Implementation of Connected Dominating Set in Fog Computing Using Knowledge-Upgraded IoT Devices

Abstract

Wireless sensor networks (WSNs) have a worldwide attraction because of its increasing popularity. The key enablers for the Internet of Things (IoT) are WSN, which plays an important role in future by collecting information through the cloud. Fog Computing, the latest innovations, connects sensor-based IoT devices to the cloud. Fog Computing is a decentralized computing infrastructure in which the data, compute, storage, and applications are distributed efficiently between the data source and the cloud. The main aim of Fog Computing is to reduce the amount of data transported to the cloud and hence increase the efficiency. The knowledge-upgraded IoT devices will be embedded with a piece of software into it, which can able to understand the Distributed Denial of Service (DDoS). Such attacks are not forwarded to the cloud and thus the cloud server down problem is avoided. The IoT devices enabled with such knowledge is connected together to form a Connected Dominating Set (CDS). The data routed through only such IoT devices will be directly connected to the cloud. The CDS-based approach reduces the search for a minimum group of IoT devices called nodes, thus forming the backbone network. Various CDS algorithms have been developed for constructing CDSs with minimum number of nodes. However, most of the research work does not focus on developing a CDS based on application and requirement. In this chapter, a Gateway-based Strategic CDS (GWS-CDS) is constructed based on strategy and communication range. Here, any node in the network assigned a critical communication range, which is in a strong neighbourhood and which is within the communication range of more than one network, will be selected as the starting node, instead of the node with maximum connectivity. If a node is not within a critical communication range, then the following factors will be increased: the number of nodes that locally compete over a shared channel, access delay, network throughput and network partitioning. The other nodes for CDS construction are selected based on density and velocity. The focus of this research work was to construct a CDS in heterogeneous networks. The algorithm was tested with respect to three metrics—average CDS node size, average CDS Edge Size and average hop count per path. Simulation results showed that the proposed algorithm was better when compared to the existing algorithms.