Developing multi-tier network design for effective energy consumption of cluster head selection in WSN / Wan Isni Sofiah Wan Din … [et al.]

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy

Asset integrity case development for normally unattended offshore installations

This thesis proposes the initial stages of the development of a NUI – Asset Integrity Case (Normally Unattended Installation). An NUI – Asset Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed. A key driver for improved asset integrity monitoring is centred on the level of accurate reporting of incidents. This stems from incidents to key offshore systems and areas. For example, gas turbine driven generators where 22% of fuel gas leaks were undetected with 60% of these 22% having been found to have ignited. Accordingly, there is a need for dynamic risk assessment and improved asset integrity monitoring. The immediate objective of this research is to investigate how a dynamic risk model can be developed for an offshore system. Subsequently, two dynamic risk assessment models were developed for an offshore gas turbine driven electrical power generation system. Bayesian Networks provided the base theory and algorithms to develop the models. The first model focuses on the consequences of one component failure. While the second model focuses on the consequences of a fuel gas release with escalated fire and explosion, based upon several initiating failures. This research also provides a Multiple Attribute Decision Analysis (MADA) to determine the most suitable Wireless Sensor Network (WSN) configuration for asset integrity monitoring. The WSN is applied to the same gas turbine system as in the dynamic risk assessment models. In the future, this work can be expanded to other systems and industries by applying the developed Asset Integrity Case framework and methodology. The framework outlines the steps to develop a dynamic risk assessment model along with MADA for the most suitable remote sensing and detection methods