Towards Optimal Kinetic Energy Harvesting for the Batteryless IoT

Traditional Internet of Things (IoT) sensors rely on batteries that need to be replaced or recharged frequently which impedes their pervasive deployment. A promising alternative is to employ energy harvesters that convert the environmental energy into electrical energy. Kinetic Energy Harvesting (KEH) converts the ambient motion/vibration energy into electrical energy to power the IoT sensor nodes. However, most previous works employ KEH without dynamically tracking the optimal operating point of the transducer for maximum power output. In this paper, we systematically analyse the relation between the operating point of the transducer and the corresponding energy yield. To this end, we explore the voltage-current characteristics of the KEH transducer to find its Maximum Power Point (MPP). We show how this operating point can be approximated in a practical energy harvesting circuit. We design two hardware circuit prototypes to evaluate the performance of the proposed mechanism and analyse the harvested energy using a precise load shaker under a wide set of controlled conditions typically found in human-centric applications. We analyse the dynamic current-voltage characteristics and specify the relation between the MPP sampling rate and harvesting efficiency which outlines the need for dynamic MPP tracking. The results show that the proposed energy harvesting mechanism outperforms the conventional method in terms of generated power and offers at least one order of magnitude higher power than the latter

Towards Delay-sensitive Routing in Underwater Wireless Sensor Networks

AbstractIn Underwater Acoustic Sensor Networks (UASNs), fundamental difference between operational methodologies of routing schemes arises due to the requirement of time-critical applications therefore, there is a need for the design of delay-sensitive techniques. In this paper, Delay-Sensitive Depth-Based Routing (DSDBR), Delay-Sensitive Energy Efficient Depth-Based Routing (DSEEDBR) and Delay-Sensitive Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (DSAMCTD) protocols are proposed to empower the depth-based routing schemes. The proposed approaches formulate delay-efficient Priority Factors (PF) and Delay-Sensitive Holding time (DS HT) to minimize end-to-end delay with a small decrease in network throughput. These schemes also employ an optimal weight function WF for the computation of transmission loss and speed of received signal. Furthermore, solution for delay lies in efficient data forwarding, minimal relative transmissions in low-depth region and better forwarder selection. Simulations are performed to assess the proposed protocols and the results indicate that the three schemes largely minimize end-to-end delay of network

PhD forum abstract: Energy harvesting based sensing for the batteryless IoT

Sensor nodes in conventional Internet-of-Things (IoT) suffer from the limitation of finite energy storage capacity of batteries, which hinders their pervasive deployment. Energy harvesters offer a promising solution to this issue by converting the environmental energy into electrical energy to power sensor nodes. Recently, energy harvesters have been employed as sensors for activity/context detection or source of energy separately. The harvested energy signal contains embedded information about the underlying physical conditions and thus can be used to detect activities in various applications. In this study, we implement Kinetic Energy Harvesters (KEHs) as context sensors and source of energy simultaneously, to detect the underlying activity and to power sensor nodes in IoT. This results in full utilisation of energy harvesters in practical environments, leading towards autonomous and perpetual operation of sensor nodes in the batteryless IoT

Task Scheduling for Energy Harvesting-based IoT: A Survey and Critical Analysis

The Internet of Things (IoT) has important applications in our daily lives, including health and fitness tracking, environmental monitoring, and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resulting from their finite energy availability. A promising solution is to harvest energy from environmental sources, such as solar, kinetic, thermal, and radio-frequency (RF) waves, for perpetual and continuous operation of IoT sensor nodes. In addition to energy generation, recently energy harvesters have been used for context detection, eliminating the need for conventional activity sensors (e.g., accelerometers), saving space, cost, and energy consumption. Using energy harvesters for simultaneous sensing and energy harvesting enables energy positive sensing - an important and emerging class of sensors, which harvest more energy than required for context detection and the additional energy can be used to power other components of the system. Although simultaneous sensing and energy harvesting is an important step forward toward autonomous self-powered sensor nodes, the energy and information availability can be still intermittent, unpredictable, and temporally misaligned with various computational tasks on the sensor node. This article provides a comprehensive survey on task scheduling algorithms for the emerging class of energy harvesting-based sensors (i.e., energy positive sensors) to achieve the sustainable operation of IoT. We discuss inherent differences between conventional sensing and energy positive sensing and provide an extensive critical analysis for devising revised task scheduling algorithms incorporating this new class of sensors. Finally, we outline future research directions toward the implementation of autonomous and self-powered IoT.</p

SolAR: Energy Positive Human Activity Recognition using Solar Cells

The high power consumption of inertial activity sensors limits the battery lifetime of today’s wearable devices. Recent studies promise to extend the lifetime of wearable devices by translating kinetic energy from human movements into electrical energy while using the harvesting signal to replace conventional activity sensors. However, in human-centric applications, the amount of harvested kinetic energy is not enough to power a real-time activity recognition algorithm and run the wearable device perpetually. In this paper, we propose Solar based human Activity Recognition (SolAR), which uses solar cells simultaneously as an activity sensor as well as an energy source. Our key observation is that the power available from a wrist-worn solar cell changes dynamically while a person moves, encoding information about the underlying activity. We collect empirical solar energy data to explore its activity sensing potential and implement the activity recognition pipeline on an ultra low-power micro-controller unit to evaluate the end-to-end power consumption of the system. Our analysis reveals that SolAR improves activity recognition accuracy by up to 8.3% and harvests more than one order of magnitude higher power compared to its kinetic counterpart. This enables SolAR to generate more energy than required for the entire activity recognition pipeline, which we term as energy positive activity recognition, achieving uninterrupted, autonomous, self-powered and real-time operation

FusedAR: Energy-Positive Human Activity Recognition using Kinetic and Solar Signal Fusion

Today's wearable Internet of Things (IoT) devices, which have numerous practical applications, suffer from the limited lifetime of batteries due to the high power consumption of conventional inertial activity sensors. Recently, kinetic energy harvesters have been employed as a source of energy as well as context information to replace conventional activity sensors. However, the harvested power from human movements using miniaturized kinetic transducers may not be sufficient to enable a perpetual and self-powered activity recognition system. In this article, we propose a novel mechanism of fused signal-based human activity recognition (FusedAR), which employs miniaturized wearable solar and kinetic energy harvesters simultaneously as an energy source as well as an activity sensor. As human activities engender distinct movement patterns and interact and interfere with the ambient light differently, the kinetic and solar energy harvesting (SEH) signals incorporate unique information about the underlying activities while generating sufficient power. After detailed experiments, we find that the FusedAR, which employs both solar and kinetic energy signals, achieves superior activity recognition performance by up to 10%, particularly in outdoor and night-time contexts, and can recognize not only activities but also contexts, compared with the individual energy harvesting signals. Furthermore, our analysis demonstrates that FusedAR, in addition to significant energy generation, consumes up to 22% less power than the highly optimized conventional three-axis accelerometer-based mechanisms, achieving energy-positive human activity recognition (HAR) leading toward perpetual, uninterrupted, and autonomous operation of wearable IoT devices.</p

Towards energy positive sensing using kinetic energy harvesters

Conventional systems for motion context detection rely on batteries to provide the energy required for sampling a motion sensor. Batteries, however, have limited capacity and, once depleted, have to be replaced or recharged. Kinetic Energy Harvesting (KEH) allows to convert ambient motion and vibration into usable electricity and can enable batteryless, maintenance free operation of motion sensors. The signal from a KEH transducer correlates with the underlying motion and may thus directly be used for context detection, saving space, cost and energy by omitting the accelerometer. Previous work uses the open circuit or the capacitor voltage for sensing without using the harvested energy to power a load. In this paper, we propose to use other sensing points in the KEH circuit that offer information-rich sensing signals while the energy from the harvester is used to power a load. We systematically analyze multiple sensing signals available in different KEH architectures and compare their performance in a transport mode detection case study. To this end, we develop four hardware prototypes, conduct an extensive measurement campaign and use the data to train and evaluate different classifiers. We show that sensing the harvesting current signal from a transducer can be energy positive, delivering up to ten times as much power as it consumes for signal acquisition, while offering comparable detection accuracy to the accelerometer signal for most of the considered transport modes