Dependability Evaluation of Wireless Sensor Networks

Abstract

The performance of a network is dependent on the qualitative and quantitative features, which are closely tied to the Quality of Service (QoS). The QoS determines the characteristics of a network required for its effective functioning. The QoS encompasses many aspects of the network such as dependability, scalability, fault recovery, energy efficiency, packet loss ratio. The most important aspect of QoS is dependability and hence dependability evaluation of a network is obligatory to investigate the perilous aspect that affects the faultless functioning of the network. This research work focuses on the reliability and security aspect of dependability. Reliability is defined as the measure of the continuity of correct service . It is the most quantifiable feature of network design. Security is defined as the judgment of how likely it is that the network can resist accidental or deliberate intrusions . The Wireless Sensor Networks (WSNs) are capable of monitoring the dynamically changing environment in a particular timespan. The data collected by this network consists of unexpected and complex patterns. To understand these patterns, Machine Learning plays a vital role. ML algorithms facilitate in discovering important correlations in the collected data and hence provide improved deployment strategy. The main focus of this research work is dedicated to the analysis of various dependability evaluation techniques in WSN. Also, an ML-based framework is proposed to optimize the data flow parameter of the network. The data flow is a vital parameter that affects the QoS of a network. This dissertation proposes a novel ML-based framework, which predicts the overall reliability of WSN in terms of performance metrics such as, sent packets, received packets, packets forfeit, packet delivery ratio, and throughput.