Selecting users in energy-efficient collaborative spectrum sensing

Cognitive radio network is defined as an intelligent wireless communication network that should be able to adaptively reconfigure its communication parameters to meet the demands of the transmission network or the user. In this context one possible way to utilize unused licensed spectrum without interfering with incumbent users is through spectrum sensing. Due to channel uncertainties, single cognitive (opportunistic) user cannot make a decision reliably and hence collaboration among multiple users is often required. Here collaboration among large number of users tends to increase power consumption and introduces large communication overheads. In this paper, the number of collaborating users is optimized in order to maximize the probability of detection for any given power budget in a cognitive radio network, while satisfying constraints on the false alarm probability. We show that for the maximum probability of detection, collaboration of only a subset of available opportunistic users is required. The robustness of our proposed spectrum sensing algorithm is also examined under flat Rayleigh fading and AWGN channel conditions.

Performance monitoring of energy efficient retrofits – 4 case study properties in Northern Ireland

Approximately half of the houses in Northern Ireland were built before any form of minimum thermal specification (U-value) or energy efficiency standard were available. At present, 44% of households are categorised as being in fuel poverty; spending more than 10% of the household income to heat the house to an acceptable level. This paper presents the results from long term performance monitoring of 4 case study houses that have undergone retrofits to improve energy efficiency in Northern Ireland. There is some uncertainty associated with some of the marketed retrofit measures in terms of their effectiveness in reducing energy usage and their potential to cause detrimental impacts on the internal environment of a house. Using wireless sensor technology internal conditions such as temperature and humidity were measured alongside gas and electricity usage for a year. External weather conditions were also monitored. The paper considers the effectiveness of the different retrofit measures implemented based on the long term data monitoring and short term building performance evaluation tests that were completed.

Building performance evaluation of domestic energy efficient retrofits in current and future climates

Approximately half of the houses in Northern Ireland were built before any form of minimum thermal specification or energy efficiency standard was enforced. Furthermore, 44% of households are categorised as being in fuel poverty; spending more than 10% of the household income to heat the house to bring it to an acceptable level of thermal comfort. To bring existing housing stock up to an acceptable standard, retrofitting for improving the energy efficiency is essential and it is also necessary to study the effectiveness of such improvements in future climate scenarios. This paper presents the results from a year-long performance monitoring of two houses that have undergone retrofits to improve energy efficiency. Using wireless sensor technology internal temperature, humidity, external weather, household gas and electricity usage were monitored for a year. Simulations using IES-VE dynamic building modelling software were calibrated using the monitoring data to ASHARE Guideline 14 standards. The energy performance and the internal environment of the houses were then assessed for current and future climate scenarios and the results show that there is a need for a holistic balanced strategy for retrofitting.

Shedding Light on the light bulb puzzle: the role of attitudes and perceptions in the adoption of energy efficient light bulbs

Despite the potential energy savings and economic benefits associated with compact fluorescent light bulbs, their adoption by the residential sector has been limited to date. In this paper, we present a theoretical model that focuses on the agents' ability to perceive the correct cost of lighting and on the role of environmental attitudes as key determinants of the adoption decision. We use original data from Ireland to test our theoretical predictions. Our results emphasize the importance of education, information and environmental awareness in the adoption decision.

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.