ILSN : An Inexpensive, Long-Lived, Sensor Network Solution

In the recent past, there has been a phenomenal increase in monitoring the physical world using wireless sensor networks comprising tiny computing devices with integrated sensors. These devices are deployed in large numbers, often across large spaces and within hostile environments. Wireless sensor networks are used to gather meaningful data and to enable important applications. They must satisfy some basic requirements. Specifially, they must be maintenance free, inexpensive, reliable, and scalable. The devices used in these networks are deployed in the hundreds to thousands and rely on battery power. It is expensive to change these batteries in such large networks, both in terms of battery cost and personnel time. The cost of an individual device plays an important role in the cost of a sensor network. Further, if these networks are deployed in safety critical contexts, we cannot risk having incorrect data or missing important data. Finally, sensor networks must be able to accommodate new devices and gracefully handle device failures. This thesis describes a hardware/software solution for wireless sensor networks which is maintenance free, inexpensive, reliable, and scalable. In this thesis, I present a wireless sensing device which harvests solar energy and stores it in a Li-Ion battery. The device works on solar energy during the daytime and relies on battery power during the night. This addresses the problem of maintaining remote devices. The design is also focused on reducing component costs. The cost is low enough to discard the individual devices without significant concern. Finally, using these devices, I present a network protocol and reference implementation which makes data reception reliable, while supporting network scalability

Powering IoT Sensors with RF Energy Harvesting

There is a need to power Internet of Things (IoT) applications that require frequent, expensive, and/or dangerous battery replacements. Radio-frequency energy harvesting (RFEH) is a possible alternative source of power for select IoT sensor applications. In comparison to other methods of energy harvesting, RFEH has the smallest incident power densities and therefore comes with many design challenges. In this project we implement a novel RFEH system powered via a dedicated transmitter. A planar inverted-F antenna (PIFA) and voltage doubler circuit form the designed rectenna (rectifier + antenna) and the system is implemented on a custom PCB to carry out RF-to-DC conversion. The system’s feasibility is demonstrated by powering a commercial power management unit (PMU) and temperature sensor over a test duration of eight hours

Energy-aware Approaches for Energy Harvesting Powered Wireless Sensor Systems

Energy harvesting (EH) powered wireless sensor systems (WSSs) are gaining increasing popularity since they enable the system to be self-powering, long-lasting, almost maintenance-free, and environmentally friendly. However, the mismatch between energy generated by harvesters and energy demanded by WSS to perform the required tasks is always a bottleneck as the ambient environmental energy is limited, and the WSS is power hunger. Therefore, the thesis has proposed, designed, implemented, and tested the energy-aware approaches for wireless sensor motes (WSMs) and wireless sensor networks (WSNs), including hardware energy-aware interface (EAI), software EAI, sensing EAI and network energy-aware approaches to address this mismatch. The main contributions of this thesis to the research community are designing the energy-aware approaches for EH Powered WSMs and WSNs which enables a >30 times reduction in sleep power consumption of WSNs for successful EH powering WSNs without a start-up issue in the condition of mismatch between the energy generated by harvesters and energy demanded by WSSs in both mote and network systems. For EH powered WSM systems, the energy-aware approaches have (1) enabled the harvested energy to be accumulated in energy storage devices to deal with the mismatch for the operation of the WSMs without the start-up issue, (2) enabled a commercial available WSMs with a reduced sleep current from 28.3 μA to 0.95 μA for the developed WSM, (3) thus enabled the WSM operations for a long active time of about 1.15 s in every 7.79 s to sample and transmit a large number of data (e.g., 388 bytes), rather than a few ten milliseconds and a few bytes. For EH powered WSN systems, on top of energy-aware approached for EH powered WSM, the network energy-aware approaches have presented additional capabilities for network joining process for energy-saving and enabled EH powered WSNs. Once the EH powered WSM with the network energy-aware approach is powered up and began the network joining process, energy, as an example of 48.23 mJ for a tested case, has been saved in the case of the attempt to join the network unsuccessfully. Once the EH-WSM has joined the network successfully, the smart programme applications that incorporate the software EAI, sensing EAI and hardware EAI allow the EH powered WSM to achieve (4) asynchronous operation or (5) synchronised operation based on the energy available after the WSM has joined the network.Through designs, implementations, and analyses, it has been shown that the developed energy-aware approaches have provided an enabled capability for EH successfully powering WSS technologies in the condition of energy mismatch, and it has the potential to be used for wide industrial applications

Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications

The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any "thing" we may be interested in. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring application

Model-based design for self-sustainable sensor nodes

Long-term and maintenance-free operation is a critical feature for large-scale deployed battery-operated sensor nodes. Energy harvesting (EH) is the most promising technology to overcome the energy bottleneck of today’s sensors and to enable the vision of perpetual operation. However, relying on fluctuating environmental energy requires an application-specific analysis of the energy statistics combined with an in-depth characterization of circuits and algorithms, making design and verification complex. This article presents a model-based design (MBD) approach for EH-enabled devices accounting for the dynamic behavior of components in the power generation, conversion, storage, and discharge paths. The extension of existing compact models combined with data-driven statistical modeling of harvesting circuits allows accurate offline analysis, verification, and validation. The presented approach facilitates application-specific optimization during the development phase and reliable long-term evaluation combined with environmental datasets. Experimental results demonstrate the accuracy and flexibility of this approach: the model verification of a solar-powered wireless sensor node shows a determination coefficient () of 0.992, resulting in an energy error of only -1.57 % between measurement and simulation. Compared to state-of-practice methods, the MBD approach attains a reduction of the estimated state-of-charge error of up to 10.2 % in a real-world scenario. MBD offers non-trivial insights on critical design choices: the analysis of the storage element selection reveals a 2–3 times too high self-discharge per capacity ratio for supercapacitors and a peak current constrain for lithium-ion polymer batteries

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

RF Energy Harvesting Wireless Communication: RF Environment, Device Hardware and Practical Issues

Radio frequency (RF) based wireless power transfer provides an attractive solution to extend the lifetime of power-constrained wireless sensor networks. Through harvesting RF energy from surrounding environments or dedicated energy sources, low-power wireless devices can be self-sustaining and environment-friendly. These features make the RF energy harvesting wireless communication (RF-EHWC) technique attractive to a wide range of applications. The objective of this article is to investigate the latest research activities on the practical RF-EHWC design. The distribution of RF energy in the real environment, the hardware design of RF-EHWC devices and the practical issues in the implementation of RF-EHWC networks are discussed. At the end of this article, we introduce several interesting applications that exploit the RF-EHWC technology to provide smart healthcare services for animals, wirelessly charge the wearable devices, and implement 5G-assisted RF-EHWC

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

Survey of Energy Harvesting Technologies for Wireless Sensor Networks

Energy harvesting (EH) technologies could lead to self-sustaining wireless sensor networks (WSNs) which are set to be a key technology in Industry 4.0. There are numerous methods for small-scale EH but these methods differ greatly in their environmental applicability, energy conversion characteristics, and physical form which makes choosing a suitable EH method for a particular WSN application challenging due to the specific application-dependency. Furthermore, the choice of EH technology is intrinsically linked to non-trivial decisions on energy storage technologies and combinatorial architectures for a given WSN application. In this paper we survey the current state of EH technology for small-scale WSNs in terms of EH methods, energy storage technologies, and EH system architectures for combining methods and storage including multi-source and multi-storage architectures, as well as highlighting a number of other optimisation considerations. This work is intended to provide an introduction to EH technologies in terms of their general working principle, application potential, and other implementation considerations with the aim of accelerating the development of sustainable WSN applications in industry