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Top1. Introduction
Internet of things (IoT) is a group of humans, services, and devices that are interconnected. They are able to communicate and share information that is needed to achieve an objective. The applications of IoT are in different domains like agriculture, transportation, military surveillance, fire monitoring, pollution monitoring, healthcare etc. IoT is an emerging technology in which users or programs can directly access the internet (Council, 2008; Atzori, 2010).
Radio Frequency Identification (RFID) and the Wireless Sensor Networks (WSNs) are the reason behind the rapid development of IoT over the past years. The basic mechanism of identification in IoT is served by the RFID which enables the stamping of devices. Each device becomes a wireless object due to WSN, and are able to communicate with the digital world (Koien, 2014).
WSN is the basic ingredient of IoT. Hence, it is to be handled in a very efficient manner. WSN have some resource constraints like buffer space, battery, etc. The lifetime of the sensor network is totally depending on the life of a battery. Hence, it is necessary to keep in mind the energy constraints of WSN. The most challenging requirement in IoT is security. The malicious nodes can hinder the process of cluster formation, can alter the results, drops the information received from other nodes, transmits multiple copies of same messages to the receiver which in turn consume more energy etc. hence, it is utmost important to provide security to WSN.
Clustering is a technique which is used to reduce the consumption of the energy by the sensor nodes (W. B. Heinzelman, 2002), balances the load among the network (Younis, 2003) which in turn again reduces the energy consumption and also helpful in key distribution (Cho, 2009). The grouping of nodes into small clusters is known as clustering. Each cluster needs a leader known as the cluster head (CH). The role of cluster head is very crucial. It collects data sensed by the sensor nodes, performs aggregation and transmits the aggregated data to the sink or base station. Here, malicious nodes may try to become CHs. There are two main schemes that prohibit the malicious nodes from being a CH. The first scheme is at the time of formation of clusters, malicious nodes are determined and they are separated from the cluster formation process. The second scheme is to keep the malicious nodes from altering the process of cluster head election. Both the schemes are known as secure clustering. The first scheme is most important for security in clustering. If the first scheme doesn’t work, then the second scheme is in danger and has malicious nodes attack. Several schemes have been proposed to protect the network from external attack (Oliveira, 2006; Liu, 2007). These schemes only prevent the network from external attacks; they cannot keep the malicious nodes to hinder the cluster formation process.
To solve the above issue, a novel and secure clustering scheme is proposed in which a trust value is calculated based on the node’s behavior. This trust value helps in preventing the compromised node from being elected as CH. The fuzzy logic is applied to select reliable CHs. The residual energy and the distance to the sink are also the parameters of this system along with trust. However, in case if any suspicious activity happens in the network then it can easily be detected by the cluster head through the authentication process.
The main contributions of this work are: