This thesis provides a synthesis of knowledge about inclusive networking in an international context, derived from the experience of stakeholders in education in countries of the South. It is suggested that inclusive networking facilitates the telling of stories, challenges stereotypes, humanises headlines and promotes contextually relevant research. The research question: “What are the key principles for the development of an inclusive international network?” was developed as a guide to this study. Using a process of autoethnographic analysis, I identified my tacit knowledge of inclusive networking through a continual analysis and cross-referencing of the ten publications presented here, and a wider search of the literature. This thesis is presented as a narrative in three parts. In the first part I trace my motivation for becoming involved in international networking as a strategy to address global inequality, review the literature on networking most relevant to this thesis, and identify some of the barriers to publishing faced by education stakeholders in the South. In the second part, a case study of the Enabling Education Network (EENET) is presented and a wide range of networking dilemmas identified, related to the way information is collected and shared across diverse Southern contexts. The overarching dilemmas of northern dominance and deficit thinking are discussed in the context of oral culture and the global digital and communication divide, and the difficulties in promoting genuinely critical conversations. In the third part, it is argued that contextual detail and transparency about authorship are critical to the construction of stories and accounts if they are to be meaningfully networked across diverse contexts. However, it is suggested that investing resources in the development of documentation, reflection and analytical skills in education stakeholders is an essential part of inclusive networking, if ‘promising’ practice is to be captured from a Southern perspective. Following this, the penultimate chapter puts forward four key principles relating to the development of an inclusive international network that emerged from the analysis of publications and the wider literature: 1. sharing of information, knowledge and stories between contexts; 2. knowledge creation: through reflection, documentation and analysis; 3. balancing insider and outsider knowledge and perspectives; and 4. developing appropriate responses at community level. I conclude with some possible future directions for research, and with a personal story which encapsulates the theoretical framework of this thesis.

The increased globalisation of information and communication technologies has transformed the world into the internet of things (IoT), which is accomplished within the resources of wireless sensor networks (WSNs). Therefore, the future IoT networks will consist of high density of connected nodes that suffer from resource limitation, especially the energy one, and distribute randomly in a harsh and large-scale areas. Accordingly, the contributions in this thesis are focused on the development of energy efficient design protocols based on optimisation algorithms, with consideration of the resource limitations, adaptability, scalability, node density and random distribution of node density in the geographical area. One MAC protocol and two routing protocols, with both a static and mobile sink, are proposed. The first proposed protocol is an energy efficient hybrid MAC protocol with dynamic sleep/wake-up extension to the IEEE 802.15.4 MAC, namely, HSW-802.15.4. The model automates the network by enabling it to work exibly in low and high-density networks with a lower number of collisions. A frame structure that offers an enhanced exploitation for the TDMA time slots (TDMAslots) is provided. To implement these enhanced slots exploitation, this hybrid protocol rst schedules the TDMAsslots, and then allocates each slot to a group of devices. A three-dimensional Markov chain is developed to display the proposed model in a theoretical manner. Simulation results show an enhancement in the energy conservation by 40% - 60% in comparison to the IEEE 802.15.4 MAC protocol. Secondly, an efficient centralised clustering-based whale optimisation algorithm (CC- WOA) is suggested, which employs the concept of software de ned network (SDN) in its mechanism. The cluster formulation process in this algorithm considers the random di- versi cation of node density in the geographical area and involves both sensor resource restrictions and the node density in the tness function. The results offer an efficient con- servation of energy in comparison to other protocols. Another clustering algorithm, called centralised load balancing clustering algorithm (C-LBCA), is also developed that uses par- ticle swarm optimisation (PSO) and presents robust load-balancing for data gathering in IoT. However, in large scale networks, the nodes, especially the cluster heads (CHs), suffer from a higher energy exhaustion. Hence, in this thesis, a centralised load balanced and scheduling protocol is proposed utilising optimisation algorithms for large scale IoT net- works, named, optimised mobile sink based load balancing (OMS-LB). This model connects the impact of the Optimal Path for the MS (MSOpath) determination and the adjustable set of data aggregation points (SDG) with the cluster formulation process to de ne an op- timised routing protocol suitable for large scale networks. Simulation results display an improvement in the network lifespan of up to 54% over the other approaches.

Abstract The importance of the role of the principal in good schools is acknowledged by many sources. The preparation of new principals is therefore an important factor in ensuring children are educated in good schools. New Zealand does not have a formal system of principal preparation. The purpose of this study was to examine the experiences of pre-principalship preparation with the aim of discovering those activities and developments that were useful in assisting teachers to make the transition to successful principalship. The research question addressed in this study is,; What are the elements of pre-principalship preparation that are most useful for potential and aspiring principals in furthering their career aims? Using qualitative methodology, a group of people who had attended the Aspiring and Potential Principals' Pilot run by the School of Education at the University of Waikato, were questioned using semi structured interviews about their experiences. Five of the six were holding principal positions, the sixth was in a deputy principal's position and had some relieving principal experience. The results the research generated indicated that while there were varying needs for potential principals because of their varied backgrounds, there were six useful experiences for all identified. These included, attendance at some form of targeted principal preparation programme, a background of ongoing professional learning, developing networks, developing successful mentoring, experience of models of principalship and support of 'family'. Different people had different levels of benefit from these experiences but they were common to all. It is hoped that this research will give assistance to guiding professional development for the potential and aspiring principals of tomorrow.

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Dans le cadre du contrôle de l'exécution d'applications temps réel, un mécanisme d'ordonnancement de tâches basé sur le critère du temps est indispensable. Le mécanisme se doit de garantir en priorité les tâches périodiques et de maximiser le nombre de celles apériodiques. Le mécanisme d'ordonnancement distribué que nous proposons, traite d'abord l'allocation statique de tâches temps réel, car les tâches périodiques doivent être allouées et ordonnancées avant l'exécution. Nous avons développé un algorithme d'allocation parallèle basé sur l'approche des algorithmes génétiques. Il permet d'obtenir des allocations correctes ou le respect des contraintes temporelles qui portent sur les tâches est assure, en effet l'ordonnancement est pris en compte lors de la construction du placement. Dans le cas de systèmes temps réel souples, le mécanisme d'ordonnancement distribué met en oeuvre deux algorithmes afin de gérer l'ordonnancement et l'allocation des tâches durant l'exécution. Le premier est un algorithme d'ordonnancement local en-ligne, simple et peu coûteux. Le second est un algorithme d'allocation dynamique, indépendant de la taille et de la topologie du réseau. Il se distingue par une heuristique visant à donner davantage de garantie aux tâches apériodiques par l'acceptation de celles-ci et par le transfert de tâches moins urgentes. Une réservation de l'emplacement des tâches sur le processeur désigné pour l'allocation permet de ne pas remettre en cause l'ordonnancement auparavant établi. La mise en oeuvre de ces algorithmes dans le noyau ParX du système d'exploitation parallèle Paros, nous a permis de montrer qu'un mécanisme d'ordonnancement distribué améliore les performances d'une application temps réel.

With the rise of services like Steam and Xbox Live, multiplayer support has become essential to the success of many commercial video games. Explicit, server-client synchronisation models are bandwidth intensive and error prone to implement, while implicit, peer-to-peer synchronisation models are brittle, inflexible, and vulnerable to cheating. We present a generalised server-client network synchronisation model targeted at complex games, such as real time strategy games, that previously have only been feasible via peer-to-peer techniques. We use prediction, learning, and entropy coding techniques to learn a bandwidth-efficient incremental game state representation while guaranteeing both correctness of synchronised data and robustness in the face of unreliable network behavior. The resulting algorithms are efficient enough to synchronise the state of real time strategy games such as Blizzard’s Starcraft (which can involve hundreds of in-game characters) using less than three kilobytes per second of bandwidth.

Measuring human behavioral patterns has broad application across different sciences. An individuals social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure.<p> This thesis presents a data collection system using tiny sensors which is capable of recording humans proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information.<p> In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the models contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.

Users of Social Networking Sites (SNSs) like Facebook, MySpace, LinkedIn, or Twitter face two problems 1) their online social friendships and activities are scattered across SNSs. It is difficult for them to keep track of all their friends and the information about their friends online social activities. 2) they are often overwhelmed by the huge amount of social data (friends updates and other activities). To solve these two problems, this research proposes an approach, named SocConnect. Soc- Connect allows users to create personalized social and semantic contexts for their social data. Users can blend their friends across different social networking sites and group them in different ways. They can also rate friends and/or their activities as favourite, neutral or disliked. SocConnect also can recommend unread friend updates to the user based on user previous ratings on activi- ties and friends, using machine learning techniques. The results from one pilot studies show that users like SocConnects functionalities are needed and liked by the users. An evaluation of the effectiveness of several machine learning algorithms demonstrated that , and machine learning can be usefully applied in predicting the interest level of users in their social network activities, thus helping them deal with the network overload.