Wearable flexible lightweight modular RFID tag with integrated energy harvester

A novel wearable radio frequency identification (RFID) tag with sensing, processing, and decision-taking capability is presented for operation in the 2.45-GHz RFID superhigh frequency (SHF) band. The tag is powered by an integrated light harvester, with a flexible battery serving as an energy buffer. The proposed active tag features excellent wearability, very high read range, enhanced functionality, flexible interfacing with diverse low-power sensors, and extended system autonomy through an innovative holistic microwave system design paradigm that takes antenna design into consideration from the very early stages. Specifically, a dedicated textile shorted circular patch antenna with monopolar radiation pattern is designed and optimized for highly efficient and stable operation within the frequency band of operation. In this process, the textile antenna's functionality is augmented by reusing its surface as an integration platform for light-energy-harvesting, sensing, processing, and transceiver hardware, without sacrificing antenna performance or the wearer's comfort. The RFID tag is validated by measuring its stand-alone and on-body characteristics in free-space conditions. Moreover, measurements in a real-world scenario demonstrate an indoor read range up to 23 m in nonline-of-sight indoor propagation conditions, enabling interrogation by a reader situated in another room. In addition, the RFID platform only consumes 168.3 mu W, when sensing and processing are performed every 60 s

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things

Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.Comment: 15 pages, 11 figure

Energy harvesting towards self-powered iot devices

The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system's life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article

Energy Harvesting Techniques for Internet of Things (IoT)

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

RF Energy Harvesting Techniques for Battery-less Wireless Sensing, Industry 4.0 and Internet of Things: A Review

As the Internet of Things (IoT) continues to expand, the demand for the use of energy-efficient circuits and battery-less devices has grown rapidly. Battery-less operation, zero maintenance and sustainability are the desired features of IoT devices in fifth generation (5G) networks and green Industry 4.0 wireless systems. The integration of energy harvesting systems, IoT devices and 5G networks has the potential impact to digitalize and revolutionize various industries such as Industry 4.0, agriculture, food, and healthcare, by enabling real-time data collection and analysis, mitigating maintenance costs, and improving efficiency. Energy harvesting plays a crucial role in envisioning a low-carbon Net Zero future and holds significant political importance. This survey aims at providing a comprehensive review on various energy harvesting techniques including radio frequency (RF), multi-source hybrid and energy harvesting using additive manufacturing technologies. However, special emphasis is given to RF-based energy harvesting methodologies tailored for battery-free wireless sensing, and powering autonomous low-power electronic circuits and IoT devices. The key design challenges and applications of energy harvesting techniques, as well as the future perspective of System on Chip (SoC) implementation, data digitization in Industry 4.0, next-generation IoT devices, and 5G communications are discussed