Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people's lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems' safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people's lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network.

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A learner in an informal learning environment, such as a museum, encounters various challenges. After initial assessment, a set of methods were proposed that may enhance a learner’s experience in a museum using computer aided technologies. The most important insight was the need to support the museum visitor in three phases of activity: prior to the visit, during the visit, and after the visit. We hypothesized that software tools that could help connect these three phases would be helpful and valuable supports for the visitor. To test and evaluate our hypothesis, a system called “The Museum Explorer” was built and instantiated using the collection in the Museum of Antiquities located at the University of Saskatchewan. An evaluation of the Museum Explorer was conducted. Results show that the Museum Explorer was largely successful in achieving our goals. The Museum Explorer is an integrated solution for visitors in museums across the pre-visit, visit, and post-visit phases. The Museum Explorer was designed to provide a means to connect and transfer user experience across the major phases of a museum visit. For each phase of a visitor’s experience, a set of tools was built that provides intelligent and interactive communication features. To assist visitors selecting artefacts to visit, a recommender system allows users to select a set of constraints. To better manage interactivity, features and functions were offered based on context. A study was conducted with volunteer museum visitors. Results from the study show that the Museum Explorer is a useful support. Analysis of the usage data captured by the Museum Explorer has revealed some interesting facts about users’ preferences in the domain that can be used by future researchers.

Context-aware computing envisions a new generation of smart applications that have the ability to perpetually sense the user’s context and use these data to make adaptation decision in response to changes in the user’s context so as to provide timely and personalized services anytime and anywhere. Unlike the traditional distribution systems where the network topology is fixed and wired, context-aware computing systems are mostly based on wireless communication due to the mobility of the network nodes; hence the network topology is not fixed but changes dynamically in an unpredictable manner as nodes join and the leave network, in addition to the fact that wireless communication is unstable. These factors make the design and development of context-aware computing systems much more challenging, as the system requirements change depending on the context of use. The Unified Modelling Language (UML) is a graphical language commonly used to specify, visualize, construct, and document the artefacts of software-intensive systems. However, UML is an all-purpose modelling language and does not have notations to distinguish context-awareness requirements from other system requirements. This is critical for the specification, visualization, construction and documentation of context-aware computing systems because context-awareness requirements are highly important in these systems. This thesis proposes an extension of UML diagrams to cater for the specification, visualization, construction and documentation of context-aware computing systems where new notations are introduced to model context-awareness requirements distinctively from other system requirements. The contributions of this work can be summarized as follows: (i) A context-aware use case diagram is a new notion which merges into a single diagram the traditional use case diagram (that describes the functions of an application) and the use context diagram, which specifies the context information upon which the behaviours of these functions depend. (ii) A Novel notion known as a context-aware activity diagram is presented, which extends the traditional UML activity diagrams to enable the representation of context objects, context constraints and adaptation activities. Context constraints express conditions upon context object attributes that trigger adaptation activities; adaptation activities are activities that must be performed in response to specific changes in the system’s context. (iii) A novel notion known as the context-aware class diagram is presented, which extends the traditional UML class diagrams to enable the representation of context information that affect the behaviours of a class. A new relationship, called utilisation, between a UML class and a context class is used to model context objects; meaning that the behaviours of the UML class depend upon the context information represented by the context class. Hence a context-aware class diagram is a rich and expressive language that distinctively depicts both the structure of classes and that of the contexts upon which they depend. The pragmatics of the proposed approach are demonstrated using two real-world case studies.

There are still many challenges in the field of VANETs that encouraged researchers to conduct further investigation in this field to meet these challenges. The issue pertaining to routing protocols such as delivering the warning messages to the vehicles facing Non-Line of Sight (NLOS) situations without causing the storm problem and channel contention, is regarded as a serious dilemma which is required to be tackled in VANET, especially in congested environments. This requires the designing of an efficient mechanism of routing protocol that can broadcast the warning messages from the emergency vehicles to the vehicles under NLOS, reducing the overhead and increasing the packet delivery ratio with a reduced time delay and channel utilisation. The main aim of this work is to develop the novel routing protocol for a high-density environment in VANET through utilisation of its high mobility features, aid of the sensors such as Global Positioning System (GPS) and Navigation System (NS). In this work, the cooperative approach has been used to develop the routing protocol called the Co-operative Volunteer Protocol (CVP), which uses volunteer vehicles to disseminate the warning message from the source to the target vehicle under NLOS issue; this also increases the packet delivery ratio, detection of NLOS and resolution of NLOS by delivering the warning message successfully to the vehicle under NLOS, thereby causing a direct impact on the reduction of collisions between vehicles in normal mode and emergency mode on the road near intersections or on highways. The cooperative approach adopted for warning message dissemination reduced the rebroadcast rate of messages, thereby decreasing significantly the storm issue and the channel contention. A novel architecture has been developed by utilising the concept of a Context-Aware System (CAS), which clarifies the OBU components and their interaction with each other in order to collect data and take the decisions based on the sensed circumstances. The proposed architecture has been divided into three main phases: sensing, processing and acting. The results obtained from the validation of the proposed CVP protocol using the simulator EstiNet under specific conditions and parameters showed that performance of the proposed protocol is better than that of the GRANT protocol with regard to several metrics such as packet delivery ratio, neighbourhood awareness, channel utilisation, overhead and latency. It is also successfully shown that the proposed CVP could detect the NLOS situation and solves it effectively and efficiently for both the intersection scenario in urban areas and the highway scenario.

The technological advances in wireless networks, smart phones, social networks, embedded sensor technologies and the wide spread of mobile devices have exposed customers to number of services. With these advanced technological innovations, computing capability and connectivity, customers look forward to get useful services and information by means of their mobile devices anywhere and at anytime thereby saving time and money. The importance of mobile commerce is increasing every day, since the mobile devices are becoming central part of our lives. One of the challenge here is the ability to reinforce the application behavior by utilizing context information. The analysis and utilization of context information is necessary to build intelligent applications and thereby focusing on less user attention by understanding the current situation. The information about the current location of a mobile customer, the time of request, and personal characteristics like nature of work, profession and economic status are utilized by applications to provide accurate context-aware services. Many of context-aware applications focus mainly on user activity, preferences which are not sufficient to provide context-aware intelligent services. However, there are few works in developing an integrated model for analyzing the context information. Many authors describe the context representation using five elements like who, what, where, when and how to provide a user centric view without emphasizing on the feature of generality and structural representation of context parameters. Since context information takes multiple forms, it is essential and necessary to make broad classification which helps application designers and developers to deal with possible contexts and their impact on application behavior. In addition, the context classification enables to understand complex customer situations due to changing mobile environment. Therefore, it is required to design a model which analyzes the context information of customers in a dynamic environment and hence, provide real time accurate service. The design and development of context-aware mobile commerce services requires a general conceptual model which can handle any type of context information for different applications. Due to the dynamic nature of business environment and also the customer preferences keep emerging, there is a need to develop business model which adapts to changing environment. To understand the current situation of customers in such highly dynamic environments and to enable the business transactions quickly among parties involved, it is essential to construct and analyze an integrated view of information from distinct sources. The adaptation of the provided services based on customer needs depends when the relevant context information is self-described in the form of beliefs. The observations made on combination of context information are deduced into beliefs as a result the decision making time to provide service reduces considerably. The aim of the thesis, is to design and develop a context-aware system which has been applied for mobile commerce environment by considering the customer context information. To do this, we have designed two models: the Context-Information, Observation and Belief (C-IOB)model and the commercial business model. The main function of C-IOB model is to support the application to identify a suitable context-aware service and to execute business transaction for a customer by analyzing the context information in the form of beliefs. We classify the used Context Information(CI)into four types: physical, system, application and social environment. The business model discusses the formal description of participants who are involved in commercial business. The C-IOB model The Context Information, Observation and Belief model deduces beliefs on customer, by combination of available context information during transaction execution or service provision. The beliefs represent the various situations of customers based on specific nature of the applications. The beliefs developed about an entity(e.g. ,person, place, thing) are primitive in most theories of decision making so that applications can use these beliefs to identify and execute context-aware services. The C-IOB model reduces the solution search space, since the knowledge about an entity is organized using cognitive factors, which maps user context information into real world observations and beliefs, as a result the decision making time by the system reduces considerably. The five CI -constructs: who, what, where, when and how enable many types of context information acquisition in our work. The commercial business model A commercial business model mainly involves the formal description of business participants who are involved in commercial business transactions. To meet the growing needs and to fulfill high expectations of customers, it is very important that application designers have to address the issue of building viable business models for commercial environment. The business model describes how an enterprise or an organization captures and delivers economical value to customers. In particular, the customers play major role in transactions execution and hence understanding and identifying the needs of customer is an important issue in building the business model for commercial purpose. Mobile Commerce Environment Mobile commerce environment (MCE) is one of the specific forms of commercial business model. It deals with transactions like purchases with the objective of supplying commodities like goods and services to customers using mobile devices. A Mobile commerce environment is established by considering set of customers, vendors and bankers, who are involved in commercial business activities like buying of electronic goods and services. The environment is based on certain factors like the type of customers, the type of transactions and the type of electronic products involved in purchasing. M-commerce is one of the most effective and useful ways of conducting business as the customers are constantly using mobile devices and it significantly assists customers in making transactions anywhere, anytime and thereby improving the customer satisfaction. C-IOB model based mobile commerce environment Mobile commerce applications are required to be flexible, in providing services due to inherent anytime and anywhere paradigm. The traditional systems during commercial business transactions, take large time to search the right product, which leads to more utilization of device resources like memory and computational power. In addition, the users are striked with more information which is not significant. Therefore, there is a need to design applications, which identify the customer requirements by acquiring the context information from distinct sources and thereby providing timely and specific information or services to the customer. Hence, to adapt services based on various situations, it is required to develop a systematic approach for representation and utilization of context information modified to special characteristics of mobile commerce applications. The mobile commerce environment uses C-IOB model for analyzing the relevant context information of customers and the context based beliefs helps to identify a suitable service for an end customer, thereby reducing mobile transaction execution time and providing customer benefits. The simulation environment consists of 10,000 electronic products which are categorized based on technical and commercial features. The simulation is carried out with ten mobile commerce transactions, the database consists of information about500 customers and 100vendors who deal with electronic products selling and10bankersfor money transactions. The customer context information is acquired from physical, system, application and social environment. When customers ends are quest for a service, the current context information is acquired, a set of observations is formulated and belief is deduced. The customer is provided with relevant information or service based on deduced belief. The simulation exhaustively tests the working of the system for mobile commerce transactions under different context environments. Some of the case studies are designed by applying the C-IOB model which includes placing the purchase order for the product, money transfer and after sales service. In summary, we have developed a context-aware system by using C-IOB model and also the design of commercial business model. To evaluate the performance of the system, we have incorporated context-awareness aspect for 10 business transactions applied to mobile commerce. The proposed system using C-IOB model provides customer required services as accurately as possible. The system has the capability to adapt to real time situation of customer needs, thereby enhancing the customer satisfaction. The simulation results have shown that the time to execute mobile commerce transactions is less using context based beliefs compared to context unaware approach. The accuracy of the system with belief based approach is higher than without context information. The customer benefits in business transaction are also enhanced by our design approach.

Context-aware systems use data about their environment for adaptation at runtime, e.g., for optimization of power consumption or user experience. Ontology-based data access (OBDA) can be used to support the interpretation of the usually large amounts of data. OBDA augments query answering in databases by dropping the closed-world assumption (i.e., the data is not assumed to be complete any more) and by including domain knowledge provided by an ontology. We focus on a recently proposed temporalized query language that allows to combine conjunctive queries with the operators of the well-known propositional temporal logic LTL. In particular, we investigate temporalized OBDA w.r.t. ontologies in the DL EL, which allows for efficient reasoning and has been successfully applied in practice. We study both data and combined complexity of the query entailment problem.

The insider threat problem is extremely challenging to address, as it is committed by insiders who are trusted and authorized to access the information resources of the organization. The problem is further complicated by the multifaceted nature of insiders, as human beings have various motivations and fluctuating behaviours. Additionally, typical monitoring systems may violate the privacy of insiders. Consequently, there is a need to consider a comprehensive approach to mitigate insider threats. This research presents a novel insider threat prevention and prediction model, combining several approaches, techniques and tools from the fields of computer science and criminology. The model is a Privacy- Preserving, Context-Aware, Insider Threat Prevention and Prediction model (PPCAITPP). The model is predicated on the Fraud Diamond (a theory from Criminology) which assumes there must be four elements present in order for a criminal to commit maleficence. The basic elements are pressure (i.e. motive), opportunity, ability (i.e. capability) and rationalization. According to the Fraud Diamond, malicious employees need to have a motive, opportunity and the capability to commit fraud. Additionally, criminals tend to rationalize their malicious actions in order for them to ease their cognitive dissonance towards maleficence. In order to mitigate the insider threat comprehensively, there is a need to consider all the elements of the Fraud Diamond because insider threat crime is also related to elements of the Fraud Diamond similar to crimes committed within the physical landscape. The model intends to act within context, which implies that when the model offers predictions about threats, it also reacts to prevent the threat from becoming a future threat instantaneously. To collect information about insiders for the purposes of prediction, there is a need to collect current information, as the motives and behaviours of humans are transient. Context-aware systems are used in the model to collect current information about insiders related to motive and ability as well as to determine whether insiders exploit any opportunity to commit a crime (i.e. entrapment). Furthermore, they are used to neutralize any rationalizations the insider may have via neutralization mitigation, thus preventing the insider from committing a future crime. However, the model collects private information and involves entrapment that will be deemed unethical. A model that does not preserve the privacy of insiders may cause them to feel they are not trusted, which in turn may affect their productivity in the workplace negatively. Hence, this thesis argues that an insider prediction model must be privacy-preserving in order to prevent further cybercrime. The model is not intended to be punitive but rather a strategy to prevent current insiders from being tempted to commit a crime in future. The model involves four major components: context awareness, opportunity facilitation, neutralization mitigation and privacy preservation. The model implements a context analyser to collect information related to an insider who may be motivated to commit a crime and his or her ability to implement an attack plan. The context analyser only collects meta-data such as search behaviour, file access, logins, use of keystrokes and linguistic features, excluding the content to preserve the privacy of insiders. The model also employs keystroke and linguistic features based on typing patterns to collect information about any change in an insider’s emotional and stress levels. This is indirectly related to the motivation to commit a cybercrime. Research demonstrates that most of the insiders who have committed a crime have experienced a negative emotion/pressure resulting from dissatisfaction with employment measures such as terminations, transfers without their consent or denial of a wage increase. However, there may also be personal problems such as a divorce. The typing pattern analyser and other resource usage behaviours aid in identifying an insider who may be motivated to commit a cybercrime based on his or her stress levels and emotions as well as the change in resource usage behaviour. The model does not identify the motive itself, but rather identifies those individuals who may be motivated to commit a crime by reviewing their computer-based actions. The model also assesses the capability of insiders to commit a planned attack based on their usage of computer applications and measuring their sophistication in terms of the range of knowledge, depth of knowledge and skill as well as assessing the number of systems errors and warnings generated while using the applications. The model will facilitate an opportunity to commit a crime by using honeypots to determine whether a motivated and capable insider will exploit any opportunity in the organization involving a criminal act. Based on the insider’s reaction to the opportunity presented via a honeypot, the model will deploy an implementation strategy based on neutralization mitigation. Neutralization mitigation is the process of nullifying the rationalizations that the insider may have had for committing the crime. All information about insiders will be anonymized to remove any identifiers for the purpose of preserving the privacy of insiders. The model also intends to identify any new behaviour that may result during the course of implementation. This research contributes to existing scientific knowledge in the insider threat domain and can be used as a point of departure for future researchers in the area. Organizations could use the model as a framework to design and develop a comprehensive security solution for insider threat problems. The model concept can also be integrated into existing information security systems that address the insider threat problem / Information Science / D. Phil. (Information Systems)