The Internet of Things (IoT) is a revolutionizing technology which aims to create an ecosystem of connected objects and embedded devices and provide ubiquitous connectivity between trillions of not only smart devices but also simple sensors and actuators. Although recent advancements in miniaturization of devices with higher computational capabilities and ultra-low power communication technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytics, data storage, and power sources. A large portion of the IoT devices cannot be powered by batteries only anymore, as they will be installed in hard to reach areas and regular battery replacement and maintenance are infeasible. A viable solution is to scavenge and harvest energy from the environment and then provide enough energy to the devices to perform their operations. This will significantly increase the device life time and eliminate the need for the battery as an energy source. This survey aims at providing a comprehensive study on energy harvesting techniques as alternative and promising solutions to power the IoT devices. We present the main design challenges of the IoT devices in terms of energy and power and provide design considerations for a successful implementation of self-powered the IoT devices. We then specifically focus on piezoelectric energy harvesting as one of the most promising solutions to power the IoT devices and present the main challenges and research directions. We also shed lights on the hybrid energy harvesting for the IoT and security challenges of energy harvesting enabled the IoT systems.

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Towards a Green and Self-Powered Internet of Things Using Piezoelectric Energy Harvesting

Miniaturization of devices with higher computational capacity coupled with advancement in communication technologies is driving the growth of deployment of sensors and actuators in our surroundings. To keep up the pace with this growth, these tiny, battery-powered devices need small-sized and high-energy density batteries for longer operational time, which calls for improvement in battery technologies. An alternative is to harvest energy from the environment. An important aspect of energy harvesting is that the devices go through birth and death cycles with respect to their power unlike battery powered ones. Another important aspect is that contextual information is also generated while devices harvest energy from their ambiance. In this article we provide a comprehensive study of various types of energy harvesting techniques. We then provide some models used in energy harvesting systems and the design of such systems. We also throw light on the power management and networking aspects of the energy harvesting devices. At the end we discuss the major issues and avenues for further research.

Reincarnation in the Ambiance: Devices and Networks with Energy Harvesting

One of the challenges of Industry 4.0 is the creation of vertical networks that connect smart production systems with design teams, suppliers, and the front office. To achieve such a vision, information has to be collected from machines and products throughout a smart factory. Smart factories are defined as flexible and fully connected factories that are able to make use of constant streams of data from operations and production systems. In such scenarios, the arguably most popular way for identifying and tracking objects is by adding labels or tags, which have evolved remarkably over the last years: from pure hand-written labels to barcodes, QR codes, and RFID tags. The latest trend in this evolution is smart labels which are not only mere identifiers with some kind of internal storage, but also sophisticated context-aware tags with embedded modules that make use of wireless communications, energy efficient displays, and sensors. Therefore, smart labels go beyond identification and are able to detect and react to the surrounding environment. Moreover, when the industrial Internet of Things paradigm is applied to smart labels attached to objects, they can be identified remotely and discovered by other Industry 4.0 systems, what allows such systems to react in the presence of smart labels, thus triggering specific events or performing a number of actions on them. The amount of possible interactions is endless and creates unprecedented industrial scenarios, where items can talk to each other and with tools, machines, remote computers, or workers. This paper, after reviewing the basics of Industry 4.0 and smart labels, details the latest technologies used by them, their applications, the most relevant academic and commercial implementations, and their internal architecture and design requirements, providing researchers with the necessary foundations for developing the next generation of Industry 4.0 human-centered smart label applications.

A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.0

The emergence of energy-scavenging techniques for powering networks of embedded devices is raising the need for dedicated simulation frameworks that can support researchers and developers in the design and performance evaluation of harvesting-aware protocols and algorithms. In this work we present GreenCastalia, an open-source energy-harvesting simulation framework we have developed for the popular Castalia simulator. GreenCastalia supports multi-source and multi-storage energy harvesting architectures, it is highly modular and easily customizable. In addition, it allows to simulate networks of embedded devices with heterogeneous harvesting capabilities.

/pdf/greencastalia-an-energy-harvesting-enabled-framework-for-the-4grnx8cnbe.pdf

GreenCastalia: an energy-harvesting-enabled framework for the Castalia simulator

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Most wireless sensor networks (WSNs) consist of battery-powered nodes and are limited to hundreds of nodes. Battery replacement is a very costly operation and a key factor in limiting successful large-scale deployments. The recent advances in both energy harvesters and low-power communication systems hold promise for deploying large-scale wireless green-powered sensor networks (WGSNs). This will enable new applications and will eliminate environmentally unfriendly battery disposal. This paper explores the use of energy harvesters to scavenge power for nodes in a WSN. The design and implementation of a generic energy-harvesting framework, suited for a WSN simulator as well as a real-life testbed, are proposed. These frameworks are used to evaluate whether a carrier sense multiple access with collision avoidance scheme is sufficiently reliable for use in emerging large-scale energy harvesting electronic shelf label (EHESL) systems (i.e., 12000 labels in a star topology). Both the simulator and testbed experiments yielded an average success rate up to 92%, with an arrival rate of 40 transceive cycles per second. We have demonstrated that our generic energy-harvesting framework is useful for WGSN research because the simulator allowed us to verify the achieved results on the real-life testbed and vice versa.

https://link.springer.com/content/pdf/10.1155%2F2010%2F343690.pdf

Design and implementation of a generic energy-harvesting framework applied to the evaluation of a large-scale electronic shelf-labeling wireless sensor network

Ad Kamerman

Papers

Design and implementation of a generic energy-harvesting framework applied to the evaluation of a large-scale electronic shelf-labeling wireless sensor network