Energy Monitoring of Energy Harvesting Wireless Sensor Networks

Understanding the power characteristics of Wireless Sensor Networks is important, as it will help better understand the system and therefore suggesting ways of improving it. A way to monitor the energy consumption of WSNs is needed. The energy consumed by these WSNs tends to be very small and difficult to measure and that is because the consumed current can be relatively small, in microamperes’ range. Besides the relatively small energy, data loggers that used to monitor the energy are costly and it will be very costly to monitor the energy of multiple nodes. In this paper, an energy monitor that is able to measure small energy has been developed to monitor the energy of energy harvesting WSN. The energy harvesting WSN consists of three main components: the mote, the battery and the energy-harvesting module. The circuitry of each one of these three main components has been built to measure their current and voltage. A low cost data logger to sample the voltage and current data has been built that will be able to sample and store the energy profile of all of the three main components. Data have been visualized through a MATLAB graphical user interface

Energy Monitoring of Energy Harvesting Wireless Sensor Networks

Understanding the power characteristics of Wireless Sensor Networks is important, as it will help better understand the system and therefore suggesting ways of improving it. A way to monitor the energy consumption of WSNs is needed. The energy consumed by these WSNs tends to be very small and difficult to measure and that is because the consumed current can be relatively small, in microamperes’ range. Besides the relatively small energy, data loggers that used to monitor the energy are costly and it will be very costly to monitor the energy of multiple nodes. In this paper, an energy monitor that is able to measure small energy has been developed to monitor the energy of energy harvesting WSN. The energy harvesting WSN consists of three main components: the mote, the battery and the energy-harvesting module. The circuitry of each one of these three main components has been built to measure their current and voltage. A low cost data logger to sample the voltage and current data has been built that will be able to sample and store the energy profile of all of the three main components. Data have been visualized through a MATLAB graphical user interface

Powertrace: Network-level Power Profiling for Low-power Wireless Networks

Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace, which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox

A Flexible In Situ Power Monitoring Unit for Environmental Sensor Networks

Wireless radios are a great consumer of energy in sensor networks. Retrieving data from a remote deployment in an energy-efficient fashion is a difficult problem, and while solutions have been proposed in literature, real-world systems typically implement robust though inefficient methods. In an effort to bring efficient monitoring techniques to real-world environmental sensor networks, we seek to now quantify the performance brought by these algorithms in practical terms, i.e., by their resulting reduction in overall station energy consumption. To this end, we have developed a power monitoring extension board that integrates seamlessly with a commercial environmental sensor network platform. The board is capable of measuring all incoming and outgoing power for a station, and can disconnect subsystems, such as the solar panel or sensor bus, as necessary. The board is currently deployed on an outdoor network and is undergoing extensive testing

Variación del consumo energético en APIs WEB para IoT

In recent years, there has been a growing interest in implementing and developing IoT applications. However, this has led to several challenges that need to be addressed. For instance, during the design stages, critical provisions such as power consumption have been identified as important requirements. Additionally, the current global climate change context has heightened awareness of the carbon footprint of new technologies. To address these challenges, APIs WEB services have emerged as a potential solution for supporting IoT device connectivity. This paper aims to explore how energy consumption at the battery level will impact IoT applications supported by APIs WEB services, and the factors that contribute to its reduction.En los últimos tiempos, ha habido un gran interés por las aplicaciones de IoT. A su vez, su desarrollo ha sido delineado por requerimientos específicos, como la eficiencia energética. Dado el contexto mundial de cambio climático, es importante que las nuevas tecnologías ayuden a reducir la huella de carbono. En este sentido, las interfaces de programación de aplicaciones WEB (APIs WEB) se presentan como una solución para la conectividad de los dispositivos IoT. Este estudio analiza como varía el consumo de energía de las baterías en las aplicaciones de IoT que utilizan servicios de APIs WEB, así como los factores que influyen en la reducción del consumo de energía

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation. © 2015, Maaß et al.; licensee Springer

The Energy Endoscope: Real-Time Detailed Energy Accounting for Wireless Sensor Nodes

This paper describes a new embedded networked sen-sor platform architecture that combines hardware and soft-ware tools providing detailed, fine-grained real-time en-ergy usage information. We introduce the LEAP2 plat-form, a qualitative step forward over the previously devel-oped LEAP [13] and other similar platforms. LEAP2 is based on a new low power ASIC system and generally appli-cable supporting architecture that provides unprecedented capabilities for directly observing energy usage of multi-ple subsystems in real-time. Real-time observation with microsecond-scale time resolution enables direct account-ing of energy dissipation for each computing task as well as for each hardware subsystem. The new hardware archi-tecture is exploited with our new software tools, etop and endoscope. A series of experimental investigations provide high-resolution power information in networking, storage, memory and processing for primary embedded networked sensing applications. Using results obtained in real-time we show that for a large class of wireless sensor network nodes, there exist several interdependencies in energy con-sumption between different subsystems. Through the use of our measurement tools we demonstrate that by carefully se-lecting the system operating points, energy savings of over 60 % can be achieved while retaining system performance.