This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability. Key features: Includes an embedded wireless sensor networks case study to help illustrate the modeling and optimization of distributed embedded systems. Provides an analysis of multi-core/many-core based embedded systems to explain the modeling and optimization of parallel embedded systems. Features an application metrics estimation model; Markov modeling for fault tolerance and analysis; and queueing theoretic modeling for performance evaluation. Discusses optimization approaches for distributed wireless sensor networks; high-performance and energy-efficient techniques at the architecture, middleware and software levels for parallel multicore-based embedded systems; and dynamic optimization methodologies. Highlights research challenges and future research directions. The book is primarily aimed at researchers in embedded systems; however, it will also serve as an invaluable reference to senior undergraduate and graduate students with an interest in embedded systems research. Table of Contents Part One: Overview Chapter 1: Introduction Chapter 2: Multicore-Based EWSNs—An Example of Parallel and Distributed Embedded Systems Part Two: Modeling Chapter 3: An Application Metrics Estimation Model for Embedded Wireless Sensor Networks Chapter 4: Modeling and Analysis of Fault Detection and Fault Tolerance in Embedded Wireless Sensor Networks Chapter 5: A Queueing Theoretic Approach for Performance Evaluation of Low-Power Multicore-Based Parallel Embedded Systems Part Three: Optimization Chapter 6: Optimization Approaches in Distributed Embedded Wireless Sensor Networks Chapter 7: High-Performance Energy-Efficient Multicore-Based Parallel Embedded Computing Chapter 8: An MDP-Based Dynamic Optimization Methodology for Embedded Wireless Sensor Networks Chapter 9: Online Algorithms for Dynamic Optimization of Embedded Wireless Sensor Networks Chapter 10: A Lightweight Dynamic Optimization Methodology for Embedded Wireless Sensor Networks Chapter 11: Parallelized Benchmark-Driven Performance Evaluation of Symmetric Multiprocessors and Tiled Multicore Architectures for Parallel Embedded Systems Chapter 12: High-Performance Optimizations on Tiled Manycore Embedded Systems: A Matrix Multiplication Case Study Chapter 13: Conclusions
Real-world issues are complex, so I seek to prepare myself with both advanced technical knowledge and practical experience. I see the MSCM program as my next step. I could fill my skill gap in optimizing complex systems. Courses such as analytic methods, industrial engineering, operation research, and systems engineering are highly technical and analytical. I believe these courses are essential for decision-based problem-solving and digital transformation. The opportunity in taking the Capstone Project would be a perfect start for my long-run goal of running a supply chain optimization solution provider company. By solving the supply chain problems of companies with quantitative modeling, I hope I can differentiate and summarize several solutions applicable to most companies.
Develop and analyze a mathematical model that will assist negotiators to respond to a fixed set of water supply and demand conditions. Use the model to inform dam operations: When the water level in Lake Mead is M and the water level in Lake Powell is P, how much water should be drawn from each lake to meet stated demands? If no additional water is supplied (from rainfall, etc.), and considering the demands as fixed, how long will it take before the demands are not met? How much additional water must be supplied over time to ensure that these fixed demands are met?
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