With the upcoming of the fifth generation of cellular networks there are several perfor- mance requirements that need to be satisfied. This thesis focuses on the topic of mobility which allows users to move through the network using the concept of handovers to switch between base stations. However, the thesis also keeps the energy efficiency in mind and investigates a strategy of reducing the energy consumption. Moving across base stations will inevitably lead to some handover failures, a goal of the system developers is to reduce these, but there exists a tradeoff between too early and too late handover failures. This thesis investigates two approaches of lowering the number of failures by letting the net- work self-optimize parameters in the handover procedure based upon the tradeoff. The first approach includes a parameter adaption to an estimated velocity of the users and the second one making a parameter more granular. Simulating different scenarios in a detailed network simulator shows performance gain by adapting handover parameters to the esti- mated velocity, but gathering more data regarding failures is needed before generalizing the conclusions.

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches.

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches.

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches.

Microprocessors are used in a variety of systems ranging from high-performance super computers running scientific applications to battery powered cell phones performing realtime tasks. Due to the large disparity between processor clock speed and main memory access time, most modern processors include several caches, which consume more than half of the total chip area and power budget. As the performance gap between processors and memory has increased, the trend has been to increase the size of the on-chip caches. However, increasing the cache size also increases its access time and energy consumptions. This growing power dissipation problem is making traditional cooling and packaging techniques less effective thus requiring cache designers to focus more on architectural level energy efficiency than performance alone. The goal of this thesis is to propose a new cache architecture and to evaluate its efficiency in terms of miss rate, system performance, energy consumption, and area overhead. The proposed architecture employs the use of a few Ternary-CAM (TCAM) cells in the tag array to enable dynamic compression of tag entries containing contiguous values. By dynamically compressing tag entries, the number of entries in the tag array can be reduced by 2N, where N is the number of tag bits that can be compressed. The architecture described in this thesis is applicable to any cache structure that uses Content Addressable Memory (CAM) cells to store tag bits. To evaluate the effectiveness of the TCAM Enhanced Cache Architecture for a wide scope of applications, two case studies were performed ?? the L2 Data-TLB (DTLB) of a high-performance processor and the L1 instruction and data caches of a low-power embedded processor. Results indicate that a L2 DTLB implementing 3-bit tag compression can achieve 93% of the performance of a conventional L2 DTLB of the same size while reducing the on-chip energy consumption by 74% and the total area by 50%. Similarly, an embedded processor cache implementing 2-bit tag compression achieves 99% of the performance of a conventional cache while reducing the on-chip energy consumption by 33% and the total area by 10%.

Energy-efficient building design involves minimizing the energy use and optimizing the performance of individual systems and components of the building. The benefits of energyefficient design, in the residential sector, are direct and tangible, provided that design strategies with a substantial combined energy and cost-saving potential are adopted. Many studies have been performed to evaluate the energy-saving potential and the costeffectiveness of various design options, and to identify conditions for optimizing the performance of building systems and components. The results of these studies, published in various resources, were analyzed discretely using different techniques, and were reported using different bases for comparison. Considering the complex interaction of, and energy flows through various building components, it is difficult to directly compare/combine the results from various studies to determine the energy-saving potential of combination of strategies, and to select an appropriate set of strategies for making design decisions. Therefore, this thesis develops a comprehensive survey and analysis of energy-efficient design strategies and their energy-saving potential, in isolation as well as in combination, using a DOE-2 simulation model of a prototype house in the hot and humid climate of Houston, Texas. Optimized strategies that included building configuration, materials/ assembly for building envelop components, and efficient mechanical and electrical systems, equipment and appliances, were applied in combination that could minimize the annual energy use. Application of these strategies is expected to allow downsizing systems and equipment and to confirm their operation at their rated performance, resulting in additional installation and operation cost savings. The study is concluded by outlining the procedures for selecting optimized set of strategies, and by developing guidelines for achieving maximum energy-efficiency in singlefamily detached houses in hot and humid climates. Thus, this study will facilitate the selection of energy-saving measures for their individual or combined application for developing energyefficient residences in hot and humid climates.

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches.

The purpose of this study is to reduce the energy cost of the low-income households in the hot and humid climates of the U.S. and thereby to help them afford comfortable homes. In this perspective, a new HVAC energy saving strategy, i.e. “partial conditioning” was modeled and its potential to reduce the HVAC energy consumption of the low income homes in Texas was quantified. The “partial conditioning” strategy combined three primary ideas: 1) using historic courtyard building schemes to provide a buffer zone between conditioned spaces, 2) zoning and applying occupancy based heating/cooling in each zone, and 3) reusing the conditioned air returning from the occupied zones in the unoccupied zones before it is returned to the system. The study was conducted in four steps: 1) data collection, 2) baseline design and modeling, 3) partial conditioning design and modeling, and 4) analyses and recommendations. First, a site visit was held to the Habitat for Humanity office in Bryan, Texas to collect data on the characteristics of the Habitat for Humanity houses built in Bryan. Second, a base-line Habitat for Humanity house was designed and modeled based on this information along with multiple other resources including International Energy Conservation Code 2012 and Building America benchmark definitions. A detailed comparison was made between the commonly used energy modeling tools (DOE-2.1e, EnergyPlus and TRNSYS) and a modeling method was developed for the estimation of the baseline energy consumption. Third, the “partial conditioning” strategy was introduced into the baseline energy model to simulate a partially conditioned atrium house. As the occupied zone and the direction of the airflow changed throughout the year in the partially conditioned house, this step required an innovative air loop model with interzonal air ducts that allowed for sched- uled bi-directional airflow. This air loop was modeled with the AirflowNetwork model of EnergyPlus. Fourth, the modeling results were analyzed and discussed to determine the performance of the partial conditioning strategy in a hot and humid climate. It was found that partial conditioning strategy can provide substantial (37%-46%) reduction in the overall HVAC energy consumption of small residences (∼1,000 ft2) in hot and humid climates while performing better in meeting the temperature set points in each room. It was also found that the quantity of the energy savings that can be obtained with the partial conditioning strategy depends significantly on the ground coupling condition of the house for low rise residential buildings.

An energy-efficient diversity scheme based on the well researched Generalized-Switch-and-Examine Combining (GSEC) is presented. The presented scheme is more efficient in terms of providing better average combined SNR per active path. This results in considerable processing power savings of the receiver especially compared to the GSC scheme. EE-GSEC performance in terms of the average combined SNR, outage probability and average bit error rate (BER) are comparable to GSEC under certain conditions. EE-GSEC’s complexity performance is better than GSC and same as GSEC. This results in a considerable hardware cost savings at the receiver. However, the complexity savings come at the cost of performance when compared to GSC. This is a natural trade-off and needs to be considered when designing a wireless communication system. A thorough statistical analysis of the presented scheme is performed and then used to mathematically formulate the performance and complexity expressions. Using simulations the performance and complexity of EE-GSEC is examined and compared against other competing schemes. An energy efficiency analysis that validates the efficiency claims of the scheme is also performed. / Graduate / 0544 / hpbains@gmail.com

abstract: The last decade has witnessed a paradigm shift in computing platforms, from laptops and servers to mobile devices like smartphones and tablets. These devices host an immense variety of applications many of which are computationally expensive and thus are power hungry. As most of these mobile platforms are powered by batteries, energy efficiency has become one of the most critical aspects of such devices. Thus, the energy cost of the fundamental arithmetic operations executed in these applications has to be reduced. As voltage scaling has effectively ended, the energy efficiency of integrated circuits has ceased to improve within successive generations of transistors. This resulted in widespread use of Application Specific Integrated Circuits (ASIC), which provide incredible energy efficiency. However, these are not flexible and have high non-recurring engineering (NRE) cost. Alternatively, Field Programmable Gate Arrays (FPGA) offer flexibility to implement any application, but at the cost of higher area and energy compared to ASIC. In this work, a spatially programmable architecture customized for image processing applications is proposed. The intent is to bridge the efficiency gap between ASICs and FPGAs, by offering FPGA-like flexibility and ASIC-like energy efficiency. This architecture minimizes the energy overheads in FPGAs, which result from the use of fine-grained programming style and global interconnect. It is flexible compared to an ASIC and can accommodate multiple applications. The main contribution of the thesis is the feasibility analysis of the data path of this architecture, customized for image processing applications. The data path is implemented at the register transfer level (RTL), and the synthesis results are obtained in 45nm technology cell library from a leading foundry. The results of image-processing applications demonstrate that this architecture is within a factor of 10x of the energy and area efficiency of ASIC implementations. / Dissertation/Thesis / Masters Thesis Computer Science 2016