Michele Magno

A survey of multi-source energy harvesting systems

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, ‘hybrid’ energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straight-forward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.

Extended Wireless Monitoring Through Intelligent Hybrid Energy Supply

This paper presents the design, implementation, and characterization of a hardware platform applicable to wireless structural health monitoring (WSHM). The primary design goal is to devise a system capable of persistent operation for the duration of the life cycle of a target structure. It should be deployable during the construction phase and reconfigurable thereafter, suitable for continuous long-term monitoring. In addition to selecting the most energy efficient useful components to ensure the lowest possible power consumption, it is necessary to consider sources of energy other than, or complementary to, batteries. Thus, the platform incorporates multisource energy harvesting, electrochemical fuel cell (FC), energy storage, recharging capability, and intelligent operation through real-time energy information exchange with the primary controller. It is shown that, with appropriate integration, the device will have sufficient energy to operate perpetually in a distributed WSHM application. This conclusion is demonstrated through experimental results, simulations, and empirical measurements that demonstrate the high-efficiency energy conversion of the harvesters (up to 86%) and low-power characteristics of the platform (less than 1 mW in sleep mode). It is shown that energy autonomy is comfortably achievable for duty cycles up to 0.75%, meeting the demands of the application, and up to 1.5%, invoking the FC.

A Low Cost, Highly Scalable Wireless Sensor Network Solution to Achieve Smart LED Light Control for Green Buildings

Reducing energy demand in the residential and industrial sectors is an important challenge worldwide. In particular, lights account for a great portion of total energy consumption, and unfortunately a huge amount of this energy is wasted. Light-emitting diode (LED) lights are being used to light offices, houses, industrial, or agricultural facilities more efficiently than traditional lights. Moreover, the light control systems are introduced to current markets, because the installed lighting systems are outdated and energy inefficient. However, due to high costs, installation issues, and difficulty of maintenance; existing light control systems are not successfully applied to home, office, and industrial buildings. This paper proposes a low cost, wireless, easy to install, adaptable, and smart LED lighting system to automatically adjust the light intensity to save energy and maintaining user satisfaction. The system combines motion sensors and light sensors in a low-power wireless solution using Zigbee communication. This paper presents the design and implementation of the proposed system in a real-world deployment. Characterization of a commercial LED panel was performed to evaluate the benefit of dimming for this light technology. Measurements of total power consumption over a continuous six months period (winter to summer) of a busy office were acquired to verify the performance and the power savings across several weather conditions scenarios. The proposed smart lighting system reduces total power consumption in the application scenario by 55% during a six month period and up to 69% in spring months. These figures take also into account individual user preferences.

Beyond duty cycling: Wake-up radio with selective awakenings for long-lived wireless sensing systems

Emerging wake-up radio technologies have the potential to bring the performance of sensing systems and of the Internet of Things to the levels of low latency and very low energy consumption required to enable critical new applications. This paper provides a step towards this goal with a twofold contribution. We first describe the design and prototyping of a wake-up receiver (WRx) and its integration to a wireless sensor node. Our WRx features very low power consumption (<; 1.3μW), high sensitivity (up to -55dBm), fast reactivity (wake-up time of 130μs), and selective addressing, a key enabler of new high performance protocols. We then present ALBA-WUR, a cross-layer solution for data gathering in sensing systems that redesigns a previous leading protocol, ALBA-R, extending it to exploit the features of our WRx. We evaluate the performance of ALBA-WUR via simulations, showing that the use of the WRx produces remarkable energy savings (up to five orders of magnitude), and achieves lifetimes that are decades longer than those obtained by ALBA-R in sensing systems with duty cycling, while keeping latencies at bay.

A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.

Benefits of Wake-Up Radio in Energy-Efficient Multimodal Surveillance Wireless Sensor Network

Scarce energy budget of battery-powered wireless sensor nodes calls for cautious power management not to compromise performance of the system. To reduce both energy consumption and delay in energy-hungry wireless sensor networks for latency-restricted surveillance scenarios, this paper proposes a multimodal two-tier architecture with wake-up radio receivers. In video surveillance applications, using information from distributed low-power pyroelectric infrared (PIR) sensors, which detect human presence limits the activity of cameras and reduces their energy consumption. PIR sensors transmit the information about the event to camera nodes using wake-up radio receivers. We show the benefits of wake-up receivers over duty cycling in terms of overcoming energy consumption versus latency tradeoff (proved with two orders of magnitude lower latency-only 9 ms). At the same time, the power consumption of the camera node, including a wake-up receiver is comparable with the one having only duty-cycled main transceiver with 1% duty cycle (about 32 mW for 25 activations per hour).

Ensuring Survivability of Resource-Intensive Sensor Networks Through Ultra-Low Power Overlays

Nodes in wireless sensor networks (WSNs) typically have limited power supply and networks are often expected to be functional for extended periods. Therefore, the minimization of energy consumption and the maximization of network lifetime are key objectives in WSN. This paper proposes an overlay, energy optimized, sensor network to extend the functional lifetime of an energy-intensive sensor network application. The overlay network consists of additional nodes that exploit recent advances in energy harvesting and wake-up radio technologies, coupled with an application specific, complementary, ultra-low power sensor. The experimental results and simulations demonstrate that this approach can ensure survivability of energy-inefficient sensor networks. Simulating applications using energy-intensive video cameras and air quality sensors, combined with the proposed overlayed ultra-low power sensor network, demonstrates that this approach can increase functional lifetime toward perpetual operation and is suitable for WSN applications in which complementarity exists between the required energy-intensive sensors and low-cost sensors that can be used as triggers.

Smart power unit with ultra low power radio trigger capabilities for wireless sensor networks

This paper presents the design, implementation and characterization of an energy-efficient smart power unit for a wireless sensor network with a versatile nano-Watt wake up radio receiver. A novel Smart Power Unit has been developed featuring multi-source energy harvesting, multi-storage adaptive recharging, electrochemical fuel cell integration, radio wake-up capability and embedded intelligence. An ultra low power on board microcontroller performs maximum power point tracking (MPPT) and optimized charging of supercapacitor or Li-Ion battery at the maximum efficiency. The power unit can communicate with the supplied node via serial interface (I2C or SPI) to provide status of resources or dynamically adapt its operational parameters. The architecture is very flexible: it can host different types of harvesters (solar, wind, vibration, etc.). Also, it can be configured and controlled by using the wake-up radio to enable the design of very efficient power management techniques on the power unit or on the supplied node. Experimental results on the developed prototype demonstrate ultra-low power consumption of the power unit using the wake-up radio. In addition, the power transfer efficiency of the multi-harvester and fuel cell matches the state-of-the-art for Wireless Sensor Networks.

A Multi-Harvester architecture with hybrid storage devices and smart capabilities for low power systems

The increasing attention on energy autonomous sensing and computing systems which can operate unattended tens of years, have made energy harvesting and power conversion techniques key technologies for the future. The goal is to power systems nearly perpetually if the scavenger is exposed to reasonable environmental energy conditions. However, the system is still threatened to run out of energy, if a prolonged lack of energy intake happens. The last frontiers of perpetual operating systems is combining cutting edge technologies for energy generation from the environment and long-term and green energy supply using small factor fuel cells with few cm3. In this paper we introduce an hybrid power architecture which improves embedded systems power availability. We present a Smart Power Unit (SPU) that is a power supply architecture which manages both energy harvesting and novel fuel cells technologies. SPU provide an efficient air-flow and solar energy harvesting stage and a hydrogen micro fuel cell interface. Each harvester stores energy in a local supercapacitor and, when full, a lithium-ion battery is charged. Micro fuel cell acts as reservoir source for recharging battery in low environmental power condition. The core of the SPU is the microcontroller based power manager that exploits MPPT, energy prevision, battery monitoring and communications with user node.

Accelerating real-time embedded scene labeling with convolutional networks

Today there is a clear trend towards deploying advanced computer vision (CV) systems in a growing number of application scenarios with strong real-time and power constraints. Brain-inspired algorithms capable of achieving record-breaking results combined with embedded vision systems are the best candidate for the future of CV and video systems due to their flexibility and high accuracy in the area of image understanding. In this paper, we present an optimized convolutional network implementation suitable for real-time scene labeling on embedded platforms. We show that our algorithm can achieve up to 96GOp/s, running on the Nvidia Tegra K1 embedded SoC. We present experimental results, compare them to the state-of-the-art, and demonstrate that for scene labeling our approach achieves a 1.5x improvement in throughput when compared to a modern desktop CPU at a power budget of only 11 W.