A score function for state of charge profiles for rechargeable batteries

We propose a new score function to compare and evaluate the relative impact of state-of-charge profiles on overall battery lifetime. Our score function, based on on a discrete Fourier transform of the state-of-charge profile, formalizes and generalizes earlier ideas found in the literature, and can form an important help in optimizing overall life time for battery powered systems. In this paper we introduce and illustrate the method, and discuss its merits as well as open issues and related literature

Enabling the Integration of Sustainable Design Methodological Frameworks and Computational Life Cycle Assessment Tools into Product Development Practice

Environmental sustainability has gained critical importance in product development (PD) due to increased regulation, market competition, and consumer awareness, leading companies to set ambitious climate targets . To meet these goals, PD practitioners (engineers and designers) are often left to adapt their practices to reduce the impacts of the products they manufacture. Literature review and interviews with practitioners show that they highly valued using quantitative life cycle assessment (LCA) results to inform decision making. LCA is a technique to measure the environmental impacts across various stages of a product life cycle. Existing LCA software tools, however, are designed for dedicated experts to use at the end of PD using detailed product information. This creates the “ecodesign paradox”, a tension between opportunity for change in the early-stages of PD and availability of data in later stages to make reliable decisions. Further, my research identified that novice users of LCA face additional barriers including: cumbersome user interfaces, unfamiliar terminology, and complicated information visualization. To address these challenges, I developed a tool called EcoSketch for use during early-stage PD by novice users. Practitioners, however, also struggle with translating environmental impact information into actionable design decisions. Hence, I co-created methodological frameworks of sustainable design strategies with industry partners: Synapse Product Development Inc. and Stanley Black and Decker Inc. Despite contextual differences, a key commonality was that practitioners at both firms sought “structured” and “data-driven\u27\u27 processes for sustainable design. Through multiple, extended internships, I also identified important drivers and barriers to sustainable design integration. Overall, my research demonstrates that co-creation improves receptivity, long-term adoption, and produces tangible improvements to sustainable outcomes in practice. In summary, my research pursues two key pathways to enable sustainable design integration: Developing human-centered life cycle assessment (LCA) tools that are designed for decision-making during the early stages of PD. Creating methodological frameworks to support the application of appropriate sustainable design strategies in PD practice. This thesis elaborates on my proposed coupling of robust frameworks with human-centered LCA tools, which I argue together comprise a transformative solution for industry professionals to effectively integrate sustainability considerations in their product development practices

An Energy Aware and Secure MAC Protocol for Tackling Denial of Sleep Attacks in Wireless Sensor Networks

Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. Indeed,the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection c ould give room for energy drain attacks such as denial of sleep attacks which have a higher negative impact on the life span ( of the sensors than the presence of security features. This thesis, therefore, focuses on tackling denial of sleep attacks from two perspectives A security perspective and an energy efficiency perspective. The security perspective involves evaluating and ranking a number of security based techniques to curbing denial of sleep attacks. The energy efficiency perspective, on the other hand, involves exploring duty cycling and simulating three Media Access Control ( protocols Sensor MAC, Timeout MAC andTunableMAC under different network sizes and measuring different parameters such as the Received Signal Strength RSSI) and Link Quality Indicator ( Transmit power, throughput and energy efficiency Duty cycling happens to be one of the major techniques for conserving energy in wireless sensor networks and this research aims to answer questions with regards to the effect of duty cycles on the energy efficiency as well as the throughput of three duty cycle protocols Sensor MAC ( Timeout MAC ( and TunableMAC in addition to creating a novel MAC protocol that is also more resilient to denial of sleep a ttacks than existing protocols. The main contributions to knowledge from this thesis are the developed framework used for evaluation of existing denial of sleep attack solutions and the algorithms which fuel the other contribution to knowledge a newly developed protocol tested on the Castalia Simulator on the OMNET++ platform. The new protocol has been compared with existing protocols and has been found to have significant improvement in energy efficiency and also better resilience to denial of sleep at tacks Part of this research has been published Two conference publications in IEEE Explore and one workshop paper

Comparison of Two Optimal Control Strategies for a Grid Independent Photovoltaic System

This paper presents two optimal control strategies for a grid independent photovoltaic system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The first strategy is based on Action Dependent Heuristic Dynamic Programming (ADHDP), a model-free adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. ADHDP critic network is used in a PV system simulation study to train an action neural network (optimal neurocontroller) to provide optimal control for varying PV system output energy and loadings. The second optimal control strategy is based on a fuzzy logic controller with its membership functions optimized using the particle swarm optimization. The emphasis of the optimal controllers is primarily to supply the critical base load at all times, thus requiring sufficient stored energy during times of less or no solar insolation. Simulation results are presented to compare the performance of the proposed optimal controllers with the conventional priority control scheme. Results show that the ADHDP based controller performs better than the optimized fuzzy controller, and the optimized fuzzy controller performs better than the standard PV-priority controller

Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices

This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and when it is actually being used. To achieve classification accuracy and power consumption preservation a newly released algorithm called MiniRocket was employed. Known for its accuracy, scalability, and fast training for time-series classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices. The paper demonstrates the portability and performance of MiniRocket on a resource-constrained, ultra-low power sensor node for floating-point and fixed-point arithmetic, matching up to 1% of the floating-point accuracy. The hyperparameters of the algorithm have been optimized for the task at hand to find a Pareto point that balances memory usage, accuracy and energy consumption. For the classification problem, we rely on an accelerometer as the sole sensor source, and BLE for data transmission. Extensive real-world construction data, using 16 different power tools, were collected, labeled, and used to validate the algorithm's performance directly embedded in the IoT device. Experimental results demonstrate that the proposed solution achieves an accuracy of 96.9% in distinguishing between real usage status and other motion statuses while consuming only 7kB of flash and 3kB of RAM. The final application exhibits an average current consumption of less than 15{\mu}W for the whole system, resulting in battery life performance ranging from 3 to 9 years depending on the battery capacity (250-500mAh) and the number of power tool usage hours (100-1500h)

Reconfigurable Antenna Systems: Platform implementation and low-power matters

Antennas are a necessary and often critical component of all wireless systems, of which they share the ever-increasing complexity and the challenges of present and emerging trends. 5G, massive low-orbit satellite architectures (e.g. OneWeb), industry 4.0, Internet of Things (IoT), satcom on-the-move, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, all call for highly flexible systems, and antenna reconfigurability is an enabling part of these advances. The terminal segment is particularly crucial in this sense, encompassing both very compact antennas or low-profile antennas, all with various adaptability/reconfigurability requirements. This thesis work has dealt with hardware implementation issues of Radio Frequency (RF) antenna reconfigurability, and in particular with low-power General Purpose Platforms (GPP); the work has encompassed Software Defined Radio (SDR) implementation, as well as embedded low-power platforms (in particular on STM32 Nucleo family of micro-controller). The hardware-software platform work has been complemented with design and fabrication of reconfigurable antennas in standard technology, and the resulting systems tested. The selected antenna technology was antenna array with continuously steerable beam, controlled by voltage-driven phase shifting circuits. Applications included notably Wireless Sensor Network (WSN) deployed in the Italian scientific mission in Antarctica, in a traffic-monitoring case study (EU H2020 project), and into an innovative Global Navigation Satellite Systems (GNSS) antenna concept (patent application submitted). The SDR implementation focused on a low-cost and low-power Software-defined radio open-source platform with IEEE 802.11 a/g/p wireless communication capability. In a second embodiment, the flexibility of the SDR paradigm has been traded off to avoid the power consumption associated to the relevant operating system. Application field of reconfigurable antenna is, however, not limited to a better management of the energy consumption. The analysis has also been extended to satellites positioning application. A novel beamforming method has presented demonstrating improvements in the quality of signals received from satellites. Regarding those who deal with positioning algorithms, this advancement help improving precision on the estimated position

An Investigation of Life Cycle Sustainability Implications of Emerging Heavy-Duty Truck Technologies in the Age of Autonomy

Heavy-duty trucks (HDTs) play a central role in U.S. freight transportation, carrying most of the goods across the country. The projected increase in freight activity (e.g. truck-miles-traveled) raises concerns regarding the potential sustainability impacts of the U.S. freight industry, marking HDTs as an ideal domain for improving the sustainability performance of U.S. freight transportation. However, the transition to sustainable trucking is a challenging task, for which multiple sustainability objectives must be considered and addressed under a variety of emerging HDT technologies while composing a sustainable HDT fleet. To gain insights into the sustainability implications of emerging HDT technologies as well as how they can be adopted by freight organizations, given their implications, this research employed an integrated approach composed of methods and techniques, grounded in sustainability science, operations research, and statistical learning theory, to provide a scientific means with public and private organizations to increase the effectiveness of policies and strategies. The research has contributed to the scientific body of knowledge in three useful ways; (1) by comprehensively analyzing HDT electrification based on regional differences in power generation practices and price forecasts, (2) by conducting the first life cycle sustainability assessment (LCSA) on HDT automation and electrification, and (3) providing a case study of an unsupervised machine learning application for sustainability science. Consequently, the research has found that, given the transformation of the U.S. energy system towards renewables, automation and electrification of HDTs offer significant potential for improving the sustainability performance of these vehicles, especially in terms of global warming potential, life cycle costs, gross domestic product, import independence, and income generation. The research has also found that, under the prevailing techno-economic circumstances and except for energy security reasons, natural gas as a transportation fuel option for freight trucks is by almost no means a viable alternative to diesel

Determining Priority Value of Processes Based on Usage History

Generally, the present disclosure is directed to determining optimal priority values for one or more processes in a computing system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an optimal priority value for a process based on system data and/or process data