which works to control broadcast overhead and subsequently results in a more energy-efficient network.…”

Section: Introductionmentioning

confidence: 99%

“…This helps to prevent those nodes from being flooded with unwanted packets previously received. The proposed mechanism in the study of Talpur et al 11 was implemented in MATLAB, and performance results were compared with existing common broadcasting mechanisms. The extended version of this work presents a distributed network broadcasting solution, and it is compared with the previously proposed version of a bloom-based broadcast presented in the study of Talpur et al 11 In this extended article, a concept of distributed bloom-based broadcast is proposed for IoT networks.…”

Section: Introductionmentioning

confidence: 99%

“…A memory-efficient broadcasting technique is the goal of the proposed work. This work has resulted in a more efficient solution for large networks with respect to the one proposed previously in the study of Talpur et al 11 The concept in this work is to partition a large IoT network of nodes into a number of small networks, called clusters. A cluster head (CH) is elected 13 or selected to manage nodes locally within each cluster.…”

Section: Introductionmentioning

confidence: 99%

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Bloom filter–based efficient broadcast algorithm for the Internet of things

Talpur

Shaikh

Newe

et al. 2017

International Journal of Distributed Sensor Networks

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In the Internet of things, a large number of objects can be embedded over a region of interest where almost every device is connected to the Internet. This work scrutinizes the broadcast overhead problem in an Internet of things network, containing a very large number of objects. The work proposes a probabilistic structure (bloom filter)-based technique, which uses a new broadcast structure that attempts to reduce the number of duplicate copies of a packet at every node. This article utilizes a clustering concept to make the broadcast efficient in terms of memory space, broadcast overhead, and energy usage. The unique idea of a bloom-based network uses a filter to incorporate neighbor information when taking a forwarding decision to reduce broadcast overhead. The simulation results show that parallel broadcasting among different clusters and the use of a bloom filter can achieve a reduction in broadcast overhead from hundreds to ones and tens, when compared with a conventional non-bloom-based broadcast algorithm and a bloom-based algorithm. In addition, it helps to reduce energy usage evenly throughout the network, 1/100 times, when compared with conventional broadcast (non-bloom-based) and, 1/10 times, when compared with bloom-based broadcast. This increases the lifetime of a network by having control over network density usage and communications overhead as a result of broadcasting.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%