Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Abstract

Wireless sensor nodes help people to monitor any operational mechanism. It is important to be implemented in industrial automation in detecting unauthorized activity or malicious sensor nodes by the outsider. However, a massive number of devices or things which are needed to be controlled become a challenge. A fast mechanism to recognize a member or a valid device is needed to minimize production costs and avoid future procedural errors. It becomes an open problem for the implementation of massive scale Wireless Sensor Networks (WSN). Hence, a scalable two-dimensional Bloom filter is proposed in this paper to deal with this issue. The low computation complexity and the storage efficiency requirements by the two-dimensional Bloom filter are proven by the experimental results. The insertion, deletion and query procedure of the proposed method only need O (1), while the storage usage of two-dimensional Bloom filters not only lower than counting Bloom filter in average 131 bits difference but also approaches accommodative Bloom filter in about 10 bits difference. These two parameters support a fast membership scheme. Furthermore, the analysis of value initialization has been performed. To the best of our knowledge, the investigation of this parameter has not been addressed by other studies. This process aims to get the best scenario to increase scalability. Several recommendations for selecting dimension number has been stated which is useful for efficient storage usage and reduce false positive.

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“…Bloom filter is a simple and space-saving and probabilistic data structure to represent groups in member search (Afianti et al, 2021). Bloom filter was first introduced by Burton Bloom in 1970 and immediately gained popularity in database applications.…”

Secure and Space Efficient Accounts Storage System Using Three-Dimensional Bloom Filter

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