Sustainable Radio Frequency Wireless Energy Transfer for Massive Internet of Things

Reliable energy supply remains a crucial challenge in the Internet of Things (IoT). Although relying on batteries is cost-effective for a few devices, it is neither a scalable nor a sustainable charging solution as the network grows massive. Besides, current energy-saving technologies alone cannot cope, for instance, with the vision of zero-energy devices and the deploy-and-forget paradigm which can unlock a myriad of new use cases. In this context, sustainable radio frequency wireless energy transfer emerges as an attractive solution for efficiently charging the next generation of ultra low power IoT devices. Herein, we highlight that sustainable charging is broader than conventional green charging, as it focuses on balancing economy prosperity and social equity in addition to environmental health. Moreover, we overview the key enablers for realizing this vision and associated challenges. We discuss the economic implications of powering energy transmitters with ambient energy sources, and reveal insights on their optimal deployment. We highlight relevant research challenges and candidate solutions.Comment: 12 pages, 6 figures, 2 tables, submitted to IEEE Internet of Things Journa

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT

Quality-Aware Scheduling Algorithms in Renewable Sensor

Wireless sensor network has emerged as a key technology for various applications such as environmental sensing, structural health monitoring, and area surveillance. Energy is by far one of the most critical design hurdles that hinders the deployment of wireless sensor networks. The lifetime of traditional battery-powered sensor networks is limited by the capacities of batteries. Even many energy conservation schemes were proposed to address this constraint, the network lifetime is still inherently restrained, as the consumed energy cannot be replenished easily. Fully addressing this issue requires energy to be replenished quite often in sensor networks (renewable sensor networks). One viable solution to energy shortages is enabling each sensor to harvest renewable energy from its surroundings such as solar energy, wind energy, and so on. In comparison with their conventional counterparts, the network lifetime in renewable sensor networks is no longer a main issue, since sensors can be recharged repeatedly. This results in a research focus shift from the network lifetime maximization in traditional sensor networks to the network performance optimization (e.g., monitoring quality). This thesis focuses on these issues and tackles important problems in renewable sensor networks as follows. We first study the target coverage optimization in renewable sensor networks via sensor duty cycle scheduling, where a renewable sensor network consisting of a set of heterogeneous sensors and a stationary base station need to be scheduled to monitor a set of targets in a monitoring area (e.g., some critical facilities) for a specified period, by transmitting their sensing data to the base station through multihop relays in a real-time manner. We formulate a coverage maximization problem in a renewable sensor network which is to schedule sensor activities such that the monitoring quality is maximized, subject to that the communication network induced by the activated sensors and the base station at each time moment is connected. We approach the problem for a given monitoring period by adopting a general strategy. That is, we divide the entire monitoring period into equal numbers of time slots and perform sensor activation or inactivation scheduling in the beginning of each time slot. As the problem is NP-hard, we devise efficient offline centralized and distributed algorithms for it, provided that the amount of harvested energy of each sensor for a given monitoring period can be predicted accurately. Otherwise, we propose an online adaptive framework to handle energy prediction fluctuation for this monitoring period. We conduct extensive experiments, and the experimental results show that the proposed solutions are very promising. We then investigate the data collection optimization in renewable sensor networks by exploiting sink mobility, where a mobile sink travels around the sensing field to collect data from sensors through one-hop transmission. With one-hop transmission, each sensor could send data directly to the mobile sink without any relay, and thus no energy are consumed on forwarding packets for others which is more energy efficient in comparison with multi-hop relays. Moreover, one-hop transmission particularly is very useful for a disconnected network, which may be due to the error-prone nature of wireless communication or the physical limit (e.g., some sensors are physically isolated), while multi-hop transmission is not applicable. In particular, we investigate two different kinds of mobile sinks, and formulate optimization problems under different scenarios, for which both centralized and distributed solutions are proposed accordingly. We study the performance of the proposed solutions and validate their effectiveness in improving the data quality. Since the energy harvested often varies over time, we also consider the scenario of renewable sensor networks by utilizing wireless energy transfer technology, where a mobile charging vehicle periodically travels inside the sensing field and charges sensors without any plugs or wires. Specifically, we propose a novel charging paradigm and formulate an optimization problem with an objective of maximizing the number of sensors charged per tour. We devise an offline approximation algorithm which runs in quasi-polynomial time and develop efficient online sensor charging algorithms, by considering the dynamic behaviors of sensors’ various sensing and transmission activities. To study the efficiency of the proposed algorithms, we conduct extensive experiments and the experimental results demonstrate that the proposed algorithms are very efficient. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis

Resource Management in Green Wireless Communication Networks

The development of wireless technologies has been stimulated by the ever increasing network capacity and the diversity of users' quality of service (QoS) requirements. It is widely anticipated that next-generation wireless networks should be capable of integrating wireless networks with various network architectures and wireless access technologies to provide diverse high-quality ubiquitous wireless accesses for users. However, the existing wireless network architecture may not be able to satisfy explosive wireless access request. Moreover, with the increasing awareness of environmental protection, significant growth of energy consumption caused by the massive traffic demand consequently raises the carbon emission footprint. The emerging of green energy technologies, e.g., solar panel and wind turbine, has provided a promising methodology to sustain operations and management of next-generation wireless networks by powering wireless network devices with eco-friendly green energy. In this thesis, we propose a sustainable wireless network solution as the prototype of next-generation wireless networks to fulfill various QoS requirements of users with harvested energy from natural environments. The sustainable wireless solution aims at establishing multi-tier heterogeneous green wireless communication networks to integrate different wireless services and utilizing green energy supplies to sustain the network operations and management. The solution consists of three steps, 1) establishing conventional green wireless networks, 2) building multi-tier green wireless networks, and 3) allocating and balancing network resources. In the first step, we focus on cost-effectively establishing single-tier green wireless networks to satisfy users' basic QoS requirements by designing efficient network planning algorithm. We formulate the minimum green macro cell BS deployment problem as an optimization problem, which aims at placing the minimum number of BSs to fulfill the basic QoS requirements by harvested energy. A preference level is defined as the guidance for efficient algorithm design to solve the minimum green macro cell BSs deployment problem. After that, we propose a heuristic algorithm, called two-phase constrained green BS placement (TCGBP) algorithm, based on Voronoi diagram. The TCGBP algorithm jointly considers the rate adaptation and power allocation to solve the formulated optimization problem. The performance is verified by extensive simulations, which demonstrate that the TCGBP algorithm can achieve the optimal solution with significantly reduced time complexity. In the second step, we aim at efficiently constructing multi-tier green heterogeneous networks to fulfill high-end QoS requirements of users by placing green small cell BSs. We formulate the green small cell BS deployment and sub-carrier allocation problem as a mixed-integer non-linear programming (MINLP) problem, which targets at deploying the minimum number of green small cell BSs as relay nodes to further improve network capacities and provide high-quality QoS wireless services with harvested energy under the cost constraint. We propose the sub-carrier and traffic over rate (STR) metric to evaluate the contribution of deployed green small cell BSs in both energy and throughput aspects. Based on the metric, two algorithms are designed, namely joint relay node placement and sub-carrier allocation with top-down/bottom-up (RNP-SA-t/b) algorithms. Extensive simulations demonstrate that the proposed algorithms provide simple yet efficient solutions and offer important guidelines on network planning and resource management in two-tier heterogeneous green wireless networks. In the last step, we intend to allocate limited network resources to guarantee the balance of charging and discharging processes. Different from network planning based on statistical historical data, the design of resource allocation algorithm generally concerns relatively short-term resources management, and thus it is essential to accurately estimate the instantaneous energy charging and discharging rates of green wireless network devices. Specifically, we investigate the energy trading issues in green wireless networks, and try to maximize the profits of all cells by determining the optimal price and quantity in each energy trading transaction. Finally, we apply a two-stage leader-follower Stackelberg game to formulate the energy trading problem. By using back induction to obtain the optimal price and quantity of traded energy, we propose an optimal algorithm, called optimal profits energy trading (OPET) algorithm. Our analysis and simulation results demonstrate the optimality performance of OPET algorithm. We believe that our research results in this dissertation can provide insightful guidance in the design of next-generation wireless communication networks with green energy. The algorithms developed in the dissertation offer practical and efficient solutions to build and optimize multi-tier heterogeneous green wireless communication networks

Perpetual Sensing: Experiences with Energy-Harvesting Sensor Systems

Industry forecasts project the number of connected devices will outpace the global population by orders of magnitude in the next decade or two. These projections are application driven: smart cities, implantable health monitors, responsive buildings, autonomous robots, driverless cars, and instrumented infrastructure are all expected to be drivers for the growth of networked devices. Achieving this immense scale---potentially trillions of smart and connected sensors and computers, popularly called the "Internet of Things"---raises a host of challenges including operating system design, networking protocols, and orchestration methodologies. However, another critical issue may be the most fundamental: If embedded computers outnumber people by a factor of a thousand, how are we going to keep all of these devices powered? In this dissertation, we show that energy-harvesting operation, by which devices scavenge energy from their surroundings to power themselves after they are deployed, is a viable answer to this question. In particular, we examine a range of energy-harvesting sensor node designs for a specific application: smart buildings. In this application setting, the devices must be small and sleek to be unobtrusively and widely deployed, yet shrinking the devices also reduces their energy budgets as energy storage often dominates their volume. Additionally, energy-harvesting introduces new challenges for these devices due to the intermittent access to power that stems from relying on unpredictable ambient energy sources. To address these challenges, we present several techniques for realizing effective sensors despite the size and energy constraints. First is Monjolo, an energy metering system that exploits rather than attempts to mask the variability in energy-harvesting by using the energy harvester itself as the sensor. Building on Monjolo, we show how simple time synchronization and an application specific sensor can enable accurate, building-scale submetering while remaining energy-harvesting. We also show how energy-harvesting can be the foundation for highly deployable power metering, as well as indoor monitoring and event detection. With these sensors as a guide, we present an architecture for energy-harvesting systems that provides layered abstractions and enables modular component reuse. We also couple these sensors with a generic and reusable gateway platform and an application-layer cloud service to form an easy-to-deploy building sensing toolkit, and demonstrate its effectiveness by performing and analyzing several modest-scale deployments.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138686/1/bradjc_1.pd

Emerging research directions in computer science : contributions from the young informatics faculty in Karlsruhe

In order to build better human-friendly human-computer interfaces, such interfaces need to be enabled with capabilities to perceive the user, his location, identity, activities and in particular his interaction with others and the machine. Only with these perception capabilities can smart systems ( for example human-friendly robots or smart environments) become posssible. In my research I\u27m thus focusing on the development of novel techniques for the visual perception of humans and their activities, in order to facilitate perceptive multimodal interfaces, humanoid robots and smart environments. My work includes research on person tracking, person identication, recognition of pointing gestures, estimation of head orientation and focus of attention, as well as audio-visual scene and activity analysis. Application areas are humanfriendly humanoid robots, smart environments, content-based image and video analysis, as well as safety- and security-related applications. This article gives a brief overview of my ongoing research activities in these areas

Sustainable Mobility and Transport

This Special Issue is dedicated to sustainable mobility and transport, with a special focus on technological advancements. Global transport systems are significant sources of air, land, and water emissions. A key motivator for this Special Issue was the diversity and complexity of mitigating transport emissions and industry adaptions towards increasingly stricter regulation. Originally, the Special Issue called for papers devoted to all forms of mobility and transports. The papers published in this Special Issue cover a wide range of topics, aiming to increase understanding of the impacts and effects of mobility and transport in working towards sustainability, where most studies place technological innovations at the heart of the matter. The goal of the Special Issue is to present research that focuses, on the one hand, on the challenges and obstacles on a system-level decision making of clean mobility, and on the other, on indirect effects caused by these changes