QoS Enabled Layered Based Clustering for Reactive Flooding in the Internet of Things

Abstract

The Internet of Things has gained substantial attention over the last few years, because of connecting daily things in a wide range of application and domains. A large number of sensors require bandwidth and network resources to give-and-take queries among a heterogeneous IoT network. Network flooding is a key questioning strategy for successful exchange of queries. However, the risk of the original flooding is prone to unwanted and redundant network queries which may lead to heavy network traffic. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. IoT devices consume more energy and high computational time. More queries leads to consumption of more bandwidth, cost, and miserable QoS. Current existing approaches focused primarily on how to speed up the basic routing for IoT devices. However, solutions for flooding are not being addressed. In this paper, we propose a cluster-based flooding (CBF) as an interoperable solution for network and sensor layer devices which is also capable minimizing the energy consumption, cost, network flooding, identifying, and eliminating of redundant flooding queries using query control mechanisms. The proposed CBF divides the network into different clusters, local queries for information are proactively maintained by the intralayer cluster (IALC), while the interlayer cluster (IELC) is responsible for reactively obtain the routing queries to the destinations outside the cluster. CBF is a hybrid approach, having the potential to be more efficient against traditional schemes in term of query traffic generation. However, in the absence of appropriate redundant query detection and termination techniques, the CBF may generate more control traffic compared to the standard flooding techniques. In this research work, we used Cooja simulator to evaluate the performance of the proposed CBF. According to the simulation results the proposed technique has superiority in term of traffic delay, QoS/throughput, and energy consumption, under various performance metrics compared with traditional flooding and state of the art.