Topic
Energy consumption
About: Energy consumption is a research topic. Over the lifetime, 101929 publications have been published within this topic receiving 1609977 citations.
Papers published on a yearly basis
Papers
More filters
Journal Article•
TL;DR: S-MAC as discussed by the authors is a medium access control protocol designed for wireless sensor networks, which uses three novel techniques to reduce energy consumption and support self-configuration, including virtual clusters to auto-sync on sleep schedules.
Abstract: This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks. Wireless sensor networks use battery-operated computing and sensing devices. A network of these devices will collaborate for a common application such as environmental monitoring. We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected. These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 802.11 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important. S-MAC uses three novel techniques to reduce energy consumption and support self-configuration. To reduce energy consumption in listening to an idle channel, nodes periodically sleep. Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules. Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes. Unlike PAMAS, it only uses in-channel signaling. Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network. We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley. The experiment results show that, on a source node, an 802.11-like MAC consumes 2–6 times more energy than S-MAC for traffic load with messages sent every 1–10s.
5,354 citations
TL;DR: In this article, the authors analyzed available information concerning energy consumption in buildings, and particularly related to HVAC systems, and compared different types of building types and end uses in different countries.
Abstract: The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts (ozone layer depletion, global warming, climate change, etc.). The global contribution from buildings towards energy consumption, both residential and commercial, has steadily increased reaching figures between 20% and 40% in developed countries, and has exceeded the other major sectors: industrial and transportation. Growth in population, increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure the upward trend in energy demand will continue in the future. For this reason, energy efficiency in buildings is today a prime objective for energy policy at regional, national and international levels. Among building services, the growth in HVAC systems energy use is particularly significant (50% of building consumption and 20% of total consumption in the USA). This paper analyses available information concerning energy consumption in buildings, and particularly related to HVAC systems. Many questions arise: Is the necessary information available? Which are the main building types? What end uses should be considered in the breakdown? Comparisons between different countries are presented specially for commercial buildings. The case of offices is analysed in deeper detail.
5,288 citations
07 Nov 2002
TL;DR: S-MAC uses three novel techniques to reduce energy consumption and support self-configuration, and applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network.
Abstract: This paper proposes S-MAC, a medium-access control (MAC) protocol designed for wireless sensor networks Wireless sensor networks use battery-operated computing and sensing devices A network of these devices will collaborate for a common application such as environmental monitoring We expect sensor networks to be deployed in an ad hoc fashion, with individual nodes remaining largely inactive for long periods of time, but then becoming suddenly active when something is detected These characteristics of sensor networks and applications motivate a MAC that is different from traditional wireless MACs such as IEEE 80211 in almost every way: energy conservation and self-configuration are primary goals, while per-node fairness and latency are less important S-MAC uses three novel techniques to reduce energy consumption and support self-configuration To reduce energy consumption in listening to an idle channel, nodes periodically sleep Neighboring nodes form virtual clusters to auto-synchronize on sleep schedules Inspired by PAMAS, S-MAC also sets the radio to sleep during transmissions of other nodes Unlike PAMAS, it only uses in-channel signaling Finally, S-MAC applies message passing to reduce contention latency for sensor-network applications that require store-and-forward processing as data move through the network We evaluate our implementation of S-MAC over a sample sensor node, the Mote, developed at University of California, Berkeley The experiment results show that, on a source node, an 80211-like MAC consumes 2-6 times more energy than S-MAC for traffic load with messages sent every 1-10 s
5,117 citations
03 Nov 2004
TL;DR: B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC, and the need for flexible protocols to effectively realize energy efficient sensor network applications is illustrated.
Abstract: We propose B-MAC, a carrier sense media access protocol for wireless sensor networks that provides a flexible interface to obtain ultra low power operation, effective collision avoidance, and high channel utilization. To achieve low power operation, B-MAC employs an adaptive preamble sampling scheme to reduce duty cycle and minimize idle listening. B-MAC supports on-the-fly reconfiguration and provides bidirectional interfaces for system services to optimize performance, whether it be for throughput, latency, or power conservation. We build an analytical model of a class of sensor network applications. We use the model to show the effect of changing B-MAC's parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11-inspired protocols, specifically SMAC, we develop an experimental characterization of B-MAC over a wide range of network conditions. We show that B-MAC's flexibility results in better packet delivery rates, throughput, latency, and energy consumption than S-MAC. By deploying a real world monitoring application with multihop networking, we validate our protocol design and model. Our results illustrate the need for flexible protocols to effectively realize energy efficient sensor network applications.
3,631 citations
TL;DR: The estimates of energy usage predict the use of distributed codes, with ≤15% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons.
Abstract: Anatomic and physiologic data are used to analyze the energy expenditure on different components of excitatory signaling in the grey matter of rodent brain. Action potentials and postsynaptic effects of glutamate are predicted to consume much of the energy (47% and 34%, respectively), with the resting potential consuming a smaller amount (13%), and glutamate recycling using only 3%. Energy usage depends strongly on action potential rate--an increase in activity of 1 action potential/cortical neuron/s will raise oxygen consumption by 145 mL/100 g grey matter/h. The energy expended on signaling is a large fraction of the total energy used by the brain; this favors the use of energy efficient neural codes and wiring patterns. Our estimates of energy usage predict the use of distributed codes, with
2,912 citations