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Piezoelectric energy harvesters (PEHs) can be used as an additional power supply for a Structural Health Monitoring (SHM) system. Its design can be optimised for the best performance; however, the optimal design depends on the input vibration, e.g. acceleration of a bridge due to wind loads and passing traffic. In previous studies, we have shown that optimal design tunes to some predominant frequency of the signal spectrum. However, our previous study was limited to a single location of a PEH on a bridge. In this work, we extend the rigorous optimisation framework to include the effect of the PEH's location. The optimisation framework uses the PEH plate model discretised by IsoGeometric Analysis (IGA) and coupled with the Particle Swarm Optimisation algorithm to find the designs with maximum energy outputs for many acceleration histories, extracted from the recorded dynamic response data of a real cable-stayed bridge in Australia. Then, clustering is performed to find several best candidates for the entire bridge. An additional study is performed to quantify the effect of traffic intensity on the produced energy. It is shown that variation in traffic volume throughout the 24hr time window leads to variation in optimal PEH designs. The study concludes the impact of location and traffic on energy harvesting by identifying the optimal PEH design and placement throughout the bridge structure. The results indicate that the key factors of maximising energy harvesting efficiency are related to the input excitation and the mode of vibration being excited. The position of maximum displacement in the vibration mode corresponds to the best location for energy harvesting. Also, the change in traffic intensity affects the amount of convertible mechanical energy and also directs the fundamental frequency of a PEH to shift within a specific range of frequencies to achieve the highest energy conversion.

The primary goal of this thesis is to investigate how the service-oriented architecture can be extended to mobile environments, facilitating resource constrained mobile devices to host services. With the advances in mobile devices and wireless communications, the opportunities to run resource-demanding applications on the mobile devices are growing. The increasing processing power, storage and support of multiple network interfaces enable the mobile devices to host services and participate in service discovery networks. These characteristics facilitate that mobile devices can act both as service consumers and service providers. Besides, roaming of mobile devices across wireless networks provides nomadic characteristics to the service providers to be available anytime and anywhere. As in web services architecture, service consumers and providers are loosely coupled and dynamically bound; this architecture is especially advantageous for the frequently changing mobile environments. -- Nevertheless, service provisioning from mobile devices is challenging, as the resources are far more constrained compared to the Internet servers that are originally targeted for web services technologies. Mobile devices have limited processing power and memory space; and suffer from lower bandwidths, higher error rates, and frequent disconnections compared to fixed networks. A few efforts have been taken to verify the feasibility of provisioning web services from mobile devices. However, they have not addressed the issue about how to host heavy-duty services on resource-constrained mobile devices with limited processing power, memory space, and transmission bandwidth. -- This thesis attempts to overcome the issues by introducing a framework which partitions the workload of resource-demanding services involving complex business processes and keeps the web service interfaces on mobile devices. The framework effectively leverages the capability of mobile devices, by offloading the partitioned computing load to resource-rich surrogates. Therefore, the mobile device works as the integration point with the support of surrogate nodes and other web services. The functions that require the resources of the mobile device or interaction with the mobile user are executed locally. -- The framework introduces an efficient partitioning approach for execution offloading from resource constrained mobile devices. The proposed approach considers the dynamic status of the resources of a mobile device to partition the tasks effectively. The framework provides a distributed platform for executing services, which adaptively offloads by considering both the interaction properties and the resource consumption of the tasks. The designed framework is analyzed using prototype experiments and the results have shown the effectiveness and efficiency of the approach in provisioning heavy-duty services from resource constrained mobile devices.

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

We propose a novel use of the conventional energy storage component, i.e., capacitor, in kinetic-powered wearable IoTs as a sensor to detect human activities. Since different activities accumulate energies in the capacitor at different rates, these activities can be detected directly by observing the charging rate of the capacitor. The key advantage of the proposed capacitor based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal during the activity detection period thus significantly saving power consumption of the wearable device. A challenge we face is that capacitors are inherently non-linear energy accumulators, which, even for the same activity, leads to significant variations in charging rates at different times depending on the current charge level of the capacitor. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate on charging cycles that are approximately linear. We design and implement a kinetic-powered shoe sole and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 73% less system power compared to conventional motion signal based activity detection.

We study a cellular networking scenario, called DroneCells, where miniaturized base stations (BSs) are mounted on flying drones to serve mobile users. We propose that the drones never stop, and move continuously within the cell in a way that reduces the distance between the BS and the serving users, thus potentially improving the spectral efficiency of the network. By considering the practical mobility constraints of commercial drones, we design drone mobility algorithms to improve the spectral efficiency of DroneCells. As the optimal problem is NP-hard, we propose a range of practically realizable heuristics with varying complexity and performance. Simulations show that, using the existing consumer drones, the proposed algorithms can readily improve spectral efficiency by 34\% and the 5-percentile packet throughput by 50\% compared to the scenario, where drones hover over fixed locations. More significant gains can be expected with more agile drones in the future. A surprising outcome is that the drones need to fly only at minimal speeds to achieve these gains, avoiding any negative effect on drone battery lifetime. We further demonstrate that the optimal solution provides only modest improvements over the best heuristic algorithm, which employs Game Theory to make mobility decisions for drone BSs., Comment: 14 pages, under review