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The power modeling of mobile application processors (APs) is a challenging task due to their complexity. The existing power models and their associated devices have mostly been made obsolete by recent hardware developments. In this paper, we propose an enhanced power model used in modern mobile devices. The model accurately estimates the power consumption of AP component and utilizes the runtime usage information of each hardware component. We evaluated the model accuracy using various benchmarks, as well as popular smartphone applications with multiple devices that employ different APs. The evaluation shows that our model achieves the mean absolute percentage error (MAPE) of 5.1%. [ABSTRACT FROM AUTHOR]

The problem of cooperative data censoring in battery-powered multihop sensor networks is analyzed in this paper. We are interested in scenarios where nodes generate messages (which are related to the sensor measurements) that can be graded with some importance value. Less important messages can be censored in order to save energy for later communications. The problem is modeled using a joint Markov Decision Process of the whole network dynamics, and a theoretically optimal censoring policy, which maximizes a long-term reward, is found. Though the optimal censoring rules are computationally prohibitive, our analysis suggests that, under some conditions, they can be approximated by a finite collection of constant-threshold rules. A centralized algorithm for the computation of these thresholds is proposed. The experimental simulations show that cooperative censoring policies are energy-efficient, and outperform other non-cooperative schemes. [ABSTRACT FROM AUTHOR]

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic. • Distributed a questionnaire about energy data visualization built in a mobile app. • Enhanced charts in the energy field have been created. • Comparison between enhanced and conventional energy visualization charts is applied. • Quantitative and qualitative questions asked in the questionnaire. • Received 133 participants' responses from multiple countries. [ABSTRACT FROM AUTHOR]

• A novel system aiming at gas demand response from domestic gas boilers is presented. • A novel methodology to calculate the gas demand baseline is provided. • Neural networks are used to forecast the indoor conditions and the boiler modulation. • Optimization of consumer's welfare from gas supply and market participation. • The presented gas DR system handles efficiently the so-called "rebound effect" This paper presents a novel, fully automated system for the implementation of explicit gas Demand Response in the Gas Balancing Market, with real-time control instructions provided to domestic gas boilers. The Demand Response is applied in both the upward and downward direction, enabling the respective gas consumer to provide both upward and downward Balancing Services to the Gas Transmission System Operator. The system targets at the welfare maximization of domestic gas consumers, i.e., the revenues attained by providing Balancing Services to the Gas Transmission System Operator minus the cost of gas supply, while maintaining the house's indoor temperature within the residents' comfort limits. Real-time data are derived from interconnected commercial thermostats and used by two Recurrent Neural Networks for each house, in order to attain forecasts for the indoor temperature change and the expected boiler's modulation levels for the next future time intervals. Such forecasted condition changes within each house are then considered in an optimization model that results in the optimal instruction signal that must be provided to the boiler. The signal concerns the boiler operation level for the following 5 min duration. A Genetic Algorithm is employed for the optimization problem solution. The whole system is deployed using a containerized software architecture to ensure scalability and full-service availability. The implemented real-world tests exhibit that domestic consumers can increase their profits from both gas consumption minimization and from the provision of gas DR services in real-time. [ABSTRACT FROM AUTHOR]

Abstract: Selective communication (censoring) strategies allow nodes in a sensor network to discard low importance messages with the purpose of saving energy that can be used for transmitting more important messages later. In this paper we apply simple selective policies based on Markov Decision Processes to a distributed target tracking scenario based on particle filters and the Information-Driven Sensor Querying (IDSQ) scheme. The resulting algorithm is combined with other energy-efficient schemes, such as data aggregation or data fusion, extending the action space of these techniques. Our simulation work shows that the network lifetime can be substantially increased while keeping a low tracking error. And even more, selecting the sampling rate properly, lifetime is prolonged without increasing the tracking error. [Copyright &y& Elsevier]

Abstract: In this paper we present an adaptive application-driven power management (AADPM) strategy with online idle period length distribution learning capability for the IEEE 802.11b WLAN. We discuss its design and evaluate the performance in comparison with other power management strategies using the network simulator NS2. We simulated both the single user and multiple user scenarios. Experimental results have shown that, compared with other power management methods examined in this paper, AADPM achieved the highest energy saving in all cases and it demonstrated strong adaptability to network congestion. [Copyright &y& Elsevier]