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Advancements in nanotechnology promise new capabilities for the Internet of Things (IoT) to monitor extremely fine-grained events with sensors as small as up to a hundred nanometers. Researchers predict that such tiny sensors can be connected to the Internet using graphene-based nano-antenna radiating in the terahertz band, giving rise to the so called Internet of Nano-Things (IoNT). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. Since in many application domains, different types of events discharge different amounts of energy to the environment, we propose an energy-neutral event monitoring framework, called eNEUTRAL IoNT, that allows the sensors to transmit event information using only the amount of energy harvested from the events. We design and analyze two implementation methods for this framework. The first method uses a single pulse containing the entire energy harvested from the event but manipulates its pulse width (time duration) to create unique pulse amplitude for a given combination of event type and its location. In the second option, the harvested event energy is divided into two pulses so that the energy of the first pulse uniquely defines a location and the second pulse uses the remaining energy to identify event types. To minimize classification error at the receiver, we optimize pulse durations in the single-pulse option and pulse energies in the dual-pulse option. Feasibility of eNEUTRAL IoNT is demonstrated using extensive numerical experiments involving terahertz channels. We find that the dual-pulse approach significantly outperforms the single-pulse approach achieving 99\% accuracy for detecting both location and event type in 10-node network monitoring two different event types for a radius of 28 mm. As nanoscale energy harvesters and transmitters are still not available to realize operational event monitoring nodes, we, therefore, evaluate and design a key component of the IoNT

The evolutions in wireless communication, low-power electronics, and energy harvesting technologies have formed a new computing spectrum widely known as the energy harvesting powered Internet of Things (IoT). In this new vision, IoT devices can harvest energy from the ambient environment to power themselves and can operate for decades without the need for battery recharge. Energy harvesting powered IoT has been widely regarded as a promising candidate in realizing perpetual ubiquitous computing that ensures computing at any time and anywhere. However, the amount of energy that can be harvested is usually unpredictable and limited. Therefore, the high energy consumption in sensing and communication of the IoT devices has become the major impediment to fulfill our vision of permanent operation. This thesis makes four contributions to energy-efficient sensing and communication for kinetic energy harvesting (KEH) powered IoTs. The first contribution is the novel use of KEH signal for human-centric sensing. We propose a KEH-based transportation mode detection system that dramatically reduces the sensing power consumption of conventional system without sacrificing the recognition accuracy. As one step further, the second contribution explores the use of the energy storage component, i.e., capacitor, in kinetic-powered IoT as the sensor to detect human activities. It obviates the need for sampling the continuous motion signal during the activity detection period, whereas, it recognizes the activity by reading the capacitor once every few seconds. The third and fourth contributions of the thesis focus on the design of energy-efficient modulation schemes that enable data sharing between IoT devices using acoustic signal. While current solutions heavily rely on complex and energy-hungry digital signal processing (DSP) to obtain frequency-domain information from the time-domain microphone samples, the third contribution proposes the use of vibration energy harvesting (VEH) as

Advances in energy harvesting hardware have created an opportunity for realizing self-powered wearables for continuous and pervasive Human Context Detection (HCD). Unfortunately, the power consumption of the continuous context sensing using accelerometer is relatively high compared to the amount of power that can be harvested practically, which limits the usefulness of energy harvesting. This chapter employs and infers HCD directly from the Kinetic Energy Harvesting (KEH) patterns generated from a wearable device that harvests kinetic energy to power itself. This proposal eliminates the need for accelerometer, making HCD practical for self-powered devices. The authors discuss in more details the use of KEH patterns as an energy efficient source of information for five main applications, human activity recognition, step detection, calorie expenditure estimation, hotword detection, and transport mode detection. This confirms the potential sensing capabilities of KEH for a wide range of wearable applications, moving us closer towards self-powered autonomous wearables.

Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80% to 95%, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79% of the overall system power consumption of conventional accelerometer-based HAR.

The rapidly growing demand for higher networking capacity and data rates is forcing researchers to explore the unused spectrum in the higher frequency bands. Two such bands, the millimeter wave (mmWave), ranging from 30 GHz to 300 GHz, and the Terahertz (THz) band, ranging from 0.1 THz to 10 THz, are currently being investigated for possible use in future networks. Because many atmospheric molecules have their natural resonant frequencies in these bands, it is important to understand the effects of molecular absorption and re-radiation on the wireless networking performance in such high frequency bands. Building on the recently discovered molecular absorption models, this thesis conducts a theoretical study on the effect of molecular absorption and re-radiation on both single-antenna and multiantenna wireless communications. For the single-antenna communication, the study focuses on quantifying the temporal and spatial variation of path loss and noise, which is caused by variation in the molecular composition in the air. In particular, it studies the extent of spatio-temporal variation of mmWave channels in three largest cities of Australia by investigating the hourly air quality and weather data over 12 months. The study finds that mmWave channels experience significant variation in both space and time domains, which causes undesirable network capacity fluctuation in various places and hours. For the multi-antenna communication, the study yields a new theoretical discovery that the Multiple-Input and Multiple-Output (MIMO) capacity can be significantly influenced by atmosphere molecules. In more detail, some common atmosphere molecules, such as Oxygen and water, can absorb and re-radiate energy in their natural resonant frequencies, such as 60 GHz, 120GHz and 180 GHz, which belong to the mmWave spectrum. Such phenomenon can provide equivalent Non-Line-of-Sight (NLoS) paths in an environment that is dominated by Line-of-Sight (LoS) transmissions, and thus greatly im

Personal tracking has become an integral part of many context-based applica- tions that rely on user’s current location to deliver their services. Although satellite- based Global Positioning System (GPS) is a popular tracking technology, it is not reliable in indoor and underground tracking scenarios due to the lack of line-of-sight to the satellites. Consequently, smartphone sensors, such as accelerometers and magnetometers, are increasingly being used as a GPS-less tracking solution in such environments.This thesis examines two important GPS-less tracking scenarios, one involving an indoor pedestrian and the other an underground train passenger. For the first case, it considers pedestrian dead reckoning (PDR), which recursively updates the location of the pedestrian by using step length and step heading. A common method to estimate the heading in PDR is to use magnetometer measurements. However, unlike outdoor environments, the Earth’s magnetic field is strongly perturbed inside buildings making magnetometer measurements unreliable for heading estimate. This thesis presents a novel collaborative approach to reduce heading estimation errors when magnetometers are used. The proposed method consists of three components. The first component uses machine learning to detect whether a heading estimate is within a specific error margin. Only heading estimates within the error margin are retained and passed to the second component, which uses data fusion to average the heading estimates from multiple people walking in the same direction. The third component uses signal strengths of radio beacons transmitted by the users to detect the fellow pedestrians walking in the same direction. The rationale of this method is based on the observation that magnetic perturbations are often highly localised in space, and if multiple people are walking in the same direction, then only some of their magnetometers are likely to be perturbed. Extensive evaluations using real data show that

The recent move to empower sensor-rich vehicles with wireless communication technology has created the potential for a city-wide sensing platform that is an essential input to realise a wide range of traffic safety and efficiency applications, that are the building blocks of a future intelligent transportation system (ITS). In particular, many applications, such as adaptive traffic light adjustment, hazard road detection and congestion predictions, are envisioned based on the assumption of the availability of high quality floating car data (FCD). However, efficiently collect- ing and disseminating the huge amount of data generated by a variety of sensors mounted on moving vehicles remains a challenging task due to the limited channel resources, and the highly dynamic and disconnected network topology presented in vehicular networks. In this thesis, we address the above challenges and developed FCD systems in vehicular networks by exploiting the concept of mobile agents. A mobile agent is a self-contained software component that migrates from one host to another, executing application-specific functions. Being highly autonomous, a mobile agent is fundamentally resilient to network faults and offers significant advantages for data collection in highly dynamic and distributed environments. We first address the problem of harvesting FCD from operating vehicles to a remote FCD server, proposing a mobile agent based centralised FCD systems (CFCD) in heterogeneous vehicular networks. The proposed system, inspired by the theory of random walk on graphs, dispatches multiple mobile agents to collect road segment traffic aggregates from vehicles moving within a large urban region. We mathematically prove that given a sufficient number of mobile agents, the system is able to collect all road segments within the region, provides 100% collection coverage in the best case. The proposed CFCD system is further extended to develop a distributed FCD (DFCD) system, such that vehicles c

Recent advancements in drone technology and base station miniaturization, together with an urgent need to reduce site rental costs, have created the unique opportunity to deploy cellular networks on a platform of mobile drones. This new development is redefining the wireless networks, as drone base stations can autonomously move in space to improve coverage and capacity of the network, tremendously enhancing the Quality of Service for conventional cell-edge users.In this research, we explore the benefit of constantly moving drone base stations in the air to reduce the distance between the base stations and the mobile user equipments, thereby improving the performance of the cellular networks.In particular, this thesis makes three fundamental contributions.First, we analyse drone manoeuvrability using theory, emulation and real field experiments to find the relationship between flying speed, turning agility and energy consumption. Under the control of our developed Android program, we reveal some practical manoeuvrability factors that must be considered for the applications that require frequent changes of direction for the drone. Second, we propose drone mobility control algorithms to decide on drones' moving directions in order to improve the performance of drone base stations in the network area. As the optimal problem is NP-hard, we propose a range of practically realizable heuristics with varying complexity and performance. The proposed algorithms are evaluated taking the practical drones' limitations into account for micro hotspots scenario where many hotspots exist next to each other and a drone is deployed over each hotspot area. We show that our proposed heuristic algorithms can readily improve spectral efficiency by 34% and the 5th-percentile packet throughput by 50% compared to the scenario where drones hover over fixed locations. Third, we consider macro hotspot scenario, where users and drones can move freely in a large area. Particular challenges such a