With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance.

Power management is an active area of research in wireless sensor networks (WSNs). Efficient power management is necessary because WSNs are battery-operated devices that can be deployed in mission-critical applications. From the communications perspective, one main approach to reduce energy is to maximize throughput so the data can be transmitted in a short amount of time. Frame fragmentation techniques aim to achieve higher throughput by reducing retransmissions. Using experiments on a WSN testbed, we show that frame fragmentation helps to reduce energy consumption. We then study and compare recent frame fragmentation schemes to find the most energy-efficient scheme. Our main contribution is to propose a new frame fragmentation scheme that is optimized to be energy efficient, which is originated from the chosen frame fragmentation scheme. This new energy-efficient frame fragmentation protocol is called (Green-Frag). Green-Frag uses an algorithm that gives sensor nodes the ability to transmit data with optimal transmit power and optimal frame structure based on environmental conditions. Green-Frag takes into consideration the channel conditions, interference patterns and level, as well as the distance between sender and receiver. The thesis discusses various design and implementation considerations for Green-Frag. Also, it shows empirical results of comparing Green-Frag with other frame fragmentation protocols in terms of energy efficiency. Green-Frag performance results shows that it is capable of choosing the best transmit according to the channel conditions. Subsequently, Green-Frag achieves the least energy consumption in all environmental conditions.

Heterogeneous multicore processors offer an energy-efficient alternative to homogeneous multicores. Typically, heterogeneous multi-core refers to a system with more than one core where all the cores use a single ISA but differ in one or more micro-architectural configurations. A carefully designed multicore system consists of cores of diverse power and performance profiles. During execution, an application is run on a core that offers the best trade-off between performance and energy-efficiency. Since the resource needs of an application may vary with time, so does the optimal core choice. Moving a thread from one core to another involves transferring the entire processor state and cache warm-up. Frequent migration leads to large performance overhead, negating any benefits of migration. Infrequent migration on the other hand leads to missed opportunities. Thus, reducing overhead of migration is integral to harnessing benefits of heterogeneous multicores. \par This work proposes \textit{AdaCore}, a novel core architecture which pushes the heterogeneity exploited in the heterogeneous multicore into a single core. \textit{AdaCore} primarily addresses the resource bottlenecks in workloads. The design attempts to adaptively match the resource demands by reconfiguring on-chip resources at a fine-grain granularity. The adaptive core morphing allows core configurations with diverse power and performance profiles within a single core by adaptive voltage, frequency and resource reconfiguration. Towards this end, the proposed novel architecture while providing energy savings, improves performance with a low overhead in-core reconfiguration. This thesis further compares \textit{AdaCore} with a standard Out-of-Order core with capability to perform Dynamic Voltage and Frequency Scaling (DVFS) designed to achieve energy efficiency. The results presented in this thesis indicate that the proposed scheme can improve the performance/Watt of application, on average, by 32\% over a static out-of-order core and by 14\% over DVFS. The proposed scheme improves $IPS^{2}/Watt$ by 38\% over static out-of-order core.

A suitable environment for the growth of plants is the Greenhouse, that needs to be monitored by a continuous collection of data related to temperature, carbon dioxide concentration, humidity, illumination intensity using sensors, preferably in a wireless sensor network (WSN). Demand initiates various challenges for diversified applications of WSN in the field of IoT (Internet of Things). Network design in IoT based WSN faces challenges like limited energy capacity, hardware resources, and unreliable environment. Issues like cost and complexity can be limited by using sensors that are heterogeneous in nature. Since replacing or recharging of nodes in action is not possible, heterogeneity in terms of energy can overcome crucial issues like energy and lifetime. In this paper, an energy efficient routing process is discussed that considers three different sensor node categories namely normal, intermediate and advanced nodes. Also, the basic cluster head (CH) selection threshold value is modified considering important parameters like initial and residual energy with an optimum number of CHs in the network. When compared with routing algorithms like LEACH (Low Energy Adaptive Clustering Hierarchy) and SEP (Stable Election Protocol), the proposed model performs better for metrics like throughput, network stability and network lifetime for various scenarios.

The construction industry has increasingly moved in an environmentally conscious direction when energy-efficient and sustainable buildings are in focus. The motivation behind this development is largely about reducing emissions and relieving the environment, both through new construction, but also through making existing buildings more efficient. For that reason, it is relevant to highlight how energy efficiency of existing properties are done, and how it can be optimized from an environmentally conscious perspective. In order to get a clear picture of energy efficiency and its environmental impact, the study will highlight the differences between new production and existing buildings. The parts that will be discussed are economics, sustainability, and the environment. Through qualitative interviews, the largest real estate companies in Skåne will reveal what the work with energy efficiency of existing commercial buildings looks like. Therefore, the purpose of this study is to investigate how the large real estate companies in Skåne work with their existing commercial properties. The method in the study is a qualitative method where people at the property companies had to answer a selection of questions. The questions posed are relevant to modern society and highlight how real estate companies work with environmental factors and economic factors. The result shows that the study of energy efficiency of existing buildings is a crucial aspect of sustainable development. The study deals with the construction and property industry's emissions of greenhouse gases, and it is therefore of the utmost importance to review the existing properties that the property companies own and where there are opportunities for improvement. The improvements are what make existing buildings increase their environmental performance so that they can comply with the environmental requirements that both Sweden and the EU provide. In conclusion, an increased awareness of the benefits of energy efficiency should produce results, and this could be done throughout training where the individuals who work with properties get a greater insight into energy saving methods.

In recent times data-driven approaches to parametrically optimize and explore building geometry has been proven to be a powerful tool that can replace computationally expensive and time-consuming simulations for energy prediction in the early design process. In this research, we explore the use of surrogate models, i.e. efficient statistical approximations of expensive physics-based building simulation models, to lower the computational burden of large-scale building geometry analysis. We try different approaches and techniques to train a machine learning model using multiple datasets to analyze the impact of geometry and envelope features on the energy efficiency of buildings. These contributions are presented in the form of two conference papers and one journal paper (being prepared for submission) that iteratively build up the underlying methodology. The first conference paper contains preliminary experiments using 4 manually generated building geometries for office buildings. Data were generated by simulating various building samples in EnergyPlus for different geometries. We used the generated data to train a machine learning model using support vector regression. We trained two separate models for predicting heating and cooling loads. The lesson learned from this first experiment was that the prediction of the models was not great due to insufficient geometric features explaining the variability in geometry and the lack of sufficient data for varied geometries. The second conference paper developed a novel dataset of 38,000 building energy models for varied geometry using 2D images of real-world residences. We developed a workflow in the Grasshopper/Rhino environment which can convert 2D images of a floor plan into a vector format then into a building energy model ready to be simulated in EnergyPlus. The workflow can also extract up to 20 geometric features from the model, to be used as features in the machine learning process. We used these features and the simulation results to train a neural network-based surrogate model. A sensitivity analysis was performed to understand the impact and importance of each feature to the energy use of the building. From the results of the experiment, we found that off-the-shelf neural network-based surrogates provided with engineered features can very well emulate the desired simulation outputs. We also repeated the experiment for 6 different climatic zones across Canada to understand the impact of geometric features across various climates; these findings are presented in an appendix. iv In the journal paper, we explored two different methodologies to train surrogate models: monolithic and component-based. We explored the component-based modeling technique as it allows the model to be more versatile if we need to add more components to it, ultimately increasing the usability of the model. We conducted further experiments by adding complexity to the geometry surrogate model. We introduced 10 envelope features as an input to the surrogate along with the 20 geometric features. We trained 6 different surrogate models using different datasets by varying geometric and envelope features. From the results of the experiment, we found that the monolithic model performs the best but the component-based surrogate also falls into an acceptable range of accuracy. From the overall results across the three papers, we see that simple neural network-based surrogate models perform really well to emulate simulation outcomes over a wide variety of geometries and envelope features / Graduate

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The low-energy characteristics of Wireless Sensor Networks (WSNs) pose a great design challenge for MAC protocol design. The cluster-based scheme is a promising solution. Recent studies have proposed different cluster-based MAC protocols. We propose an intra-cluster communication bit-map-assisted (BMA) MAC protocol. BMA is intended for event-driven applications. The scheduling of BMA can change dynamically according to the unpredictable variations of sensor networks. In terms of energy efficiency, BMA reduces energy consumption due to idle listening and collisions. In this study, we develop two different analytic energy models for BMA, conventional TDMA and energy efficient TDMA (E-TDMA) when used as intra-cluster MAC schemes. Simulation experiments are constructed to validate the analytic models. Both analytic and simulation results show that in terms of energy efficiency, BMA performance heavily depends on the sensor node traffic offer load, the number of sensor nodes within a cluster, the data packet size and, in some cases, the number of sessions per round. BMA is superior for the cases of low and medium traffic loads, relatively few sensor nodes per cluster, and relatively large data packet sizes. In addition, BMA outperforms the TDMA-based MAC schemes in terms of average packet latency.

<p>    </p> <p>An energy harvesting system (EHS) provides the intriguing possibility of battery-less computing and enables various applications such as wearable, industrial or environmental sensors, and im- plantable medical devices. The biggest challenge of EHS is the instability of energy sources (e.g., Wi-Fi, solar, thermal energy, etc.) which causes unpredictable and frequent power outages. To address the challenge, existing works introduce software-based and hardware-based power failure recovery solutions that ensure program correctness across a power outage. However, they cause a significant performance overhead without providing the high quality of service in reality, and suffer from a reliability issue. In this dissertation, we address the limitations of recovery solutions across the system stack, from the compiler-directed approach and run-time systems to hardware mechanisms, and demonstrate the effectiveness of the approaches using real EHS platforms and simulators. We first present software-based recovery solutions by leveraging compiler support. We develop a compiler-directed solution built upon commodity EHS platform that can achieve 3X speedup compared to the software-based state-of-the-art solution. We also introduce a compiler optimization technique that can cooperate with run-time systems and hardware support, achieving 8X speedup compared to the software-based solution. We then present hardware-based recov- ery solutions by leveraging compiler and hardware support. We develop an architecture/compiler co-design solution that re-purposes existing hardware components in a core for power failure spec- ulative execution, a new speculation paradigm, and leverages a novel compiler analysis for cor- rect power failure recovery. Our result highlights 2 ∼ 3x performance improvement compared to the hardware-based state-of-the-art solution without requiring hardware modification. Next, we present a new cache design for EHS that can achieve cost-effective, high-performance intermit- tent computing. According to experimental results, the new cache design outperforms the state- of-the-art cache scheme by 4X and reduces the hardware cost by 90%. Finally, we present an operating system (OS)-driven solution to address a reliability problem on EHS devices while all existing works are vulnerable, causing the wrong recovery across power failure. Our experiments demonstrate that the solution causes less than 1% run-time overhead and successfully addresses the reliability problem without compromising correct power failure recovery. </p>

Wireless network devices, especially in ad hoc networks, are typically battery-powered. The growing need for energy efficiency in wireless networks, in general, and in mobile ad hoc networks (MANETs), in particular, calls for power enhancement features. The goal of this dissertation is to extend network lifetime by improving energy utilization in MANET routing. We utilize the ability of wireless network interface cards to dynamically change their transmission power, as well as the ability of wireless devices to read the remaining battery energy of the device to create a table of what we term "reluctance values," which the device uses to determine how to route packets. Choosing routes with lower reluctance values, on average and with time, leads to better utilization of the energy resources of the devices in the network. Our power-aware scheme can be applied to both reactive and proactive MANET routing protocols. As examples and to evaluate performance, the technique has been applied to the Dynamic Source Routing (DSR) protocol, a reactive routing protocol, and the Optimized Link State Routing (OLSR) protocol, a proactive routing protocol. Simulations have been carried out on large static and mobile networks. Results show improvements in network lifetime in static and certain mobile scenarios. Results also show better distribution of residual node energies at the end of simulations, which means that the scheme is balancing energy load more evenly across network nodes than the unmodified versions of DSR and OLSR. Average change in energy over time in the unmodified protocols show a steady increase with time, while the power-aware protocols show an increase in the beginning, then it levels for sometime before it starts to decrease. The power-aware scheme shows improvements in static and in coordinated mobility scenarios. In random mobility the power-aware protocols show no advantage over the unmodified protocols. / Ph. D.