Energy Efficiency Balance Research Articles

Cross Layer Design and Implementation for Balancing Energy Efficiency in Wireless Sensor Networks

Cross Layer Design and Implementation for Balancing Energy Efficiency in Wireless Sensor Networks

Technology Tomorrow: The Chemistry of Carbon Capture and Storage

Editor's note: This is the third installment of a yearlong series designed to stimulate discussion in research and development. The oil and gas industry faces the prospect of progressively increasing CO2 production because of the presence of CO2 in produced natural gas and the fact that petroleum production is becoming heavier. However, the industry is uniquely positioned to sequester CO2, no matter the source. This article features the perspective of an expert outside the oil and gas industry regarding the challenges associated with CO2 capture and storage. Comments on articles in the Technology Tomorrow series are welcome. Please send any questions, comments, or ideas to vik.rao@halliburton.com. Evidence linking anthropogenic carbon emissions with climate change is accepted by many experts, and the key challenge now is to act to avert disaster. Countries signed on to the Kyoto Protocol and those that have set more stringent domestic targets are now defining energy policies that balance energy efficiency and energy technologies alongside measures for energy security. In the U.K., the government is currently reviewing its energy policy and is examining a range of policy and technological measures that will secure clean and affordable energy for the long term while meeting the U.K. target of reducing carbon emissions 60% by 2050 (based on 1990 levels). It is widely expected that the U.K. will continue to rely upon fossil fuels for electricity, transportation, and heating for the foreseeable future, and, therefore, it is important that carbon-abatement technologies are employed when feasible. The chemical sciences have played a crucial role in optimizing energy output from fossil fuels through developments in areas such as combustion chemistry and fuel additives. The chemical sciences will be critical in providing future energy technologies such as batteries, fuel cells, solar power, microgeneration, energy-efficient lighting, new nuclear power and nuclear waste management, and carbon capture and storage (CCS). CCS is not a new technology. In fact, there are a number of examples of its application around the globe (e.g., in ammonia production). It has been predicted that CCS technologies could reduce carbon dioxide (CO2) emissions from power plants (and other energy-intensive processes such as oil refining and steel and cement manufacturing) by up to 85%. However, the technology will reduce the energy efficiency of power plants significantly and increase the cost of energy production, according to the Intergovernmental Panel on Climate Change (Table 1).

Balancing energy efficiency and quality of aggregate data in sensor networks

In-network aggregation has been proposed as one method for reducing energy consumption in sensor networks. In this paper, we explore two ideas related to further reducing energy consumption in the context of in-network aggregation. The first is by influencing the construction of the routing trees for sensor networks with the goal of reducing the size of transmitted data. To this end, we propose a group-aware network configuration method that “clusters” along the same path sensor nodes that belong to the same group. The second idea involves imposing a hierarchy of output filters on the sensor network with the goal of both reducing the size of transmitted data and minimizing the number of transmitted messages. More specifically, we propose a framework to use temporal coherency tolerances in conjunction with in-network aggregation to save energy at the sensor nodes while maintaining specified quality of data. These tolerances are based on user preferences or can be dictated by the network in cases where the network cannot support the current tolerance level. Our framework, called TiNA, works on top of existing in-network aggregation schemes. We evaluate experimentally our proposed schemes in the context of existing in-network aggregation schemes. We present experimental results measuring energy consumption, response time, and quality of data for Group-By queries. Overall, our schemes provide significant energy savings with respect to communication and a negligible drop in quality of data.

NONLINEAR OPTIMAL CONTROL OF HVAC SYSTEMS

In this paper we consider the optimum control of heating, ventilation and air-conditioning (HVAC) systems. The objective of the control is to balance energy efficiency, against user comfort. The HVAC system it self is nonlinear, and the cost function to be minimized, non-quadratic. Both adaptive and non-adaptive strategies are given.