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Advances in energy harvesting hardware have created an opportunity to realise self-powered wearables for continuous and pervasive Human Activity Recognition (HAR). Unfortunately, the power requirements of continuous activity sensing using accelerometer sensors and burdensome on-node classification are relatively high compared to the amount of power that can be practically harvested, which limits the usefulness of energy harvesting. This thesis makes three fundamental contributions. First, we propose HARKE, Human Activity Recognition from Kinetic Energy, a novel approach to HAR that does not use an accelerometer. Instead, HARKE employs and infers human physical activities directly from the Kinetic Energy Harvesting (KEH) patterns generated from a device that harvests kinetic energy to power the wearable device. We also show the ability of HARKE to detect related details such as the steps taken by the user in a walking activity. By not using an accelerometer, a significant percentage of the limited harvested energy can be saved. Second, we introduce a novel framework that reduces the on-node classification overhead and guarantees energy neutrality. The proposed framework transmits an unmodulated signal, called an activity pulse, and uses only the received signal strength of the activity pulse to classify human activities. Neither accelerometer nor classifier is required on the wearable device, which therefore, guarantees energy neutrality. Finally, we validate the feasibility of using KEH patterns generated from human speech as a potential new source of information for detecting hotwords, such as ``OK Google", which are used by voice control applications to differentiate user commands from background conversations. Unlike methods that use existing sensors like microphones or accelerometers, our proposal enables pervasive voice control and HAR at a minimum energy cost.We believe that the findings in this thesis will open the door for a new direction of research and dev

Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. The key objective of this thesis is to explore more advanced decision making tools that would enable an improved tradeoff between conflicting QoE metrics in vehicular environments. In particular, this thesis studies the effectiveness of Markov decision process (MDP), which is known for its ability to optimize decision making under uncertainty. The thesis makes three fundamental contributions: (1) using real video and network bandwidth datasets, it shows that MDP can reduce playback deadline miss of the video (video freezing) by up to 15 times compared to a well known non-MDP strategy when the bandwidth model is known a priori, (2) it proposes a Q-learning implementation of MDP that does not need any a priori knowledge of the bandwidth, but learns optimal decision making in a self-learning manner by simply observing the outcome of its decision making. It is demonstrated that, in terms of deadline miss, the Q-learning-based MDP outperforms the model-based MDP by a factor of three, and (3) it implements the proposed decision making framework in an

Advances in nanotechnology are paving the way for wireless nanoscale sensor networks (WNSNs), promising radically new applications in medical, biological, and chemical fields. However, the small scale poses formidable challenges for communication. First, small nanomaterial-based antennas communicate in the terahertz band, which coincides with the natural resonance frequencies of many types of molecules causing severe molecular absorption and noise. The problem is particularly complicated if the molecular composition of the channel changes over time, causing time-varying absorption and noise. Second, as it is not practical to fit large batteries or replace batteries in a small device, these devices are expected to power themselves by harvesting ambient energy from the environment. However, the amount of energy that can be harvested is directly proportional to the size of the harvester. A nanodevice therefore can generate only a tiny fraction of its total power consumption, which requires us to rethink the design of communication protocols for self-powering WNSNs.In order to address aforementioned challenges, this thesis makes three fundamental contributions. First, it proposes dynamic frequency and power selection as a means to overcome the first problem, i.e, changing molecular composition problem in a time-varying terahertz channel. The dynamic frequency/power selection problem is modelled as a Markov Decision Process to derive the optimal solutions, while several practical heuristics are proposed that achieve close to optimal solutions. Second, to address the severe power shortage problem in a self-powering nanodevice, this thesis proposes a mechanism to exploit the information contained in the energy harvesting data to detect the energy-dissipating events occurring in the environment. This form of event monitoring makes dual use of the energy-harvesting unit in the nanodevice, i.e., it is used to generate power as well as monitor the environment, thus saving sign

A novel method for nitrogen doping of graphene via solid-state impregnation was developed using graphene oxide (GO) as the raw substrate and aminoterephthalic acid as the doping agent via a facile thermal treatment at 750 °C. The structure, morphology and chemical composition of the synthesised N-doped graphene were characterised using XRD, SEM, EDS and XPS. The N-graphene product exhibits homogeneous doping with high nitrogen content (approx. 6 at%) in four configurations: pyridinic-N, pyrrolic-N, pyridinic-N-oxide and graphitic-N. The electric double layer capacitor (EDLC) fabricated using an N-doped graphene electrode attained a specific capacitance of 210 F g-1 (at a current density of 1 A g-1), which was greater than the values attained by pristine graphene and a GO electrode by factors of about two and six, respectively. Our synthesised N-graphene shows supercapacitance at a low electrolyte concentration compared to supercapacitors reported in the literature for high electrolyte concentrations with similar electrodes. The EDLC device we constructed based on N-graphene showed excellent charge-discharge stability for tests of up to 5000 cycles with high capacity retention (>90%). A comparison of the electrochemical performance of GO, graphene and N-graphene demonstrated that doping with nitrogen can dramatically enhance capacitance.

Graphene quantum dots (GQDs) with their edge-bound nanometer-size present distinctive properties owing to quantum confinement and edge effects. We report a facile ultrasonic approach with chemical activation using KOH to prepare activated GQDs or aGQDs enriched with both free and bound edges. Compared to GQDs, the aGQDs we synthesized had enhanced BET surface area by a factor of about six, the photoluminescence intensity by about four and half times and electro-capacitance by a factor of about two. Unlike their non-activated counterparts, the aGQDs having enhanced edge states emit enhanced intense blue luminescence and exhibit electrochemical double layer capacitance greater than that of graphene, activated or not. Apart from their use as part of electrodes in a supercapacitor, the superior luminescence of aGQDs holds potential for use in biomedical imaging and related optoelectronic applications.