Enhancing route request broadcasting protocols constitutes a substantial part of research in mobile ad hoc network routing. In the thesis, enhancements of ad hoc routing protocols, energy efficiency metrics and clustered topology generators are discussed. The contributions include the followings. First, a node caching enhancement of Ad-hoc On-demand Distance Vector (AODV) routing protocol is introduced. Extensive simulation studies of the enhanced AODV in NS2 shows up to 9-fold reduction in the routing overhead, up to 20% improvement in the packet delivery ratio and up to 60% reduction in the end-to-end delay. The largest improvement happens to highly stressed situations. Secondly, new metrics for evaluating energy efficiency of routing protocols are suggested. New node cached AODV protocols employing non-adaptive and adaptive load balancing techniques were proposed for extending network lifetime and increasing network throughput. Finally, the impact of node clustered topology on ad hoc network is explored. A novel method for generating clustered layout in NS2 is introduced and experiments indicate performance degradation of AODV protocols for the case of two clusters.

The purpose with this report is to examine how the implemented energy efficiency measures on Bokelundskolan in Växjö have had impact on the energy use. The measures which have been implemented are, new ventilation system, new heating system, optimized adjustment of the heating system, new windows with U-value 1.2 and lower window height, insulation under windows and in the crawl space. The school's energy use before and after rebuilding has been calculated with the calculation program, Vip-Energy 1.5.5. Calculations of energy use for the school with windows on U-value, 0.9 and 0.7 have also been done. The replacement of windows has been studied from a cost perspective. The conclusion is that the exchange of heat and ventilation system was the biggest contributor to reduced energy use on Bokelundskolan. The evaluation of windows with different U-value showed that windows with U-value 1.2 were the most profitable.

This dissertation consists of three independent essays in macroeconomics. The first essay studies the transition to a low carbon economy using an extension of the neoclassical growth model featuring endogenous energy efficiency, exhaustible energy and explicit climate-economy interaction. I derive the properties of the laissez faire equilibrium and compare them to the optimal allocations of a social planner who internalizes the climate change externality. Three main results emerge. First, the exhaustibility of energy generates strong market based incentives to improve energy efficiency and reduce CO2 emissions without any government intervention. Second, the market and optimal allocations are substantially different suggesting a role for the government. Third, high and persistent taxes are required to implement the optimal allocations as a competitive equilibrium with taxes. The second essay focuses on coal fired power plants (CFPP) - one of the largest sources of CO2 emissions globally - and their generation efficiency using a macroeconomic model with an embedded CFPP sector. A key feature of the model is the endogenous choice of production technologies which differ in their energy efficiency. After establishing four empirical facts about the CFPP sector, I analyze the long run quantitative effects of energy taxes. Using the calibrated model, I find that sector-specific coal taxes have large effects on generation efficiency by inducing the use of more efficient technologies. Moreover, such taxes achieve large CO2 emissions reductions with relatively small effects on consumption and output. The final essay studies the procyclicality of fiscal policy in developing countries, which is a well-documented empirical observation seemingly at odds with Neoclassical and Keynesian policy prescriptions. I examine this issue by solving the optimal fiscal policy problem of a small open economy government when the interest rates on external debt are endogenous. Given an incomplete asset market, endogeneity is achieved by removing the government's ability to commit to repaying its external obligations. When calibrated to Argentina, the model generates procyclical government spending and countercyclical labor income tax rates. Simultaneously, the model's implications for key business cycle moments align well with the data.

The last decade has been characterized by radical changes in the computing landscape. We have witnessed the advent of multi-core processors, flash-based storage systems and the proliferation of scale out architectures, such as map-reduce-based systems and massively parallel databases. Although data management systems have embraced modern hardware technologies to some extent, they have not realized their full potential. The goal of this thesis is two-fold. Primarily, it demonstrates the staggering potential for performance improvement offered by modern hardware architectures and, then, proposes how data management systems must alter in order to realize this potential. Additionally, this thesis demonstrates that utilizing modern hardware architectures is important both for performance and energy-efficiency. Towards this goal, we propose query processing and indexing techniques for chip multiprocessors and we analyze the trade-offs of executing complex database queries on modern processor technologies. Subsequently, we propose query processing methods tailored to flash-based storage systems. Finally, we analyze the power consumption of database systems and we reveal opportunities for improving their energy efficiency.

With advances in hardware miniaturization and wireless communication technologies even small portable wireless devices have much communication bandwidth and computing power. These devices include smartphones, tablet computers, and personal digital assistants. Users of these devices expect to run software applications that they usually have on their desktop computers as well as the new applications that are being developed for mobile devices. Web browsing, social networking, gaming, online multimedia playing, global positioning system based navigation, and accessing emails are examples of a few popular applications. Mobile versions of thousands of desktop applications are already available in mobile application markets, and consequently, the expected operational time of smartphones is rising rapidly. At the same time, the complexity of these applications is growing in terms of computation and communication needs, and there is a growing demand for energy in smartphones. However, unlike the exponential growth in computing and communication technologies, in terms of speed and packaging density, battery technology has not kept pace with the rapidly growing energy demand of these devices. Therefore, designers are faced with the need to enhance the battery life of smartphones. Knowledge of how energy is used and lost in the system components of the devices is vital to this end. With this view, we focus on modeling and evaluating the energy performance of smartphones in this thesis. We also propose techniques for enhancing the energy efficiency and functionality of smartphones. The detailed contributions of the thesis are as follows: (i) we present a nite state machine based model to estimate the energy cost of an application running on a smartphone, and provide practical approaches to extract model parameters; (ii) the concept of energy cost pro le is introduced to assess the impact of design decisions on energy cost at an early stage of software design; (iii) a generic architecture is proposed and implemented for enhancing the capabilities of smartphones by sharing resources; (iv) we have analyzed the Internet tra c of smartphones to observe the energy saving potentials, and have studied the implications on the existing energy saving techniques; and nally, (v) we have provided a methodology to select user level test cases for performing energy cost evaluation of applications. All of our concepts and proposed methodology have been validated with extensive measurements on a real test bench. Our work contributes to both theoretical understanding of energy e ciency of software applications and practical methodologies for evaluating energy e ciency. In summary, the results of this work can be used by application developers to make implementation level decisions that affect the energy efficiency of software applications on smartphones. In addition, this work leads to the design and implementation of energy e cient smartphones.

Buildings are a significant energy consumer and are responsible for an increasingly large percent of worldwide greenhouse gas emissions, currently between 30 and 40 percent. Energy efficiency presents unique opportunities for building owners to reduce their environmental footprint and add value through cost savings, tax deductions, and increased market value. An analysis of 183 samples of efficiency measures in seven technology categories found that 74% of efficiency investments had a positive net present value. Building automation system and chiller plant improvements had the highest mean energy and carbon dioxide savings per square foot. Additionally, building automation systems had, on average the highest return on investment, approximately $800 above the cost of implementation per one thousand square feet. Only building envelope modifications had a negative mean return on investment. Building automation system upgrades avoided an average of 350 pounds of CO2e for every dollar spent, reducing a building’s total carbon footprint by as much as 28%. The results suggest that a significant opportunity for cost, energy, and emission savings is available across all technology categories.

This dissertation consists of three independent essays in macroeconomics. The first essay studies the transition to a low carbon economy using an extension of the neoclassical growth model featuring endogenous energy efficiency, exhaustible energy and explicit climate-economy interaction. I derive the properties of the laissez faire equilibrium and compare them to the optimal allocations of a social planner who internalizes the climate change externality. Three main results emerge. First, the exhaustibility of energy generates strong market based incentives to improve energy efficiency and reduce CO2 emissions without any government intervention. Second, the market and optimal allocations are substantially different suggesting a role for the government. Third, high and persistent taxes are required to implement the optimal allocations as a competitive equilibrium with taxes. The second essay focuses on coal fired power plants (CFPP) - one of the largest sources of CO2 emissions globally - and their generation efficiency using a macroeconomic model with an embedded CFPP sector. A key feature of the model is the endogenous choice of production technologies which differ in their energy efficiency. After establishing four empirical facts about the CFPP sector, I analyze the long run quantitative effects of energy taxes. Using the calibrated model, I find that sector-specific coal taxes have large effects on generation efficiency by inducing the use of more efficient technologies. Moreover, such taxes achieve large CO2 emissions reductions with relatively small effects on consumption and output. The final essay studies the procyclicality of fiscal policy in developing countries, which is a well-documented empirical observation seemingly at odds with Neoclassical and Keynesian policy prescriptions. I examine this issue by solving the optimal fiscal policy problem of a small open economy government when the interest rates on external debt are endogenous. Given an incomplete asset market, endogeneity is achieved by removing the government's ability to commit to repaying its external obligations. When calibrated to Argentina, the model generates procyclical government spending and countercyclical labor income tax rates. Simultaneously, the model's implications for key business cycle moments align well with the data.

The last decade has been characterized by radical changes in the computing landscape. We have witnessed the advent of multi-core processors, flash-based storage systems and the proliferation of scale out architectures, such as map-reduce-based systems and massively parallel databases. Although data management systems have embraced modern hardware technologies to some extent, they have not realized their full potential. The goal of this thesis is two-fold. Primarily, it demonstrates the staggering potential for performance improvement offered by modern hardware architectures and, then, proposes how data management systems must alter in order to realize this potential. Additionally, this thesis demonstrates that utilizing modern hardware architectures is important both for performance and energy-efficiency. Towards this goal, we propose query processing and indexing techniques for chip multiprocessors and we analyze the trade-offs of executing complex database queries on modern processor technologies. Subsequently, we propose query processing methods tailored to flash-based storage systems. Finally, we analyze the power consumption of database systems and we reveal opportunities for improving their energy efficiency.

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.

Under the pressure of energy shortage, energy saving has become one of the most important topics. The world isseeking different ways to follow the sustainable development concept and to solve the energy shortage crisis.This thesis is based on the idea of improving energy efficiency in the building industry which is one of thebiggest energy consumption industries. The aim of this paper is to simulate a renovation of an existing oldbuilding in Sweden according to the concept of building a Swedish Passive House and to see how much energycould be saved after the renovation. The target building Taljstenen 7A-C was built in 1960 in Uppsala and itbelongs to the housing company Uppsalahem. The target building is facing extensive renovation due to its age.An energy consumption model of the present building was built by the software VIP-Energy after measurementsand calculations. Based on the model, three important improvements are made in a simulative renovation process.The three improvements are insulating building envelope, installing a new FTX ventilation system with highefficient heat recovery system and installing solar collectors for hot top water and space heating. The resultsshow a significant reduction of energy consumption of the renovated building compared to the original onewhich is from 516MWh per year decreased to 371MWh. Although the renovated building did not completelyfulfil the Passive House Standard in Sweden, it still has improved to be a low energy building. The purpose ofsaving energy can be achieved.