TY - JOUR
T1 - Bloom filter–based efficient broadcast algorithm for the Internet of things
AU - Talpur, Anum
AU - Shaikh, Faisal K.
AU - Newe, Thomas
AU - Sheikh, Adil A.
AU - Felemban, Emad
AU - Khelil, Abdelmajid
N1 - Publisher Copyright:
© The Author(s) 2017.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - bloom filter
KW - broadcast
KW - cluster
KW - Internet of things
UR - http://www.scopus.com/inward/record.url?scp=85039902234&partnerID=8YFLogxK
U2 - 10.1177/1550147717749744
DO - 10.1177/1550147717749744
M3 - Article
AN - SCOPUS:85039902234
SN - 1550-1329
VL - 13
SP - -
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
IS - 12
ER -