This dissertation studies the caching of queries and how to cache in an efficient way, so that retrieving previously accessed data does not need any intermediary nodes between the data-source peer and the querying peer in super-peer P2P network. A precise algorithm was devised that demonstrated how queries can be deconstructed to provide greater flexibility for reusing their constituent elements. It showed how subsequent queries can make use of more than one previous query and any part of those queries to reconstruct direct data communication with one or more source peers that have supplied data previously. In effect, a new query can search and exploit the entire cached list of queries to construct the list of the data locations it requires that might match any locations previously accessed. The new method increases the likelihood of repeat queries being able to reuse earlier queries and provides a viable way of by-passing shared data indexes in structured networks. It could also increase the efficiency of unstructured networks by reducing traffic and the propensity for network flooding. In addition, performance evaluation for predicting query routing performance by using a UML sequence diagram is introduced. This new method of performance evaluation provides designers with information about when it is most beneficial to use caching and how the peer connections can optimize its exploitation.

Wireless Sensor Networks (WSNs) form a new paradigm of computing that allows the physical world to be measured at an unprecedented resolution; and the importance of the technology has been increasingly recognised. However, WSNs are still facing critical challenges, including the low data quality and high energy consumption. In this thesis, formal statistical models are employed to address these two practical problems. With the formalism that is properly designed, sound statistical inferences can be made to guide local sensor nodes to make reasonable and timely decisions at local level in the face of uncertainties. To improve data reliability, we introduce formal Bayesian statistical method to form two on-line in-network fault detectors. The two detection techniques are well integrated with existing data collection protocols. Experimental results demonstrate the technique has good detection accuracy but limited computational and communication overhead. To improve energy efficiency, we propose a novel data collection framework that features both energy conservation and data fault filtering by exploiting Hidden Markov Models (HMMs). Another data collection framework, a Dynamic Linear Model (DLM) based solution, featuring both adaptive sampling and efficient data collection is also proposed. Experimental results show the two solutions effectively suppress unnecessary packet transmission while satisfying users' precision requirement. To prove the feasibility, we show all the proposed solutions are lightweight by either real world implementation or formal complexity analysis.

This study looks at discovering information about the dynamics of a metro network, in real-time, using entry and exit data from the passengers’ smart cards. The data shows to be a valuable source of information about the current conditions of the network for both operators and passengers. An algorithm was developed which used real-time data to determine journey time characteristics, and to determine deviations from normal travel time and the extent to which these constitute a delay. This study focuses on the London Underground network and the Hong Kong MTR network as case studies to test the algorithm using the data produced by the automated ticketing systems. It aims to mine the data to provide information that can be used by passengers of the network. This information can lead to passengers knowing optimal routes, a realistic travel time and the number of minutes a delay may cost them; when the delay may be caused by congestion or service problems. Operationally this can allow for delay status reports to be more realistic, dynamic and responsive to crowding and provide information to the operators about the dynamics of the network in real time.

Mobile Ad Hoc Networks pose challenges in terms of power control, due to their fixed transmission power, the mobility of nodes and a constantly changing topology. High levels of power are needed in wireless networks, particularly for routing. As a result of the increase in the number of communication devices being used, there is the challenge of increased density within these networks, and a need to extend the battery life of communication devices. In order to address this challenge, this thesis presents the development of a new protocol (Dynamic Power AODV), which is an enhancement of the Ad Hoc On Demand Distance Vector (AODV) protocol. The new protocol dynamically adjusts the transmission power based on the range, which depends on node density. This thesis provides a systematic evaluation of the performance of DP-AODV, in a high speed and high density environment, in comparison with three other routing protocols. The experiments demonstrated that DP-AODV performed better than two of the protocols in all scenarios. As compared to the third protocol (AOMDV), DP-AODV gave better performance results for throughput and Power Consumption, but AOMDV performed better in terms of Packet Delivery Fraction rate and End-to-End Delay in some cases.

In the last 15 years, network science has established itself as a leading scientific tool for the study of complex systems, describing how components in a system interact with one another. Understanding the structure and dynamics of these networks of interactions is the key to understanding the global behaviour of the systems they represent, with a wide range of applications to fundamental societal problems; from designing stable and resilient infrastructures which are critical to our sustainability, to identifying topological patterns in interactome networks that are associated with breast cancer. Most studies so far have focused on isolated single networks that do not interact with or depend upon other networks, while in reality networks rarely live in isolation and are often just one component in a much larger complex multilevel network. Together with the increased availability of richer, bigger and multi-relational datasets, the analysis of coupled networks has been recently attracting many researchers, and has exposed a multitude of new features and phenomena that were not observed for isolated networks. In this thesis, we present analytical, numerical and empirical studies of coupled complex networks, aiming to understand the implications of coupling to the functionality and behaviour of complex systems. First, we present a theoretical framework for studying the robustness of modular or interconnected networks, providing the critical concentration of interconnections between modules, above which the internal structure of each module is inseparable from the system as a whole. Second, we present another theoretical framework to study epidemic spreading in interconnected adaptive networks, discovering a new stationary state that only emerges in the case of weakly coupled networks, where the epidemic localise in the coupled nodes. In order to obtain the exact quantitative behavior of the new state from the analytical model, one must account for the actual second-order moments of the system, even for homogeneous networks, where in single networks it is usually sufficient to treat such higher-order terms by a uniform approximation. Thirdly, we present a numerical study on the effect of correlated coupling on spreading dynamics in the presence of resource constraints, finding that positive correlation between coupled nodes can impede flow process through contention, and thus constitute a less spreading-efficient structure than negatively correlated networks. Finally, we complete the thesis with a large-scale empirical study of interacting transportation networks in the entire metropolitan areas of both London and New York. We find that coupling can strongly affect the structure, and consequently the behaviour, of such multilayer transportation systems.

In a secure multi-party computation (MPC) protocol, a number of parties jointly compute a function on their private inputs, learning only the output of the function. The SPDZ protocol is one of the most practical, actively secure MPC protocols tolerating a majority of corrupted parties. We describe a covertly secure distributed key generation protocol for the preprocessing stage of SPDZ, which was previously assumed as a global setup, and also present more efficient, covertly secure protocols for the remainder of the preprocessing. We then give a new approach to implementing the preprocessing using oblivious transfer, which provides an alternative foundation for this stage of the protocol. Using a novel actively secure protocol for extending oblivious transfers, we estimate that this should be much more efficient than existing protocols based on somewhat homomorphic encryption. Next, we turn to implementing the online phase of SPDZ, where the actual computation takes place. We design a virtual machine-based architecture for running the online phase and create a compiler that allows programmers without any experience of MPC protocols to easily implement algorithms in MPC, producing optimised code for the virtual machine. We present benchmarks of various common and useful functions - including secure comparison, floating point arithmetic and AES - running on our architecture. These are significantly faster than all previously published results with the same level of security. Finally, we present efficient, sec ure protocols for oblivious data structures such as arrays, priority queues and dictionaries using Oblivious RAM. As an application. of these, we show how to perform secure computation of Dijkstra's shortest paths algorithm on graphs, where the graph structure is secret. We give benchmarks for the resulting protocols using the previous architecture.

The problem of secure computation considers a set of parties who do not trust each other and yet want to perform public computations on data sets held privately by each individual. The most important property of secure computations is that they are input-privacy preserving. Intuitively this means that after the computation has finished no party can say something meaningful about the inputs of the other parties -beyond what is implicit in the outcome of the computation. This thesis focuses on the design and analysis of protocols in the pre-processing model with a majority of dishonest parties. This model presupposes an initial set-up that produces a large number of independent data to be used as auxiliary input at a later stage, when the actual secure computation takes place. The' current state of the art for producing such precomputed data employs homomorphic encryption and techniques derived from oblivious transfer. As a side effect we also dwell on the problem of extending oblivious transfer.

The research presented in this thesis looks at situated computing from the embodiment perspective. It presents broader theoretical framework to help understand the notion of embodiment and its relevance to situated computing. It explains in detail how the ethos in current research practice concerning embodiment is somewhat misplaced. It explains what may cause embodied experience in interactive environments, and draws constitutive understanding about the idea of space, objects and their relationships to the human subject in this regard. It denounces tangible computing's claims for physically based interface being a prerequisite for embodied interaction experience and explains the correct relevance of tangibility in interfaces. Thus, it outlines how exactly, tangibility, space, objects or their virtual counterparts (computational metaphors) fit into embodied perspective. The thesis also presents an in-depth analysis of context aware systems' architecture and exposes the limitations of context modelling and context meaning determination processes. It defines what these limitations are, where the problems are located in system design process and shows why they occur.

In many types of network, the relationship between structure and function is of great significance. This work is particularly concerned with community structures, which arise in a wide variety of domains. A simple oscillator model is applied to networks with community structures and shows that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we demonstrate that such global oscillations may arise as a direct result of network topology. We also observe that additional modes of oscillation (as detected through frequency analysis) occur in networks with additional levels of hierarchy and that such modes may be directly related to network structure. This method is applied in two specific domains (metabolic networks and metropolitan transport), demonstrating the robustness of the results when applied to real world systems. A topological analysis is also applied to the real world networks of metabolism and metropolitan transport using standard graphical measures. This yields a new artificial network growth model, which agrees closely with the graphical measures taken on metabolic pathway networks. This new model demonstrates a simple mechanism to produce the particular features found in these networks. We conclude that (where the distribution of oscillator frequencies and the interactions between them are known to be unimodal) the observations may be applicable to the detection of underlying community structure in networks, shedding further light on the general relationship between structure and function in complex systems.

Digital networking technologies open up a new world of possibilities for music making, allowing performers to collaborate in ways not possible before. Network based Multi-User Instruments (NMIs) are one novel method of musical collaboration that take advantage of networking technology. NMIs are digital musical instruments that exist as a single entity instantiated over several nodes in a network and are performed simultaneously by multiple musicians in realtime. This new avenue is exciting, but it begs the question of how does one design instruments for this new medium? This research explores the use of an aesthetically driven design process to guide the design, construction, rehearsal, and performance of a series of NMIs. This is an iterative process that makes use of a regularly rehearsing and performing ensemble which serves as a test-bed for new instruments, from conception, to design, to implementation, to performance. This research includes details of several NMIs constructed in accordance with this design process. These NMIs have been quantitatively analysed and empirically tested for the presence of interconnectivity and group influence during performance as a method for measuring group collaboration. Furthermore qualitative analyses are applied which test for the perceived e ectiveness of these instruments during real-world performances in front of live audiences. The results of these analyses show that an aesthetically driven method of designing NMIs produces instruments that are interactive and collaborative. Furthermore results show that audiences perceive a measurable impression of interconnectivity and liveness in the ensemble even though most of the performers in the ensemble are not physically present.