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

Abstract

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.

Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario

“…Amoretti et al [43] proposed a distributed naming service for the IoT that relies on BFs to generate compact names from node descriptions, while Singh et al [44] proposed the Accommodative Bloom filter approach to deal with massive data streaming from IoT sensor devices. In [45], the authors deal with an efficient broadcasting mechanism in IoT networks. In particular, they used BFs to prevent IoT nodes from being flooded with unwanted packets previously received.…”