Hazard and early warning analysis based on domain specific modeling technologies

An aim of proactive risk management strategies is the timely identification of safety related risks. One way to achieve this is by deploying early warning systems. Early warning systems aim to provide useful information on the presence of potential threats to the system, the level of vulnerability of a system, or both of these, in a timely manner. This information can then be used to take proactive safety measures. The United Nation’s has recommended that any early warning system need to have four essential elements, which are the risk knowledge element, a monitoring and warning service, dissemination and communication and a response capability. This research deals with the risk knowledge element of an early warning system. The risk knowledge element of an early warning system contains models of possible accident scenarios. These accident scenarios are created by using hazard analysis techniques, which are categorised as traditional and contemporary. The assumption in traditional hazard analysis techniques is that accidents are occurred due to a sequence of events, whereas, the assumption of contemporary hazard analysis techniques is that safety is an emergent property of complex systems. The problem is that there is no availability of a software editor which can be used by analysts to create models of accident scenarios based on contemporary hazard analysis techniques and generate computer code that represent the models at the same time. This research aims to enhance the process of generating computer code based on graphical models that associate early warning signs and causal factors to a hazard, based on contemporary hazard analyses techniques. For this purpose, the thesis investigates the use of Domain Specific Modeling (DSM) technologies. The contributions of this thesis is the design and development of a set of three graphical Domain Specific Modeling languages (DSML)s, that when combined together, provide all of the necessary constructs that will enable safety experts and practitioners to conduct hazard and early warning analysis based on a contemporary hazard analysis approach. The languages represent those elements and relations necessary to define accident scenarios and their associated early warning signs. The three DSMLs were incorporated in to a prototype software editor that enables safety scientists and practitioners to create and edit hazard and early warning analysis models in a usable manner and as a result to generate executable code automatically. This research proves that the DSM technologies can be used to develop a set of three DSMLs which can allow user to conduct hazard and early warning analysis in more usable manner. Furthermore, the three DSMLs and their dedicated editor, which are presented in this thesis, may provide a significant enhancement to the process of creating the risk knowledge element of computer based early warning systems.

Early warning sign attributes for the safety of drinking water treatment works

A proactive risk management strategy seeks to prevent accidents from taking place and maintain the safety of a system. In this context, the task of identifying and disseminating early warning signs and signals is among the most important. The problem is that warning signs that are present before an accident takes place are often being overlooked and not picked up or identified as warning signs. If these warning signs were responded to, then an accident may be averted. Accidents occuring in the critical domain of a drinking water treatments works can have serious implications for the public health of consumers of the water supplied. Realising and comprehending early warning signs is a major challenge for the domain of systems safety and especially in the domain of a water treatment works. The approaches that are typically used to enhance the realisation, comprehension and dissemination of early warning signs in the water treatment domain in Ireland mainly involves the creation of accident scenarios, the use of monitoring data and procedures for the dissemination of warnings. While all of these approaches are all useful to inform the mental or process models of possible accident scenarios, nevertheless, accidents are still occurring in this domain. Therefore, a new approach to enhance the comprehension of and effective dissemination of early warning signs is required in order to improve safety and proactive risk management strategies. The contributions of this thesis is the provision of a set of attributes associated with the early warning sign concept that provides meaningful data on the early warning signs and allows recipients to better comprehend them. The values of these attributes were customised for application in the water treatment domain. This research proves that early warning signs at a water treatment works received with information on their attributes are comprehended and communicated more effectively and efficiently than the usual pragmatic approach and thereby improves the safety and proactive risk management strategies.

Energy-aware dynamic route management for THAWS

In this research we focus on the Tyndall 25mm and 10mm nodes energy-aware topology management to extend sensor network lifespan and optimise node power consumption. The two tiered Tyndall Heterogeneous Automated Wireless Sensors (THAWS) tool is used to quickly create and configure application-specific sensor networks. To this end, we propose to implement a distributed route discovery algorithm and a practical energy-aware reaction model on the 25mm nodes. Triggered by the energy-warning events, the miniaturised Tyndall 10mm data collector nodes adaptively and periodically change their association to 25mm base station nodes, while 25mm nodes also change the inter-connections between themselves, which results in reconfiguration of the 25mm nodes tier topology. The distributed routing protocol uses combined weight functions to balance the sensor network traffic. A system level simulation is used to quantify the benefit of the route management framework when compared to other state of the art approaches in terms of the system power-saving.