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Vehicular ad-hoc networks (VANETs) that enable communication among vehicles and between vehicles and unmanned aerial vehicles (UAVs) and cellular base stations have recently attracted significant interest from the research community, due to the wide range of practical applications they can facilitate (e.g., road safety, traffic management and rescue missions). Despite this increased research activity, the high vehicle mobility in a VANET raises concerns regarding the robustness and adaptiveness of such networks to support time-critical applications and internetworking. In this thesis, as a first step toward the design of efficient MAC protocol to support time-critical applications and internetworking, we show that it is indeed possible to follow the dynamics of a network and consequently adapt the transmission probability of the Aloha protocol to reduce the interference and maximise the single-hop throughput between adjacent nodes. Extensive simulation validates the proposed analytical model, which thus can serve as a promising tool to improve VANETs performance. By exploiting the parallel between the CSMA/CA and Aloha performance models, the optimal transmission probability for the Aloha protocol as a function of estimated vehicular density is derived. This probability is then used to obtain the optimal maximum CW that can be integrated in an amended CSMA/CA protocol to maximise the single-hop throughput among adjacent vehicles. We show by means of simulation that the beneficial impact the proposed protocol is increased channel throughput and reduced transmission delay when compared with the standardised protocol CSMA/CA in IEEE 802.11p. These results reveal the applicability of the new, optimised protocol to safety applications and clustering techniques with stringent performance requirements. Lastly, we propose a Stable Clustering Algorithm for vehicular ad-hoc networks (SCalE) internetworking. The exchange of the necessary status information to support the efficient clusters formation can firmly relay on the support of our optimised CSMA/CA protocol. The SCalE algorithm makes use of the knowledge of the vehicles behaviour (explained in Chapter 5) for efficient selection of CHs, and selects a backup CH on top of the CH to maintain the stability of cluster structures. The increased stability and improved performance of the SCalE algorithm is studied and compared with existing clustering algorithms.

This thesis investigates inductive links used in wireless power transfer systems. Inductive power transfer can be used as a power delivery method for a variety of portable devices, from medical implants to electric vehicles and is gaining increased interest. The focus is on high quality factor coils and MHz operation, where accurate measurements are difficult to achieve. Fast models of all pertinent aspects of inductive power transfer systems for constant cross section coils are developed. These models are used to optimise a new coil winding pattern that aims to increase efficiency in volume constrained scenarios. Measurement systems are developed to measure coil Q factors in excess of 1,000. The prototype measurement systems are verified against models of that system, as well as finite element simulations of the coil under test. Shielding of inductive power transfer systems is then investigated. A structure typically used at GHz frequencies, the artificial magnetic conductor, is miniaturised as an alternative to conventional ferrite backed ground plane shielding. Finite element simulation shows this structure significantly improves link efficiency. The artificial magnetic conductor prototype does not result in a gain in efficiency expected, however it does display the properties expected of an artificial magnetic conductor, including increased coupling factor. Finally, an unconventional inductive power transfer system is presented where transmitter and receiver are up to 6m away from each other and of radically different size. This system provides mW level power to remote devices in a room, for example thermostats or e-ink displays. Conventional approaches to design do not consider the distortion of the magnetic field caused by metallic objects in the room. It was found that treating the system as a decoupled receiver and transmitter provides a better prediction of received power in real world environments.

One of the emerging problems facing populated urban areas represents high level of the air pollution. Current monitoring approaches assume processing of pollution data in a centralised manner, however due to external factors (e.g. current atmospheric conditions) the air pollution level can have fast fluctuations inside a street which might lead to disparity of the pollution levels in neighbouring streets. This thesis proposes a decentralised monitoring framework that enables harvesting of the pollution data, its processing, and dissemination of early warnings to the Static Monitoring Units (SMU) when the onsets of hazardous air pollution concentration are detected at the street (micro-scale) level. The proposed air pollution monitoring framework relies on a Vehicular Sensor Network infrastructure where vehicles have limited on-board resources, such as processing, battery power and storage. Three aspects of the micro-scale air pollution monitoring have been investigated. The Decentralised data Dissemination and Harvesting (DDH) is a single-hop dissemination mechanism which enables the nodes to decide whether they should harvest the data based on their current position and movement direction. This mechanism identifies the streets that nodes need to monitor by comparing the amount of stored pollution data with the one harvested from other nodes in the network. The Decentralised Data Fusion (DDF) algorithm is developed to fuse the delayed pollution data that arrives from other nodes in the network. The algorithm uses the Delayed State Information Filter to refine sensor measurements and applies an interpolation technique to interpolate the missing data in the time gaps which occur as a consequence of receiving delayed data. The proposed algorithm augments the pollution data history at the slight cost of data accuracy. The Decentralised Dissemination of Warnings (DDW) is a beaconless, multihop dissemination mechanism designed to relay early warnings to the SMUs. Mobile nodes calculate the time interval (waiting time) that they need to wait before they rebroadcast the warning message. The waiting time is calculated by taking into account the distance between sending and receiving nodes, and nodes’ distances to the SMUs. The DDW mechanism ensures that high number of non-duplicated warnings is received at the collection points (SMUs). The proposed framework enables efficient utilisation of node’s on-board resources in terms of transmitting and processing activity, and data storage. It reduces the number of mobile nodes required for monitoring purposes without losing the volume of the relevant collected pollution data. Using uncontrolled mobile nodes and their mobility, the framework enables reporting the hazardous pollution levels in near real-time.

Non-intrusive medical-grade accuracy respiratory monitoring is one of the two unresolved challenges in design of wearable vital signs monitoring system, besides blood pressure monitoring. The challenge of respiratory monitoring includes automatic breath detection, respiratory phase classification, and extraction of other respiratory related parameters. The main problem is that the sensed respiratory physiological signals could be heavily corrupted by artefacts and interference from non-relevant sources such as movement and other unwanted physiological signals collected at the same time. This thesis proposes a novel system which can detect respiratory activity from tracheal sounds recorded by a miniature wearable sensor with limited power budget, and can be tailored for variety of clinical scenarios by combining novel state-of-the-art techniques at both hardware and software level. The respiratory monitoring system presented in this thesis consists of two parts: one is a hardware platform for sensing breath sounds and the other is an algorithm for respiratory activity analysis. The low power sensor platform has been optimised for breath sound data acquisition by a combination of: a custom engineered acoustic chamber and a MEMS microphone; and optional power efficient data compression techniques which include the use of novel fixed length adaptive sample size (FLASS) frames optimised for Bluetooth® Smart wireless transmission protocol. The respiratory activity algorithm is based on 3-stage backward adaptive approach, using previous detected respiration’s parameters to fine tune the system and then balancing the system’s sensitivity and specificity in various ways to suit the need of different applications. These include sleep apnoea diagnosis, sudden unexpected death prevention in both epilepsy and babies, early warning scoring in hospitals and management of chronic conditions such as chronic obstructive pulmonary disease and asthma. A clinical trial focused on sleep apnoea detection was conducted in the National Hospital for Neurology and Neurosurgery, London, UK, in order to verify the respiratory monitoring system. Questionnaire results show that all participants scored the novel wearable respiratory sensor in the range 4 to 5, where 5 represents the best possible scoring in a number of attributes related to wearability. Experimental results show the proposed automatic apnoea and hypopnoea detection algorithm has 88.6% sensitivity and 99.6% specificity when comparing its performance with a specialist clinician considered as a gold standard. In comparison a state-of-the-art automatic ambulatory sleep diagnosis system has 14.3% sensitivity and 99.3% specificity.

Communication services such as telephony, broadband and TV are increasingly migrating into Internet Protocol(IP) based networks because of the consolidation of telephone and data networks. Meanwhile, the increasingly wide application of Cloud Computing enables the accommodation of tens of thousands of applications from the general public or enterprise users which make use of Cloud services on-demand through IP networks such as the Internet. Real-Time services over IP (RTIP) have also been increasingly significant due to the convergence of network services, and the real-time needs of the Internet of Things (IoT) will strengthen this trend. Such Real-Time applications have strict Quality of Service (QoS) constraints, posing a major challenge for IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement. Thus in this thesis we first describe our design for a novel ''Real-Time (RT) traffic over CPN'' protocol which uses QoS goals that match the needs of voice packet delivery in the presence of other background traffic under varied traffic conditions; we present its experimental evaluation via measurements of key QoS metrics such as packet delay, delay variation (jitter) and packet loss ratio. Pursuing our investigation of packet routing in the Internet, we then propose a novel Big Data and Machine Learning approach for real-time Internet scale Route Optimisation based on Quality-of-Service using an overlay network, and evaluate is performance. Based on the collection of data sampled each $2$ minutes over a large number of source-destinations pairs, we observe that intercontinental Internet Protocol (IP) paths are far from optimal with respect to metrics such as end-to-end round-trip delay. On the other hand, our machine learning based overlay network routing scheme exploits large scale data collected from communicating node pairs to select overlay paths, while it uses IP between neighbouring overlay nodes. We report measurements over a week long experiment with several million data points shows substantially better end-to-end QoS than is observed with pure IP routing. Pursuing the machine learning approach, we then address the challenging problem of dispatching incoming tasks to servers in Cloud systems so as to offer the best QoS and reliable job execution; an experimental system (the Task Allocation Platform) that we have developed is presented and used to compare several task allocation schemes, including a model driven algorithm, a reinforcement learning based scheme, and a ''sensible’’ allocation algorithm that assigns tasks to sub-systems that are observed to provide lower response time. These schemes are compared via measurements both among themselves and against a standard round-robin scheduler, with two architectures (with homogenous and heterogenous hosts having different processing capacities) and the conditions under which the different schemes offer better QoS are discussed. Since Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet, we also describe a smart distributed system that combines local and remote Cloud facilities, allocating tasks dynamically to the service that offers the best overall QoS, and it includes a routing overlay which minimizes network delay for data transfer between Clouds. Internet-scale experiments that we report exhibit the effectiveness of our approach in adaptively distributing workload across multiple Clouds.

This PhD thesis researches thermoelectric generator (TEG) which transfers wasted heat into electricity by thermoelectric materials. As a parameter used to characterize thermoelectric materials, figure-of-merit (ZT) models of Si bulk, Si/Si_(1-x) Ge_x bulk and Si bulk/nanowires (NWs) are built via building their models of Seebeck coefficient, electrical conductivity and thermal conductivity in this thesis. ZT of Si bulk is increased by 18% by applying a 3μm thick Si_0.8 Ge_0.2 bulk layer, and it is increased by 1000% by applying 35μm long Si NWs. TEG’s output power model which takes account of the effects of thermoelectric material, as well as all parasitic effects that affect TEG’s output power. TEG’s output power model demonstrates the output power depends on thermoelectric material’s characteristics and the contact interface quality between thermoelectric material and metal probe. Thermoelectric material’s characteristics are improved by Si NWs, Si_(1-x) Ge_x bulk, Si_(1-x) Ge_x NWs and spin-on-doping (SOD). SOD also improves the contact interface quality between thermoelectric material and metal probe, which also can be improved by sputter coating a layer of metal on thermoelectric material’s surface. Finally, TEG’s output power is increased by an order of 3 by these techniques.

As the minimum feature sizes of current integrated circuits approach 10 nm, improvements in the speed, complexity and packing density are becoming increasingly difficult. In particular, at these scale, the operation of 'classical' complementary metal-oxide-semiconductor (CMOS) devices is expected to degrade unacceptably. Single-electron devices, where the Coulomb blockade effect can be used to control charge at the one electron level, provide a means to fabricate large scale integration (LSI) circuits with ultra-low power consumption, immunity from charge fluctuations, and high scalability at sub-10 nm dimensions. Single-electron devices are potentially a successor technology to conventional classical Si metal-oxide-semiconductor field effect transistors (MOSFETs), and will play an increasingly important role both in future CMOS and 'beyond CMOS' technologies. In this thesis, we first introduce the history of single-electron (SE) effects and the previous work in both theory and practical fabrication. Subsequently, the theoretical operation of the single-electron transistor (SET) is discussed, followed by a brief introduction to the quantum dot (QD) and the multiple tunnel junction (MTJ) transistor. The fabrication process for SET devices in heavily doped, n-type silicon-on-insulator (SOI) material, using the electron-beam lithography (EBL), is then introduced. Two types of Si SET devices have been studied, the 1 μm nanowire (NW) SET and 'point gate' SET, which are both defined by EBL followed by reactive-ion etching (RIE) to create trench isolation of the devices, source, drain and nanowire regions. A thermal oxidation approach, was then used to reduce the Si core to the sub-10 nm scale in the NW. This passivates surface defects, creates charging 'islands' isolated by tunnel barriers and forms the SET. Variation in surface roughness, doping concentration and any disorder inherent at the nanoscale can form the tunnel barriers con ning the charging island. The SiNW SETs fabricated in this work have been electrically characterised at temperatures from 8 - 300 K. Results obtained from NWs with core widths from 5 nm to 40 nm with two di erent gate lengths of 1 μm to 50 nm have been compared. Here, detailed Ids vs. Vds, Vgs measurements have been performed at 8 K, and 'Coulomb diamond' characteristics have been observed. The 1 μm long NWs behave as MTJs, with 40 nm scale islands. Here, the width of the Coulomb diamond cannot be reduced to zero. The detailed temperature dependence of the Ids vs. Vds characteristics show that some SE effects persist even at 300 K. The reduction in NW gate length to 50 nm reduces the likelihood of quantum dots to only three dots, but increases their influence on the electrical characteristics. In the point contact device, QD behaviour with a combination of SE charging and quantum confinement effects is observed at 8 K. In a highly scaled point circuit Coulomb blockade and a single-electron oscillation are observed. Monte Carlo simulations have been used to further investigate the devices and their island con gurations. The results of this thesis demonstrate explicitly the signicance of quantum effects for the electrical performance of nominally 'classical' SiNW devices and highlight their potential for quantum effect 'beyond CMOS' devices.