Doctoral Theses

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https://hdl.handle.net/10292/4
The Doctoral Theses collection contains digital copies of AUT doctoral theses deposited with the Library since 2004 and made available open access. All theses for doctorates awarded from 2007 onwards are required to be deposited in Tuwhera Open Theses unless subject to an embargo.

For theses submitted prior to 2007, open access was not mandatory, so only those theses for which the author has given consent are available in Tuwhera Open Theses. Where consent for open access has not been provided, the thesis is usually recorded in the AUT Library catalogue where the full text, if available, may be accessed with an AUT password. Other people should request an Interlibrary Loan through their library.

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Browsing Doctoral Theses by Supervisor "Al-Anbuky, Adnan"

Now showing 1 - 18 of 18
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    Adaptive Quality of Service for IoT-based Wireless Sensor Networks
    (Auckland University of Technology, 2018) Syed Nor Azlan, Syarifah Ezdiani
    The future of the Internet of Things (IoT) is envisaged to consist of a high amount of wireless resource-constrained devices connected to the Internet. Moreover, a lot of novel real-world services offered by IoT devices are realised by wireless sensor networks (WSNs). Integrating WSNs to the Internet has therefore brought forward the requirements of an end-to-end quality of service (QoS) guarantees. In this thesis, a QoS framework for integrating WSNs with heterogeneous data traffic is proposed. The concept of Adaptive Service Differentiation for Heterogeneous Data in WSN (ADHERE) is proposed based on the varying QoS factors and requirements analysis of mixed traffic within an IoT-based WSN. The objective of the QoS framework is to meet the requirements of heterogeneous data traffic in the WSN - in the domain of timeliness and reliability. Another objective is to implement an adaptive QoS scheme that can react to dynamic network changes. This thesis provides the literature analysis and background study for integrating a WSN which contains heterogeneous data traffic with the Internet. In the discussion of network modelling and implementation tools for the testing, this thesis provides an insight into the different tools that are available and their ability to investigate the concept of service differentiation among heterogeneous traffic within the IoT-based WSN network. Furthermore, the major components of ADHERE are presented in the Concept chapter. The major components are: a heterogeneous traffic class queuing model that encompasses a service differentiation policy, a congestion control unit and a rate adjustment unit that supports the adaptive mechanism. Network modelling and the simulation of an ADHERE QoS framework which is carried out primarily using the network simulation tool, Riverbed Modeler, are also presented. Additionally, a proposed co-simulation between Riverbed Modeler and MATLAB is introduced, which aims to provide a seamless QoS monitoring using the ADHERE concept. The simulation results suggest that real-time traffic achieves low bound delay while delay-tolerant traffic experiences a lower packet drop. This indicates that the needs for real-time and delay-tolerant traffic can be better met by treating both packet types differently using ADHERE. Furthermore, a verification and added-value to the ADHERE QoS model using a neural network is also presented. The learning capabilities in ADHERE optimise the QoS framework’s performance by accommodating the QoS requirements of the network through the unpredictable traffic dynamics and when complex network behaviour takes place. Before concluding the thesis, the implementation of ADHERE QoS as a use-case on a physical test environment is also discussed. The test environment offers a flexible system that is capable of reacting to the dynamic changes of process demands. Physical network performance can be predicted by analysing the historical data in the background on a network simulator or virtual network. Finally, this thesis offers a conclusion with an indication of our future research work.
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    An Authenticated Key Agreement Scheme for Sensor Networks
    (Auckland University of Technology, 2014) Yang, Mee Loong
    In wireless sensor networks, the messages between pairs of communicating nodes are open to eavesdropping, tampering, and forgeries. These messages can easily be protected using cryptographic means but the nodes need to share a common secret pairwise key. This thesis proposes a new scheme, the Blom-Yang key agreement (BYka) scheme, that enables pairs of sensor nodes in large networks to compute their pairwise keys quickly and efficiently. Prior to deployment, the Trusted Authority (TA), assigns each node their public IDs, and using its master keys, computes and stores in the nodes their private key-sets. When a pair of nodes need to obtain their pairwise keys, they exchange their public key identifier IDs which are just 16-bit integers. Using the counterpart's ID with its own set of private keys, the nodes are able to compute a large common pairwise key, but only if they have obtained their keying material from the same TA. Hence, the scheme is also mutually authenticating. The computations use simple arithmetic operations which are fast and efficient, easily undertaken by sensor devices which have limited computational, memory, and energy resources. For example, it is able to compute keys of 128 bits in 279 milliseconds in the MICAz mote, requiring 1170 bytes of memory to store the private keying material. Similar key agreement schemes, already widely used in computer networks, use public key cryptographic algorithms which require computationally expensive mathematical operations, taking much longer time, and requiring much more resources. The security of the BYka scheme is based on the difficulty of obtaining information about the private-public-master-key associations (PPMka). The private keys in each node are computed by the TA using all the permutations of its multiple master keys and the node's public keys operating over a small prime field, and then stored in a random order in the node. If these are captured, the private keys cannot be used directly as the adversary would first have to discover the PPMka. The analysis showed that, with suitable keying parameters, even if sufficient number of private keys are stolen, an adversary with powerful computing resources would need to expend an infeasibly large amount of time and resources to try all the possible PPMka to break the scheme. The adversary may try to discover the PPMka by using pairs of captured nodes to compute their pairwise keys, but this would require the capture of tens of thousands of nodes. Alternatively, even when using the most efficient method, the adversary needs to try a large number of possibilities equivalent to security strengths of 80 to 192 bits. Overall, the adversary has only a small probabilistic chance of breaking the scheme. These analytical results were verified using computer simulated attacks and are used to provide some guidelines and tables for the selection of the keying parameters to meet implementation and performance requirements including computation times, memory availability, network sizes, and pairwise key sizes. The proposed key agreement scheme is in effect a non-interactive identity-based scheme which uses the node's identity (ID) as its public key. This allows a node to encrypt messages to a target node once its ID is known. It can be used by nodes in dynamic, mobile and ad hoc situations to opportunistically send authenticated messages to each other when they are in range. A single message authenticated protocol (SMAP) using the BYka scheme as the cryptographic primitive is proposed. The speed, efficiency, and resilience of the BYka scheme would make it useful as the cryptographic primitive in other applications such as email and voice communications.
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    A Cross-Layer Design for Sensor-Based Ambient Intelligence Systems
    (Auckland University of Technology, 2014) Liu, Yang
    The wireless sensor network (WSN) is an enabling technology of ambient intelligence (AmI) where an intelligent system can sense the presence of and respond to the context or situations of people in the environment. AmI relies on the massive deployment of interconnected and distributed sensor devices to provide personalised services via intuitive interfaces and natural interactions in a manner consistent with the user contexts. Cross-layer approaches have been widely used for WSN management and play an important role in designing solutions for protocol optimisation. The cross-layer approaches allow the sharing of information in a protocol stack across different layers for significant improvements on network performance and efficiency. After an extensive literature review, it emerges that there exists research opportunities on cross-layer designs for WSNs in context-aware systems. Therefore, the research presented in this thesis is to develop a cross-layer optimisation approach for WSNs by utilising the user and environment context information from an AmI system. This approach can provide the resource-constrained sensor devices with the capability to understand the situations of their surroundings for the purpose of optimising WSN communications.
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    Cyber Physical System for Pre-operative Patient Prehabilitation
    (Auckland University of Technology, 2022) Al-Naime, Khalid Abdulrazak Mahmood
    Abdominal cancer is the one of the most frequent and dangerous cancers in the world, particularly among the elderly, and is considered one of the leading causes of death in New Zealand and throughout the world. Major surgery is associated with a significant deterioration in quality of life, as well as a 20%-40% reduction in postoperative physical function. Physical fitness and level of activity are considered important factors for patients with cancer undergoing major abdominal surgery. These patients are often given exercise programmes prior to surgery (prehabilitation), aimed at improving fitness to reduce perioperative risk. Even though the number of prehabilitation programmes has increased over the last decade, there are many obstacles preventing large numbers of patients being involved in such programmes. One key problem is access to prehabilitation facilities and resources. The long-distance travel to vital cancer services can have a significant impact on a patient’s quality of life and survival. Furthermore, limited numbers of healthcare centres and staff impact on the number of patients who can participate in supervised prehabilitation programmes. Unsupervised prehabilitation programmes have problems such as uncertainty of compliance with home-based exercises. Also lacking are measurements for the movements that are performed in relation to the intended frequency and intensity. Patient safety is also an issue with an unsupervised programme. To minimise the above barriers, a model for a mixed mode prehabilitation programme has been designed. An environment for hosting the prehabilitation tracking model has also been developed. The end result proposes an end-to-end solution that provides patients and healthcare staff with a real-time remote monitoring and visualisation system. Furthermore, architectural features were recruited for this work to balance the computational load between the IoT device, gateway and cloud. This has facilitated better usage of the available environment through fewer messages, and the sharing of resources has reflected positively on overall system performance, such as: a. The system showed high performance with activity recognition percentages ranging from 70%-94% when using the personalised database. b. Different logical methods (M1, M2, M3, and M4) for activity recognition were implemented and embedded at the gateway level. c. Using a mixed mode enabled detecting both casual and formal activities relevant to the prehabilitation programme. Also, the system offers real-time feedback on patients’ progress during the prehabilitation period. On the other hand, many challenging areas require additional research to provide better system performance, such as using artificial intelligence (AI) techniques in various embedded IoT devices and differentiating between the different weights credited to different types of movement and activities. This thesis is divided into seven different chapters, each accounting for a specific element of the overall work. The motivational background for the rising demand for healthcare monitoring is presented in the first chapter. The second chapter accounts for a critical review of the existing literature pertaining to the various key elements and boundaries associated with constructing a mixed mode prehabilitation model. The third chapter provides information related to the tools used for the implementation of hardware and software in the testing and verification of concepts. Chapter 4 proposes a conceptual mixed mode prehabilitation model based on existing rules and health programmes. Chapter 5 examines the various components of CPS in terms of data collection, data analysis, activity recognition, data visualisation, and short- and long-term data storage. Chapter 6 presents the clearly defined validation output data of the developed mixed mode prehabilitation model. The conclusions of this thesis, as well as the future path of the work, are presented in Chapter 7. Finally, this work has delivered four articles that have been published in international journals and conferences, and two proposed papers are under development to state the research outcome.
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    Distributed Incremental Data Stream Mining for Wireless Sensor Network
    (Auckland University of Technology, 2012) Sabit, Hakilo
    Wireless sensor networks (WSNs) despite their energy, bandwidth, storage, and computational power constraints, have embraced dynamic applications. These applications generate a large amount of data continuously at high speeds and at distributed locations, known as distributed data stream. In these applications, processing data streams on the fly and in distributed locations is necessary mainly due to three reasons. Firstly, the large volume of data that these systems generate is beyond the storage capacity of the system. Secondly, transmitting such large continuous data to a central processing location over the air exhausts the energy of the system rapidly and limits its lifetime. Thirdly, these applications implement dynamic models that are triggered immediately in response to events such as changes in the environment or changes in set of conditions and hence, do not tolerate offline processing. Therefore, it is important to design efficient distributed techniques for WSN data stream mining applications under these inherent constraints. The purpose of this study was to develop a resource efficient online distributed incremental data stream mining framework for WSNs. The framework must minimize inter-node communications and optimize local computation and energy efficiency without compromising practical application requirements and quality of service (QoS). The objectives were to address the WSN energy constraints, network lifetime, and distributed mining of streaming data. Another objective was to develop a novel high spatiotemporal resolution version of the standard Canadian fire weather index (FWI) system called the Micro-scale FWI system based on the framework. The perceived framework integrates autonomous cluster based data stream mining technique and two-tiered hierarchical WSN architecture to suit the distributed nature of WSN and on the fly stream mining requirements. The underlying principle of the framework is to handle the sensor stream mining process in-network at distributed locations and at multiple hierarchical levels. The approach consists of three distinct processing tasks asynchronously but cooperatively revealing mining the sensor data streams. These tasks are the sensor node, the cluster head, and the network sink processing tasks. These tasks were formulated by a lightweight autonomous data clustering algorithm called Subtractive Fuzzy C-Means (SUBFCM). The SUBFCM algorithm remains embedded within the individual nodes to analyze the locally generated streams ‘on the fly’ in cooperation with a group of nodes. The study examined the effects of data stream characteristics such as data stream dimensions and stream periods (data flow rates). Moreover, it evaluated the effects of network architectures such as node density per cluster and tolerated approximation error on the overall performance of the SUBFCM through simulations. Finally, the QoS or certain level of guaranteed performance that is supported by the WSN architecture for applications utilizing the framework was examined. The results of the study showed that the proposed framework is stream dimension and data flow rate scalable with average errors of less than 12% and 11% in reference to the benchmarks, respectively. The node density per cluster and local model drift threshold showed significant effects on the framework performance only for very fast streams. The study concludes that the network architecture is an important factor for the quality of mining results and should be designed carefully to optimally utilize basic concepts of the framework. The overall mining quality is directly related to the combined effect of the stream characteristics, the network architecture, and the desired performance measures. The study also concludes that WSNs can provide good QoS feasible for online distributed incremental data stream mining applications. Simulations of real weather datasets indicate that the Micro-scale FWI can excellently approximate the results obtained from the Standard FWI system while providing highly superior spatial and temporal information. This can offer direct local and global interaction with a few meter square spaces as against the tens of square kilometers of the present systems.
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    Distributed Trust-based Routing Decision Making for WSN
    (Auckland University of Technology, 2019) Khalid, Nor Azimah
    This thesis describes novel approaches to deal with routing in distributed wireless sensor networks (WSNs) decision making and proposes new distributed protocols based on trust. The trust is defined as the level of belief that a sensor node has on another node for specific action, based on certain criterion that is specified according to applications. As WSNs are applications specific, the proposed trust-based solutions are mainly targeting at two types of network structures, namely, the static homogeneous network, and the network with mobile sink. The first contribution of the thesis is a multi criteria trust model called Hierarchical Trust-based Model (HTM). The model considers several criteria and evaluates the trustworthiness of a node in two levels. HTM is different from most of the existing trust models as it evaluates the trust for multiple nodes rather than a single node evaluation. The model uses the Analytical Hierarchical Process (AHP) in computing the node's trust. The second contribution is a novel distributed trust-based protocol called Adaptive Trust-based Routing Protocol (ATRP). The proposed ATRP embed the proposed HTM in its process. Four network performance metrics (energy, reliability, coverage and reputation) were considered in the forwarder selection. The reputation, which is the accumulated value provided by indirect nodes about evaluated nodes previous communication behaviours is gained using Q-learning. ATRP takes into consideration the resource constrained factors of the nodes by introducing several control mechanisms (timeliness and number of interactions). Thirdly, the thesis considers the implementation of the mobile sink and taken into consideration the relocation issue which is the main concern in existing distributed mobile sink routing. A new distributed mobile sink routing protocol called Blockchain-based Routing Protocol (BCRP) is presented where it adapts the blockchain elements in its relocation decision strategy. The decision in BCRP is determined by other mobile sinks in ensuring the relocation position is not redundantly covered. This is because the redundant coverage in some applications are unnecessary and will consume more energy. The participating mobile sinks are able to make decisions without the central entity's help but based on a set of rules that are pre-agreed by all mobile sinks. The relocation will only happen if it is agreed (verified) by a certain number of mobile sinks. In such situations, the decision making will benefit a larger number of nodes and all nodes are able to get updated information. The performances of BCRP are evaluated and compared under several simulation environments in terms of five performance metrics, i.e., energy consumption, packet delivery ratio, average delay, throughput and coverage level. Based on the simulation results, the proposed approaches outperform the other comparable protocols for all the performance metrics.
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    Energy Efficient Opportunistic Connectivity for Wireless Sensor Network
    (Auckland University of Technology, 2013) Sivaramakrishnan, Sivakumar
    This thesis provides a theoretical analysis of the effects of mobility, node density and a limited transmission range on the connectivity of a varying density of nodes in wireless sensor networks. Connectivity in cellular networks has the advantage of a fixed centralised infrastructure that can provide wide communication coverage. Wireless sensor networks, on the other hand, have a limited range. This limited range, coupled with nodes’ mobility, often results in network holes. As the architecture is de-centralised, there is no central node that monitors the nodes’ joining or leaving the network. The challenge of identifying these nodes, which is due to their dynamic nature of movement, is presented here. Opportunistic connectivity addresses the challenge of providing connectivity to isolated mobile nodes. This is through the process of discovery of regions where good density of network nodes are available. The concept involves four key components. These are adaptive sampling, coverage, handoff and directional communication. These act on the minimisation of energy cost incurred with the discovery of related nodes and establishment of connectivity in the network. The window of time for communication is extended in an energy–efficient manner through coverage, handoff and direction for such delay–tolerant networks. The overall contribution of this thesis is a protocol design for opportunistic connectivity, its implementation and analysis, with reference to the conservation of energy and reduction of packet drops, in conjunction with protocol testing on an application scenario. The thesis is structured into seven chapters. The first two chapters provide the background and the literature analysis. The third chapter deals with systems and tools which are used for the modelling and testing. It gives an insight into the different available tools and their ability to validate the parameter of our concept of an opportunistic connectivity protocol. Subsequently, the thesis discusses the design of the ‘adaptive Energy COnscious DElay Tolerant OpportUnistic Routing’ (ECO-DETOUR) protocol for such delay–tolerant networks in chapter four, as a four stage process involving adaptive sampling, coverage, direction and hand-off. Design of the protocol is followed by implementation in chapter five, which was performed using the OPNET and MATLAB environments. The chapter details the different conditions in which each of the four parameters are triggered and discusses the implementation of each of the four parameters as pseudo-code. Finally in chapter six the protocol is tested on a wildlife application scenario. The effectiveness of the protocol is measured in relation to the energy saved and the reduction in number of packet drops achieved under different mobility conditions. Results show that ECO-DETOUR achieves a 45% - 60% reduction in expended energy to set up communication and exchange data packets. The bulk of the saving in energy by the ECO-DETOUR protocol comes from adaptive sampling which is followed by coverage, handoff and direction.
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    IoT-Based Sensor Networks: Architectural Organization, Virtualization and Network Re-orchestration
    (Auckland University of Technology, 2021) Acharyya, Indrajit
    Internet of ‘Things’ (IoT), an extension of localised ‘Wireless’ Sensor Networks (WSN), has been employed to realize a multitude of smart, intelligent and pervasive Cyber Physical System (CPS) infrastructures. CPS encompasses a host of technological and architectural challenges like low-power communication, protocol conversions, data transport and the ability to interoperate with other IoT technologies. This makes CPS significantly complex and reduces its flexibility to adapt. A typical IoT-based sensor network may to a certain extent, lack key softwarization-enabled operational drivers that may introduce significant constraints on its ability to flexibly engage with its external surroundings. Flexible re-orchestration of such complex IoT based sensor networks, however, is vital towards aligning system ‘dynamics’ with that of a monitored ‘physical’ phenomenon while operating in dynamic physical environments (for example WSNs deployed for outdoor applications such as forest fire monitoring). Arguably, considerable levels of operational flexibility can be achieved through a cloud-based architectural framework that hosts the required operating tools to allow for software-defined network virtualization that enable suitable re-orchestrations of the related physical network. In a nutshell, research work documented within this thesis endeavours towards rendering a sensor network capable of undergoing desired flexible re-orchestrations via converging upon a novel architectural proposition inclusive of modularization, cloud-based virtualization, software control via ‘command-driven reconfigurability’, and maintenance of a library for additional firmware modules (for each of the nodes at the physical level), among others. Other equally noteworthy and innovative contributions of this thesis pertain to outlining of a seemingly logical strategy for the sensor network ‘re-orchestration’ process (that spans across ‘three’ phases of, ‘Data Analysis and Event-Identification’, ‘Re-orchestration-Planning’ and ‘Re-orchestration-Execution’) as well as both determining and formulating a generic model for the latency associated with the same. The approach adopted herein is to allow for the underlying physical layer to undergo desired node and network-level (including topological) re-orchestrations (based on the outcomes derived from the cloud) in a flexible and expeditious manner during run-time through a ‘Command-driven’ re-configurability approach. This relatively simplistic yet expedient approach involves loading of a ‘unified firmware’ (i.e., one encompassing the requisite, ‘well-defined’ software modules) onto nodes (assumed to be capable of accommodating for and executing the corresponding functional roles owing to the enhanced capabilities ushered in by the advancements attained in the field of SoC and Embedded Systems technologies) to allow for conditional execution of the same remotely by means of ‘commands’. In order to augment the flexibilities that could be offloaded by the node over time based on the service requirements, a library of ‘reusable firmware modules’ (within which the requisite new functional modules could be integrated from time-to-time) could be maintained to be readily accessible by the main firmware. In regard to the above context, it is deemed worthy to reiterate that the thesis underscores the key prerequisites for the above prior to laying the concept in chapter four. Firstly, this includes identifying and clearly defining the core functional components (constituting any IoT-based sensor network organization viz., ‘leaf’, router and ‘Gateway’ functionalities) as ‘modules. The second prerequisite pertains to modularization of the core functional components that have been identified and defined. Virtualization of the core functional modules so identified and thereby the entire network (essentially, cloud-level Network Virtualization i.e., ‘NV’) that ‘logically’ (i.e., from a software standpoint) mimics the operational dynamics of the underlying physical network functions will form the third prerequisite. As alluded to earlier, the fourth prerequisite refers to the library of reusable ‘firmware modules’ at the node level (for augmented flexibility). The thesis is sectioned into seven different chapters, each accounting for a specific element of the overall work. The first chapter provides an overview of the various technological domains and aspects associated with this research work, whilst laying out the necessary background, vision and motivation behind the same. The second chapter accounts for a review of the existing literature pertaining to the various elements associated with this research viz., WSN virtualization, softwarization, re-orchestration and associated network downtime (as well as other architectural frameworks designed with relatively similar motives in mind). Information pertaining to the tools employed for virtualization and hardware implementation purposes are provided in the third chapter. As elaborated above, Chapter 4 firstly spells out the key perquisites for the proposed architecture prior to describing the same, along with its internal components. It then outlines the strategy adopted for the re-orchestration process, including formulation of a generic model for the latency that the network may experience as a result of the same. By means of certain pertinent example cases of software-defined sensor network re-orchestrations, chapter 5 details the specifics of both virtual and physical implementations, conducted via utilizing the Contiki-oriented virtual platform of the Cooja simulator as well as the Contiki-ported Texas Instruments CC2538 wireless transceivers respectively. It also brings to the fore the practicability of employing Contiki as a tool for software development that allows for precise replication of the codes employed for physical motes at the virtual level, whilst leveraging on the same to better analyse and conduct more accurate performance evaluations pertaining to the re-orchestration process. As a means to demonstrate the workability of the proposed concept with respect to a real-life scenario, chapter 6 deals with the use case pertaining to forest fire monitoring wherein dynamic re-orchestration of sensor network so deployed could significantly aid (pre-emptive) re-routing of network dataflow and/or maintenance of network connectivity in the event of network fragmentation emanating out of rapidly spreading uncontained fire outbreaks. Chapter 7 puts forth the conclusion of this thesis work, along with the future course of work to be undertaken.
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    Microwave Sensing for Non-Destructive Evaluation of Anisotropic Materials With Application in Wood Industry
    (Auckland University of Technology, 2012) Bogosanovic, Mirjana
    Microwave non-destructive testing of wood is an active research field, but, despite remarkable advances reported in the literature to date, the wood testing devices are not widely implemented in industry. This thesis aims to progress the knowledge on wood testing by investigating two of the key issues: microwave propagation through dried wood and sensor design. Two microwave antennas with focused beam are designed and implemented. First antenna is a commonly used horn with a dielectric lens, offering a broadband solution, operating over the 8 to 12.4 GHz frequency band. The second solution is a novel metal plate lens antenna with beam forming in the near field zone. A successful beam forming and focusing is achieved, but a narrowband characteristic prevented application of this sensor for microwave wood testing considered in this thesis. A microwave system for a free-space measurement of wood properties is, in its various forms, applied to measurement of wood properties, considering wood as an anisotropic, heterogeneous and multiphase dielectric. Microwave free-space transmission measurement methods are considered, analysing error sources and available mitigation techniques. A focused-beam transmission measurement setup with free-space calibration has been identified as an optimum solution for microwave wood testing. The properties of this measurement system are analysed, having in mind its application for wood measurement in industrial environment. The samples for the study are carefully chosen to cover a range of features frequently met in practice. The ‘actual’ sample properties, against which the performance microwave measurements are judged, are determined using visual inspection and CT scan. The theoretical background on electromagnetic wave propagation through anisotropic media is considered. Of particular interest is depolarisation of a linear plane wave in anisotropic media, which is also demonstrated experimentally. A simple case of grain inclination in a plane is considered first, demonstrating experimentally that grain inclination directly relates to the level of depolarisation. This is then applied to a general case, in which the grain is inclined in three-dimensional space. It is shown that the technique has a good correlation with visually inspected grain angle values, but additional sensor calibration is recommended. Heterogeneity of the sample is analysed using the same set of sensors, but in different arrangement. The aim was to detect variations in wood structure and investigate a method for automated categorisation of wood samples, based on the type of defect. The categorisation of samples is considered as a way to combat a great variability in sample properties and allow easier and more accurate empirical modelling. The microwave transmission measurement data are compared with CT scans and visual inspection of samples. Good results are achieved, not only for samples with distinctive defects such as knots, but for samples with needle flecks, resin pockets and change in annual ring arrangement along the axial direction. Heterogeneity study is then extended to include an analysis of effects which gradual variations in wood structure have on the measured microwave signal. The obtained results show that phase of the microwave transmission coefficient can be used as a good indicator of slow variation in sample density. The study also includes an analysis of free space calibration and broadband transmission measurement, investigating its positive sides such as improved accuracy, as well as its negative sides such as complexity which these procedures introduce in an industrial process. Techniques for combating residual error are investigated, offering the frequency averaging as an easily implemented option. The importance of working over a frequency bandwidth is demonstrated, for dealing with phase periodicity as well as combating measurement uncertainty. Response calibration is considered as an affordable option which can remove some of the systematic errors, yet is less disruptive for the industrial process. Furthermore, both moisture content and density distribution are considered, as well as bulk properties, averaged over the whole sample volume. It has been demonstrated that both moisture and density of wood contribute to the changes in microwave transmission coefficient. Measured data reveal a polarisation dependence of the moisture related transmission magnitude, which may be used as additional information in attempt to distinguish between the contributors. This was further investigated on the set of samples observed at several moisture content values. The correlation between bulk density and microwave measured density improves when samples with knots are omitted, demonstrating advantage of sample categorisation. In the final section of the thesis, the scattering experiment is performed, measuring the transmission through the wood when transmitting and the receiving antenna axes are at the right angle. This experiment shows that maximum transmission in this direction correlates best with the arrangement of annual rings in the sample, indicating possible existence of guided modes in the layered media. This finding is significant as it demonstrates the complexity of microwave propagation model for the sample with such complex structure.
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    Motors Fault Recognition Using Distributed Current Signature Analysis
    (Auckland University of Technology, 2012) Gheitasi, Alireza
    Immediate detection and diagnosis of existing faults and faulty behaviour of electrical motors using electrical signals is one of the important interests of the power industry. Motor current signature analysis is a modern approach to diagnose faults of induction motors. This thesis investigates the significance of propagated fault signatures through distributed power systems, aiming at explaining and quantifying different observations of faults signals and hence diagnoses machine faults with a higher accuracy. Electrical indicators of faults, unlike other fault indicators, (e.g. vibration signals), propagate all over the network. Therefore fault signals may be manipulated by operation of neighbouring motors and the system‘s environmental noise. Both simulation and practical results clearly demonstrate the signal interference and hence confusion in diagnosis due to presence of a faulty motor nearby. Thus a knowledge based system is necessary to understand the meaning of the signals manifested at various parts of the distributed power system. On another side, taking into account that fault signals are travelling all over the network, several observations can be made for events in the network. In this thesis the idea of cross evaluation of fault signals considering signal propagation will be discussed and analysed. The research attempts to improve diagnosis reliability with a simple and viable framework of decision making. The thesis scope is limited to monitoring behaviour of induction motors in distributed power systems. These types of electrical motors are the main load of most industries. In this thesis, existing formulations of fault signatures would not be significantly disturbed, as distributed diagnosis can fit into an existing framework of current signature analysis. The research takes advantage of multiple areas of study to formulate propagation of fault signals while they are travelling in a scaled down distributed power system. At the beginning, a systematic approach has been employed to estimate influence of fault signals in currents of neighbouring electrical motors. Further analysis in attenuation of electrical signals leads to a technical framework that evaluates propagation of fault signals in power networks. The framework has been developed to estimate origin of fault signal by employing propagation patterns and estimating anticipated fault representatives around the network. An analytical process has been proposed to take advantage of multiple observations in order to diagnose the type and identify origin of fault signals. This can help maximize the number of independent observations and thus improve the accuracy of traditional approaches to current signature analysis. In general, this provides a better monitoring of behaviour of electrical motors at a given site. A rewarding system has been used to identify and track the signals caused by motors and quantify association of current signals with known industrial faults. An example of a scaled down distributed power system has been simulated to describe behaviour of distributed power systems with faulty components. The simulation model is carefully compared with the practical results to validate the simulation results thoroughly. Type and strength of faults and size, speed, load and placement of electrical motors are acting variables in propagation patterns of fault signals. These variables have been simulated in a scaled down industrial power network to examine distributed diagnosis in the new environment. In addition a number of scaled-down experiments have been employed to verify results of simulation models and confirm the accuracy of results. Analytical results demonstrate significant improvement in describing interference amongst electrical motors that work together in an electrical network. This leads to a simple strategy for identifying the ownership of fault signals and hence having more accurate diagnostic results. Further developments in modelling the propagation of fault indicators emerged for improving the reliability and efficiency of fault diagnosis in industrial systems. On the other hand, a number of shortcomings have been observed in implementing strategy of distributed diagnosis including confusion among many similar faults in the power network and malfunctioning of the diagnosis system due to non-linear interferences of noise signals. Some of these problems are believed to be solvable by using a proper numerical solution (e.g. Artificial Neural Network, Bayesian, etc.) to process fault indices and propagation patterns before and after occurrence of each fault. In conclusion, the thesis does not claim to provide a complete solution of fault diagnosis in electrical motors. But it is an attempt to provide a more dependable industry solution for fault diagnosis in induction motors. Distributed diagnosis is a framework which takes advantage of multiple observations of a single fault and hence it is dependent on quality of acquired signals among individual observations.
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    Network Trustworthiness Evaluation in P2P Networks
    (Auckland University of Technology, 2018) Xiang, Ming
    Trust and reputation management emerges as a significant research trend, in term of soft security to tackle the security issues in computer networks. It is different from the traditional security mechanisms such as cryptography that is described as hard security. The basic idea is that every entity in the network, as an individual, can rate each other based on previous experiences. This rating on trust can assist other machines in deciding whether to collaborate with that machine in the future. Recently there has been a rapid increase in literature on trust and reputation management that mainly focuses on algorithmically modelling and evaluating the trust to effectively detect and avoid various malicious attacks. These trust algorithms can isolate the malicious entities from the local trust aspect. While the concept of trust in the computer network is derived from the sociology, and in sociology, it is defined as the belief that trustees will have a positive expectation of intention and behaviours. Moreover, the trustee at different positions will behave differently, such as at the Structural Hole or the position surrounded with Simmelian Ties. Do these position-based phenomena also exist in computer networks? In other words, in computer networks, is the location of a node will affect its behaviours, especially in the emerging peer-to-peer (P2P) network architecture? Motivated by above research questions, in this thesis, we have focused on studying how the underlying network topological connectivity can affect the overlay trust behaviours from the global network perspective. This thesis has four main contributions. Firstly, we have revealed the underlying topology impact on the overlay trust behaviours in P2P networks. We have confirmed the correlations between the topological structures of Simmelian Ties and Structural Hole, and the node trustworthiness behaviours. Secondly, we have defined a new term of network trustworthiness to describe the trust level on a network topology. This is followed by introducing the Network Trustworthiness as a Service (NTaaS) concept, which can be adapted to accommodate the different levels of trust service demands from the users. Thirdly, we have proposed the $T$ value and Trustworthiness Tolerance Margin (MTT) based evaluation framework to evaluate the trustworthiness of the network topologies from the global aspect. Lastly, we have proposed a mathematical approach to optimise the network topology by adding a link in the most critical position so that the underlying network structures can best resist various unwanted behaviours and network failures.
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    Object-centric Intelligence: Sensor Network and Thermal Mapping
    (Auckland University of Technology, 2013) Yamani, Naresh
    Quality of product is an important aspect in many commercial organizations where storage and shipment practices are required. Temperature is one of the main parameters that influence quality and temperature treatments of agricultural products therefore require special attention. The temperature variation in a meat chiller has a significant effect on tenderness, color and microbial status of the meat, therefore thermal mapping during the chilling process and during chilled shipment to overseas markets is vital. The literature indicates that deviations of only a few degrees can lead to significant product deterioration. There are several existing methods for thermal mapping: these includes Computational Fluid Dynamics (CFD), Finite Element Methods (FEM) for examination of the environmental variables in the chiller. These methodologies can work effectively in non real-time. However these methods are quite complex and need high computational overhead when it comes to hard real-time analysis within the context of the process dynamics. The focus of this research work is to develop a method and system towards building an object-centric environment monitoring using collaborative efforts of both wireless sensor networks and artificial neural networks for spatial thermal mapping. Thermal tracking of an object placed anywhere within a predefined space is one of the main objectives here. Sensing data is gathered from restricted sensing points and used for training the Neural Network on the spatial distribution of the temperature at a given time. The solution is based on the development of a generic module that could be used as a basic building block for larger spaces. The Artificial Neural Networks (ANNs) perform dynamic learning using the data it collects from the various sensing points within the specific subspace module. The ANN could then be used to facilitate mapping of any other point in the related sub-space. The distribution of the sensors (nodes placement strategy for better coverage) is used as a parameter for evaluating the ability to predict the temperature at any point within the space. This research work exploits the neuro Wireless Sensor Network (nWSN) architecture in steady-state and transient environments. A conceptual model has been designed and built in a simulation environment and also experiments conducted using a test-bed. A Shepard’s algorithm with modified Euclidian distance is used for comparison with an adaptive neural network solution. An algorithm is developed to divide the overall space into subspaces covered by clusters of neighbouring sensing nodes to identify the thermal profiles. Using this approach, a buffering and Query based nWSN Data Processing (QnDP) algorithm is proposed to fulfil the data synchronization. A case study on the meat plants cool storage has been undertaken to demonstrate the best layout and location identification of the sensing nodes that can be attached to the carcasses to record thermal behavior. This research work assessed the viability of using nWSN architecture. It found that the Mean Absolute Error (MAE) at the infrastructural nodes has a variation of less than 0.5C. The resulting MAE is effective when nWSN can be capable of generating similar applications of predictions.
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    Post-Operative Hip Fracture Rehabilitation Activity Movement Monitoring
    (Auckland University of Technology, 2022) Gupta, Akash
    Hip fracture incidence is a life-threatening event that increases with age and is common among the older population. It causes significant problems as there is an increased risk of mortality, restriction of movement and well-being, loss of independence, and other adverse health-related concerns for the injured. Following surgery, physiotherapy is essential for strengthening muscles, mobilizing joints, and fostering the return to regular physical activity. Ideally, appropriate rehabilitation with a set programme performed under a predefined supervised and unsupervised environment can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. Tracking, recording, and continuous real-time monitoring of activity movements can significantly help in following up the correct implementation of a predefined programme. The ever-increasing technology such as the Internet of Things (IoT), which produces advancements in digital health revolutionizing industries and markets could be useful in advancing conventional rehabilitation care. This will also aid in enhancing backup intelligence used in the rehabilitation process, and will provide transparent coordination and information about activity movements among relevant parties. This thesis provides a motivational background for the problem and a critical literary analysis of the key components involved in structuring an IoT-based rehabilitation care monitoring system. The thesis proposes and presents a post-operative hip fracture rehabilitation model from the existing rules and health programmes. The model reflects the key stages a patient undergoes straight after hospitalisation, and provides clarification for the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. Considering the model monitoring requirements, the thesis highlights the system modelling and development tools for testing the proof-of-concept and overall conceptual ideology. To support this model, the thesis proposes an IoT-enabled wearable movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real-time and throughout the rehabilitation process. The conceptual ideology was tested incrementally on ten young and healthy subjects, for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the recognition of the hip fracture rehabilitation activity movements based on frequency-domain analysis and concerning sensor localisation. Research findings suggested that the amplitude parameter was suitable for the classification of the static state of a patient and ambulatory activities. Whereas, for the hip fracture related movements, both the frequency content and related amplitude of the acceleration signal play a significant role. From the analysis, the ankle is considered to be an appropriate sensor location that can categorise the majority of the activity movements thought to be important during the rehabilitation programme and data collection time of four seconds is considered to be the minimum time for recognising a particular activity movement without any loss of information or signal distortion. Furthermore, the thesis presents the importance of personalisation and one-minute history of data in improving recognition accuracy and monitoring real-time behaviour. This thesis also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements. Finally, this thesis offers a clearly-defined structured rehabilitation follow-up programme use case and conclusion with an indication of our future research work.
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    Rendezvous in Cognitive Radio Ad-Hoc Networks
    (Auckland University of Technology, 2015) Hossain, Md Akbar
    Cognitive radio (CR) is a promising technique to enhance the spectrum utilisation by enabling the CR users to opportunistic access the spectrum holes or channels. CR ad hoc network is a multi-channel environment where channel status changes over time depending on primary users’ (PUs) activities. Analogous to control channel establishment in traditional multi-channel ad hoc network, rendezvous in CR ad hoc network is one of the most important processes for a pair of unknown CR users to initiate communication. Most of the existing research have utilised a common control channel to achieve rendezvous. This utilization generates channel saturation, extreme transmission overhead of control information, and a point of vulnerability. The traditional designs for rendezvous protocols do not support an ad-hoc CR network model. Therefore, this thesis is focused on improving control channel establishment to solve the rendezvous problem and support further CR ad-hoc networks. This thesis proposes a new channel hopping (CH) scheme called extended torus quorum channel hoping (ETQCH) for asymmetric and asynchronous pair wise RDV in CR ad-hoc networks. The ETQCH employs channel ranking information for allocating more slots to high-rank channels than low-rank ones. The system dynamically updates the CH sequence by replacing channels from both the licensed and unlicensed bands to protect intermittent PUs. Channel hopping sequence scheme is a mathematical concept to guarantee overlap between two CR users. A successful RDV establishment depends on successful channel probe or control packet exchange which is a MAC layer issue. Therefore, a new MAC protocol named cognitive radio rendezvous (CR-RDV) MAC is proposed to facilitate the multiuser contention in CR ad-hoc networks. CR-RDV is developed by re-defining the traditional backooff procedure and incorporating a sensing period immediately after the request-to-send; the incumbent PU’s transmission is protected and blocking problems are resolved. The analysis and simulation results show the potential to minimise service interruption, block node problems, and efficiently utilise dynamic radio resources. The thesis also provides a guideline for CR system planners to design and deployment of dense networks with active PUs.
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    Sensor Network Embedded Intelligence: Human Comfort Ambient Intelligence
    (Auckland University of Technology, 2013) Mohamed Rawi, Mohd Izani
    This study explored the multidiscipline domain of the Wireless Sensor Network (WSN) and Ambient Intelligence (AmI) in addressing the problem of the comfort of a living space. This thesis addresses the potential for embedding an intelligent engine into WSN and the aggregation of multiple comfort factors in a living space. The four most important comfort factors for humans are taken into account. These are thermal comfort, visual comfort, indoor air comfort and acoustical comfort. This thesis introduces a WSN based embedded intelligent system architecture and a system framework for a living space’s comfort level. Human Comfort Ambient Intelligence System (HCAmI) architecture is presented. The HCAmI key component encompasses a flexible generic distributed fuzzy engine embedded within WSN nodes. The engine serves as a key knowledge component in solving specific human comfort requirements. With the proliferation of pervasive computing, there is an increasing demand for the inclusion of WSN in wider areas such as buildings, living space, system automation and much more. Focusing on buildings and living space alone, multitudinous studies have been made of environmental comfort for occupants. A smart environment and low energy homes are amongst the driving forces behind this research. Also, WSN research has been progressing well and expanding into various aspects of life such as support of the elderly, environmental senor, security and much more. Unfortunately, separate studies have been conducted in their own discipline focusing on specific issues and challenges. Little attention has been paid to putting it together under one roof. Lack of interdisciplinary research inspired this effort to unite these unconnected research domains. This has acted as the key motivational catalyst. The motivation behind combining these effects brings us to the specific issue of the human comfort realm that prompted this study. Human comfort deals with providing a comfortable and healthy place for people to live. Hence, in a living space, other than good design and construction, it is essential to monitor and maintain the modifiable environment such as temperature, lighting, humidity, noise, air quality and psychological factors. Functional environmental comfort system adaptability and the WSN system determination to solve the problem is a fascinating issue that certainly warranted further investigation. The HCAmI concept was designed and implemented based on a knowledge based architecture and framework. This approach addressed the component level first, catering for the four key human comfort factors. The system level design was then looked at. Each individual component was subjected to simulated and real sensor data and tested against a corresponding model built using appropriate tools such as the MATLAB Simulink and Sun SPOT Solarium WSN simulator. The HCAmI System was used to collect raw data from 20/04/2010 to 26/08/2010 (four months of data) in the SeNSe Laboratory, School of Engineering. A short snapshot of the collected data (from 08:00am 25/08/2010 to 11:40am 26/08/2010) is presented as a case study. The main achievement / contribution of this thesis is a distributed fuzzy logic based wireless sensor node in the human comfort realm. The framework, architecture and development of an integrated human comfort concept could be embedded in a wireless sensor network environment. The modular architecture and framework presented here highlights the flexibility and integrated approach of the design. The knowledge component of each comfort area can be changed easily and adding or removing comfort components is catered for as well. Overall, this thesis adds to the WSN body of knowledge in an embedded distributed generic fuzzy engine, thermal comfort engine, spatial sensing engine, human comfort index engine, application layer communication protocol and specific external sensor driver development and interface for Sun SPOT WSN.
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    Smart Sensor Network Organization: Sensor Data Fusion and Industrial Fault Traceability
    (Auckland University of Technology, 2015) Altaf, Saud
    The industrial environment usually contains multiple motors that are supplied through a common power bus. The power-line acts as a good conducting environment for signals to travel through the power-line network. In effect, this influences other motors with noisy signals that may indicate a fault condition. Further complexity arises when signals are generated by motors with different power ratings, a different slip speed and more than one source of fault signals. This sort of complexity and mixed signals from multiple sources makes them difficult to measure and precisely correlate to a given machine or fault. Generally, an industrial power-line network consists of different sizes of induction motor from small to large, which together can have a considerable combined influence on the overall system’s operation. The combined effect of all these induction motors can have a strong impact on power-line network permanence. In this thesis, the concept of cross evaluation of motor fault signals is considered to be signal propagation manifesting into healthy signal. Different concepts relating to propagation and manifestation of faults will be discussed and analysed. Initially, a systematic technique was employed to analyse the influence of the fault electric current signals of different motors within a power-line network. Further analysis analysed the attenuation ratio of electrical signals that leads toward a technical framework which evaluates the strength of signal propagation over a power-line network. The diagnostic process was demonstrated at individual sensing points to estimate the strength of propagated signals and identify fault points. This proved very helpful in maximizing the different independent observations. A sample industrial distributed motor network was simulated, to observe the behavior of a distributed power-line network in the presence of fault components. The multi-motor dynamic simulation model was developed, to compare the results with the test-bed practical results, to validate the acquired data. A number of case scenario experiments was done to verify the simulation results and validate the accuracy of these results. In this research, analytical results present significant improvements in describing the interference of faulty signals amongst motors running parallel to the power-line network. Some shortcomings were observed while implementing the strategy of distributed fault diagnosis, including false identification of similar types of fault symptom in power-line network and failure of the diagnosis system due to interference from non-linear noisy signals travelling within multi motor network. Some of these complications are supposed to be solvable by using an efficient and proper knowledge-based numerical technique. Furthermore, the focus of this research was also to develop a wireless node-level feature extraction technique for data fusion, using MCSA at end node-level. Decision-level fusion was implemented at the node coordinator for efficient fault diagnosis. In conclusion, this research does not claim to provide a complete solution to cover all types of fault diagnosis in electric drives. But it is a fitting attempt to provide a more reliable industry solution for motor fault diagnosis.
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    A Strict Quality of Service MAC Framework for Emergency Traffic in Wireless LANs
    (Auckland University of Technology, 2019) Memon, Shuaib Karim
    The increasing usage of wireless local area networks (WLANs) in distributed emergency services (e.g., for natural or manmade disasters, telemedicine, and health care) and other time-critical applications requires an efficient medium access control (MAC) protocol. This MAC protocol would support emergency traffic and provide a strict quality of service (QoS) guarantee in saturated emergency applications where a high number of nodes report an emergency. The IEEE 802.11e working group enhanced the 802.11 MAC protocol to support QoS. However, recent studies have shown that the 802.11e standard has limitations since it neither supports emergency traffic nor provides a QoS guarantee under medium-to-high traffic loads. In this thesis, a strict QoS MAC framework for emergency traffic in distributed WLANs under medium-to-high traffic loads is investigated. This framework is based on novel MAC protocols supporting emergency traffic in WLANs. This research first proposes a multiple preemptive MAC protocol (termed as multi-preemptive enhanced distributed channel access [MP-EDCA]), which was developed by modifying an enhanced 802.11e standard (EDCA) in which high priority emergency traffic is given the privilege to preempt the low priority traffic in accessing the medium. A significant network performance gain with respect to lower delays for lifesaving emergency traffic is obtained with MP-EDCA under medium-to-high traffic loads. The improved performance is achieved by modifying the slot time and short inter-frame space in the frame header. One of the most crucial mechanisms for providing a strict QoS guarantee in WLANs is admission Control. The admission control estimates the state of the network’s resources and thereby decide the emergency traffic flow that can be admitted without promising more resources than are available and therefore violating a previously made guarantee. Thus, a preemptive admission control MAC protocol is developed and reported in this thesis to support a strict QoS guarantee for emergency traffic in WLANs. To serve more emergency nodes in WLANs, it is useful to be able to redesign frame aggregation and BlockAck method of MP-EDCA protocol mentioned earlier. The frame aggregation with a simple 2-bit BlockAck scheme (called FASBA) for MP-EDCA is investigated in this thesis. FASBA enhances the capabilities of MP-EDCA, provides assurance of service delivery and offers higher throughput performance by reducing MAC transmission overheads.
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    Wavelet based OFDM with V-BLAST Virtual MIMO for Wireless Multimedia Sensor Networks
    (Auckland University of Technology, 2014) Rafique, Zimran
    Wireless sensor networks (WSNs) are finding their place in many real life applications because low power and small-size sensor nodes can be inexpensively and easily deployed in the areas of interest for different applications. Wireless multimedia sensor networks (WMSNs) consist of wireless nodes capable of producing multimedia (image/video) data streams that will enable a new generation of WSN applications. The transmission of multimedia content involves high volume data communication that may require significant bandwidth and energy resources. Hence, supporting high data rate while maintaining energy efficiency is a key challenge of WMSNs. Multi-Input Multi-Output (MIMO) techniques can be used to increase the data rate for a given bit error rate (BER) and transmission power. Due to the small form factor, energy and processing constraints of WSN nodes, sometimes it is not feasible to equip the nodes with multiple antennas. Virtual MIMO as opposed to True MIMO system architecture is considered more feasible for WSN applications. In this thesis, we analyse the performance of WSN with Virtual MIMO system architecture at transmitter side, and True or Virtual MIMO system architecture using Vertical Bell Laboratories Layered Space Time (V-BLAST) signal processing technique at receiver side. We investigated for the first time, the impact of different modulation techniques on the performance of a Virtual MIMO system based on V-BLAST architecture with multi-carrier modulation techniques in the context of WMSNs. Through analytical models and simulations using real hardware and environment settings, both communication and processing energy consumptions, BER, spectral efficiency, and total time delay performances have been analysed. The results show that Virtual MIMO system with Binary Phase Shift Keying-Wavelet based Orthogonal Frequency Division Multiplexing (BPSK-WOFDM) modulation is a promising solution for future high data-rate and energy-efficient WMSNs. This research also proposes a new channel equalisation technique which uses Quadrature Mirror Filter (QMF) bank architecture that is also found in WOFDM modulator and demodulator. The proposed technique is found to perform better in terms of BER, energy efficiency, and total time delay as compared to QR detection process. In this thesis, we also present a novel method to mitigate the problem of phase offset, which is a major issue affecting the performance of Virtual MIMO systems. We optimise the wavelet bases of BPSK-WOFDM technique using genetic algorithm (GA) to compensate for unwanted phase differences between sensor nodes in a Virtual MIMO WSN. Results show that the optimised BPSK-WOFDM technique can effectively mitigate the phase offset issue.
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