Situation Awareness Measurement Enhanced for Efficient Monitoring in the Internet of Things

Abstract

The Internet of Things (IoT) is a heterogeneous network of objects that communicate with each other and their owners over the Internet. In the future, the utilization of distributed technologies in combination with their object applications will result in an unprecedented level of knowledge and awareness, creating new business opportunities and expanding existing ones. However, in this paradigm where almost everything can be monitored and tracked, an awareness of the state of the monitoring systems' situation will be important. Given the anticipated scale of business opportunities resulting from new object monitoring and tracking capabilities, IoT adoption has not been as fast as expected. The reason for the slow growth of application objects is the immaturity of the standards, which can be partly attributed to their unique system requirements and characteristics. In particular, the IoT standards must exhibit efficient self-reliant management and monitoring capability, which in a hierarchical topology is the role of cluster heads. IoT standards must be robust, scalable, adaptable, reliable, and trustworthy. These criteria are predicated upon the limited lifetime, and the autonomous nature, of wireless personal area networks (WPANs), of which wireless sensor networks (WSNs) are a major technological solution and research area in the IoT. In this paper, the energy efficiency of a self-reliant management and monitoring WSN cluster head selection algorithm, previously used for situation awareness, was improved upon by sharing particular established application cluster heads. This enhancement saved energy and reporting time by reducing the path length to the monitoring node. Also, a proposal to enhance the risk assessment component of the model is made. We demonstrate through experiments that when benchmarked against both a power and randomized cluster head deployment, the proposed enhancement to the situation awareness metric used less power. Potentially, this approach can be used to design a more energy efficient cluster-based management and monitoring algorithm for the advancement of security, e.g. Intrusion detection systems (IDSs), and other standards in the IoT.