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In recent years, wind power is emerging as one of the most promising renewable energy sources. Yet, reliability is less due to the unplanned downtimes caused by faults in the wind turbines. Monitoring of turbine elements with existing wired technology is not economical and hence replacement with smart wireless sensor devices are rapidly emerging as the next evolution. In such a wireless scenario, maximizing network lifetime and reliability is essential. The recently proposed hybrid multihop routing protocol aims to improve the lifetime of a WSN deployed in a widely distributed network. Though its performance is revealed to be better, the chain based CH selection and routing of data through MST increases the energy consumption thereby reducing the network lifetime. With an objective to circumvent this, three novel multihop routing strategies namely, Shortest Grid Routing, Partitioned Super Grid Direct Routing, and Shortest Super Grid Routing are proposed. For different network scenarios, the proposals are analyzed through simulation and validated via real time experimentation. The performance of the proposals is evaluated using metrics such as first node death, energy consumption, stability, and network reliability and are compared with the recently proposed protocol. The results reveal enhanced network lifetime and reliability which proves the applicability of the proposed routing protocols for CPS based infrastructure network.

Node localisation plays a significant role in wireless sensor networks, as most of the applications require exact location of sensor nodes. To obtain the exact location of sensor nodes, a range-based localisation method based on Jaya optimisation using received signal strength indicator (RSSI) is proposed. The distance between the target node and the reference nodes is obtained from the measured RSSIs using regression-based log normal shadowing model. Further, improvement in the localisation accuracy is accomplished by formulating the location of target node as an optimisation problem. Jaya optimisation algorithm is adopted, as it is parameter-free and efficient. The Jaya algorithm is used for estimating the distance as well as the coordinates of the target node. The proposal is compared with particle swarm optimisation and validated through simulation and hardware experiments. The maximum localisation error using Jaya and particle swarm optimisation through simulation is observed to be 0.08 and 0.37 m, respectively. Real-time experiments using Jaya algorithm exhibited the maximum localisation error of about 0.14 m for indoor environment and 0.3 m for outdoor environment. The results of the proposed methodology show significant improvement in terms of accuracy.

Due to the emergence of digital revolution and competitiveness in recent decades, almost all organizations and industries intend to develop solutions to extract information from unstructured documents. These documents comprise of information related to multiple divergent domains, and therefore, there is a need of a multidomain knowledge base. Since recent research works suggest ontology as the predominant model, it is proposed to evolve a unified ontology modeling approach with multiple layers and divergent domains to support information processing from unstructured documents. The model is evolved by integrating relevant domains to facilitate cross domain query. Further as the features of unstructured documents span across multiple domains, domain identification is to be performed prior to any information processing. Hence, an attempt is made to identify the domain using the proposed ontology model. The proposed ontology is developed for the Thermal Power Plant Industry and domain identification is demonstrated with an example. A statistical similarity index is proposed to associate divergent volatile features of unstructured text with ontology knowledge for domain identification. The outcome of the proposal is evaluated using the proposed similarity index. A subsequent study to extract information using classified content with the support of directed acyclic graph relationship is under progress. The merit of the proposal is its ability to extend its usage across multiple stages of information processing with distinctive purpose.

Recent research works suggest cluster head reselection as a mechanism for network lifetime improvement. Cluster head reselection is either energy or time dependent. However, frequent cluster head reselection increases time overhead and energy consumption leading to reduction in network lifetime. Hence, an attempt is made to minimise the cluster head reselection frequency. The cluster head reselection is carried out whenever the energy of current cluster head falls below the threshold and this threshold corresponds to the minimum energy required for a node to survive. It is this selection of the threshold value that favours reduction in cluster head reselection frequency. Further, the cluster head reselection is based on energy drainage parameters such as the rate of recurrent communication of sensor node in addition to the residual energy of the node and distance of the node from the base station. To demonstrate the effect of rate of recurrent communication of sensor node on cluster head reselection, experiments are conducted both through simulation and hardware. Further, to study the effectiveness of the proposal, comparisons are made with other algorithms such as low energy adaptive clustering hierarchy (LEACH)-C, adaptive decentralised re-clustering protocol and T-LEACH. Reduced cluster head reselection frequency exhibits low energy consumption and time overhead with enhanced network lifetime.