A reusable application framework for context-aware mobile patient monitoring systems

The development of Context-aware Mobile Patient Monitoring Systems (CaMPaMS) using wireless sensors is very complex. To overcome this problem, the Context-aware Mobile Patient Monitoring Framework (CaMPaMF) was introduced as an ideal reuse technique to enhance the overall development quality and overcome the development complexity of CaMPaMS. While a few studies have designed reusable CaMPaMFs, there has not been enough study looking at how to design and evaluate application frameworks based on multiple reusability aspects and multiple reusability evaluation approaches. Furthermore, there also has not been enough study that integrates the identified domain requirements of CaMPaMS. Therefore, the aim of this research is to design a reusable CaMPaMF for CaMPaMS. To achieve this aim, twelve methods were used: literature search, content analysis, concept matrix, feature modelling, use case assortment, domain expert review, model-driven architecture approach, static code analysis, reusability model approach, prototyping, amount of reuse calculation, and software expert review. The primary outcome of this research is a reusable CaMPaMF designed and evaluated to capture reusability from different aspects. CaMPaMF includes a domain model validated by consultant physicians as domain experts, an architectural model, a platform-independent model, a platform-specific model validated by software expert review, and three CaMPaMS prototypes for monitoring patients with hypertension, epilepsy, or diabetes, and multiple reusability evaluation approaches. This research contributes to the body of software engineering knowledge, particularly in the area of design and evaluation of reusable application frameworks. Researchers can use the domain model to enhance the understanding of CaMPaMS domain requirements, thus extend it with new requirements. Developers can also reuse and extend CaMPaMF to develop various CaMPaMS for different diseases. Software industries can also reuse CaMPaMF to reduce the need to consult domain experts and the time required to build CaMPaMS from scratch, thus reducing the development cost and time

Availability estimation and management for complex processing systems

“Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization