The allocation of resources to tasks in a computationally efficient manner is a key problem in computer science. One important application domain for solutions to this class of problem is the allocation of sensing resources for environmental monitoring, surveillance, or similar sensing tasks. Within this domain, however, the complexity of the problem is compounded by a number of factors: new tasks may arrive at any time, resources may be shared between tasks under some conditions, tasks may be composed of inter-dependent sub-tasks, and tasks may compete for sensor resources. These factors combined with the dynamic nature of the topology of sensor networks (e.g. sensors may move out of range or become damaged) mean that it is extremely difficult or impossible to have a solution using existing techniques. In this thesis, we propose an efficient, agent-based solution (FAST for Flexible Allocation for Sensing Tasks) to this complex dynamic problem. The sensing resources in FAST can be either static or mobile or a mixture of both. Particularly, each resource is managed by a task leader agent (i.e. the actual sensor that is closest to the task central point). The problem is then modelled as a coordination problem where the task agents employ a novel multi-round Knapsack-based algorithm (GAP-E) to obtain a solution. If there are dependencies between sub-tasks, such relationships are solved prior to the actual allocation. At execution time, if there is any environment change that affects the task sensing type requirements, the previously determined sensor types for tasks are revised. When applicable, the agents are cooperative through exchanging and sharing resources to maximise their profits. In addition, FAST addresses the situation where sensor resource sharing is not possible and there is no incentive for sensor resources to be exchanged. In such situations, an additional post-process step underpinned by mechanism for exchanging resources through negotiation were introduced. Through those mechanisms, agents may, in a decentralized manner, decide the means to deliver on a sensing task given local conditions, and to alleviate the impact of task arrival time on the quality of the global solution. Via empirical evaluation, these steps significantly improved the number of sensing tasks that can be successfully completed with only a minor impact on execution time.

abstract: Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines. / Dissertation/Thesis / Masters Thesis Computer Science 2014

Grocery shopping is a routine task that people perform to fulfill their needs for food. We suspect many people would like to do grocery shopping with the grocery list to save their money and time. While creating a grocery list, people have to follow some steps such as checking the ingredients inventory available in their homes, planning meals for few days or weeks, creating a grocery list based on their meal plan and ingredients inventory status, and looking out for deals or offers, which can be utilized in their grocery purchases. These steps can be repetitive and involve people’s manual effort and a considerable amount of time to carry out effectively that makes the creation of a grocery list difficult to accomplish every time considering people’s busy modern lifestyles. As many grocers begin to leverage technology, they have an opportunity to understand the relationship between the people buying behavior from their purchasing history and stores’ grocery information to make profit-driven decisions and promote the reduction of food waste in stores. This thesis presents SmartGrocer, a context-aware personalized grocery system that dynamically gathers user context including their past purchase history and budget, and store context including clearance grocery inventory that consists of those ingredients that are soon-to-expire or being on sale to recommend personalized coupons to users. The personalized coupons are automatically applied to the missing ingredients of recipes thereby reducing the recipes’ cost and recommending them according to the user’s food budget. Recommendation of personalized coupons to users is an effective promotional strategy to not only saving the user’s money but also promoting the reduction of food waste in stores, which eventually drives more profit to the grocery retail businesses. SmartGrocer also automates the whole process of creating a grocery list with minimal effort and time expended by the user by leveraging the user and store context. / Graduate

This thesis argues that the concept of spatial proximity offers a viable and practical option for the development of context-aware systems for highly mobile and dynamic environments. Such systems would overcome the shortcomings experienced by today’s location-based and infrastructure dependent systems whose ability to deliver context-awareness is prescribed by their infrastructure. The proposed architecture will also allow for scalable interaction as against the single level of interaction in existing systems which limits services to a particular sized area. The thesis examines the concept of spatial proximity and demonstrates how this concept can be exploited to take advantage of technological convergence to offer mobility and scalability to systems. It discusses the design of a proximity-based system that can deliver scalable context-aware services in highly mobile and dynamic environments. It explores the practical application of this novel design in a proximity-sensitive messaging application by creating a proof-of-concept prototype. The proof-of-concept prototype is used to evaluate the design as well as to elicit user views and expectations about a proximity-based approach. Together these provide a valuable insight into the applicability of the proximity-based approach for designing context-aware systems. The design and development work discussed in the thesis presents a Proximity-Sensitive System Architecture that can be adapted for a variety of proximity-sensitive services. This is illustrated by means of examples, including a variety of context-aware messaging applications. The thesis also raises issues for information delivery, resource sharing, and human-computer interaction. While the technological solution (proximity-based messaging) offered is only one among several that can be developed using this architecture, it offers the opportunity to stimulate ideas in the relatively new field of proximity and technological convergence research, and contributes to a better understanding of their potential role in offering context-aware services.

Applications that can change their behaviour based on the user’s contextual appearance are called context-aware applications. Applications developed for smart phones, which carry a multitude of different sensors, and actuators have gained a huge penetration within the market. Frequent usage of mobile applications such as home automation, friend finder, car accident notification and tracking etc; have seen an increased growth from the user’s perspective. However, for such applications, it is necessary to have knowledge of the performance and cost parameters, which directly affect the QoE of the application users.  This thesis investigates possible context aware behaviours in real time situations and measures the Quality of Experience (QoE) as well as identifying the Packet Delay Variation (PDV). The background and some state-of-the-art technologies are studied, and based on these studies, three scenarios are designed and implemented and based on their QoE, results and conclusions are presented, with the  results obtained  shown by means of   graphical representations of the QoE and PDV values.

This thesis has investigated how the energy consumption can be reduced in a mobile sensor unit by using a dynamic measurement scheme. This was done by developing a scheme based on inspiration from existing works in related areas and on techniques found in literature. The developed scheme was then implemented on a mobile sensor unit and tests were conducted where the energy consumed by the scheme was measured. This was compared to a static baseline approach in order to evaluate the efficiency of the scheme. The results showed that on the platform used in this thesis the developed scheme can reduce the energy consumption in a typical scenario by 4.7% or 6.7% depending on which sensors are used. A conclusion drawn is that the platform has a major impact on how effective the scheme can be.

Dans ce travail de recherche, nous nous sommes intéressés à la conception et au développement d'un système iNformatIque COmmunautaire mobiLe et AdapTatif, appelé NICOLAT. Ce dernier vise à supporter une CoP dans laquelle l'apprentissage s'effectue via la résolution communautaire de problèmes en offrant des solutions qui permettent de limiter les facteurs qui peuvent aboutir à la démotivation des membres de la CoP.Pour expérimenter et valider les solutions que nous proposons à travers ce système, nous ciblons la CoP des enseignants usagers de la méthode pédagogique MAETIC, qui peuvent rencontrer, en classe, des problèmes dans l'usage de celle-ci.Ainsi, nos principales contributions se résument dans les points suivants : 1) La mise en place du noyau communautaire du système NICOLAT. Ce dernier est sous forme d'un réseau social supportant la résolution communautaire de problèmes, 2) La mise en place d'une couche de résolution de problèmes ayant pour objectif d'aider le membre à résoudre son problème par exploitation de l'historique des problèmes résolus dans le passé. Ceci pour minimiser le nombre de demandes d'aide répétitives. Le cycle du raisonnement RàPC (Raisonnement à Partir de Cas) a été utilisé pour guider ce processus, 3) La mise en place de deux couches d'adaptation permettant de supporter les interactions des membres dans les outils d'interaction qu'ils préfèrent ou avec lesquels ils sont familiarisés le plus. L'objectif visé par cette adaptation est, d'une part, de dépasser les problèmes de prise en main de nouveaux outils d'interaction. D'autre part, de permettre l'accès au système en cas de mobilité et minimiser ainsi le temps de réponse, 4) La mise en place d'une approche de sélection des membres qui peuvent contribuer positivement à résoudre un problème à qui faire aboutir la demande d'aide. L'objectif est de permettre à un membre cherchant à résoudre son problème de recevoir une réponse pertinente / In this research work, we focused on design and development of an adaptive and mobile community system, called NICOLAT (iNformatIque COmmunautaire mobiLe et AdapTatif). The latter aims to support a Community of Practice (CoP) in which learning is done through community problem solving by providing solutions that limit the factors that can lead to the demotivation of the CoP members.To experiment and validate the solutions we provide through this system, we target the CoP of teachers users of the MAETIC pedagogical method, who can meet, in classroom, problems in the use of it.Thus, our main contributions are summarized in the following points: 1) Establishment of community kernel of the NICOLAT system. The latter is as a social network supporting the community solving of problems, 2) Implementation of problems resolution layer that aims to help the member solve his problem through the exploitation of the history of problems solved in the past. This is to minimize the number of repetitive help requests. The cycle of the CBR (Case-Based Reasoning) was used to guide this process, 3) Establishment of an interactions adaptation layer to support the members' interactions in the interaction tools they prefer or with which they are most familiar. The purpose of this adaptation is, firstly, to exceed the problems of interaction tools manipulation. On the other hand, to enable access to the system in case of mobility and thereby minimize response time, 4) Establishment of a dynamic approach of selection of members who can contribute positively to solve a problem, to whom bring the help requests. The objective is to enable a member seeking to solve his problem to receive a relevant answer

This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness.

Driver distraction remains a matter of concern throughout the world as the number of car accidents caused by distracted driving is still unacceptably high. Industry and academia are working intensively to design new techniques that will address all types of driver distraction including visual, manual, auditory and cognitive distraction. This research focuses on an existing technology, namely in-car communication systems (ICCS). ICCS allow drivers to interact with their mobile phones without touching or looking at them. Previous research suggests that ICCS have reduced visual and manual distraction. Two problems were identified in this research: existing ICCS are still expensive and only available in limited models of car. As a result of that, only a small number of drivers can obtain a car equipped with an ICCS, especially in developing countries. The second problem is that existing ICCS are not aware of the driving context, which plays a role in distracting drivers. This research project was based on the following thesis statement: A mobile, context-aware model can be designed to reduce driver distraction caused by the use of ICCS. A mobile ICCS is portable and can be used in any car, addressing the first problem. Context-awareness will be used to detect possible situations that contribute to distracting drivers and the interaction with the mobile ICCS will be adapted so as to avert calls and text messages. This will address the second problem. As the driving context is dynamic, drivers may have to deal with critical safety-related tasks while they are using an existing ICCS. The following steps were taken in order to validate the thesis statement. An investigation was conducted into the causes and consequences of driver distraction. A review of literature was conducted on context-aware techniques that could potentially be used. The design of a model was proposed, called the Multimodal Interface for Mobile Info-communication with Context (MIMIC) and a preliminary usability evaluation was conducted in order to assess the feasibility of a speech-based, mobile ICCS. Despite some problems with the speech recognition, the results were satisfying and showed that the proposed model for mobile ICCS was feasible. Experiments were conducted in order to collect data to perform supervised learning to determine the driving context. The aim was to select the most effective machine learning techniques to determine the driving context. Decision tree and instance-based algorithms were found to be the best performing algorithms. Variables such as speed, acceleration and linear acceleration were found to be the most important variables according to an analysis of the decision tree. The initial MIMIC model was updated to include several adaptation effects and the resulting model was implemented as a prototype mobile application, called MIMIC-Prototype.

Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average Prevision (MAP) measure of the resulted recommendations. We demonstrate the feasibility of HPEM with two prototype applications that use contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.