MRI:Development of Energy-Efficient Embedded Systems for Wireless Sensor Networks

该项目开发用于传感器网络节点的智能节能嵌入式系统的实验原型，计划获得硬件设备，-构建节能嵌入式传感器系统;-表征其能耗参数;验证和评估新设计。目标是将正在进行的能效研究转化为先进传感器的开发，尽可能节省特征，同时保持轻、小、便宜和相对灵活。该调查涵盖的范围包括：节能电路、节能架构、节能嵌入式系统编译器、节能系统集成以及节能通信和网络。研究需要-比较模拟和数字电路实现，修改标准处理器架构，-开发动态存储器管理方案，集成组件，并-根据精心选择的处理和传输算法定制组件（选择用于整体效率）。特别是，传感器网络的大量应用（如事件检测，监控和数据收集）依赖于不可再生能源。传感器网络中使用的两种主要能量形式，即RF传输和处理，都表现出依赖于大量因素的支出模式，从底层到网络协议栈。显然，在传输所花费的能量和处理所花费的能量之间存在基本的权衡。例如，由传感器收集的数据可以经历复杂的压缩，这减少了传输比特的数量，因此以增加处理能量为代价节省了传输能量;因此，包括通信系统的传感器组件也必须在能量效率方面进行优化。因此，这项工作实验不同的网络通信场景。通过仿真和原型实现，研究了与通信和网络相关的两个特定问题：将通信功能（例如调制和编码）与硬件设计（例如，功率放大器）-通过实际物理测试评估传输与处理以及较长与较短跳的功耗-为了计算能耗权衡，测量和显示设备是必要的。具体的研究项目包括-节能电路设计;微控制器架构;存储器层次结构; -组件集成和通信协议。更广泛的影响：该项目应该有一个巨大的教育影响。在培训下一代研究人员的同时，这项工作为周期级仿真提供了一个免费的工具，以准确估计设计的能耗。部署长寿命传感器网络的必要性是有据可查的，并直接影响到商业和军事部门。存在显著降低能耗的机会。

This project, developing experimental prototypes of intelligent energy-efficient embedded systems for use in nodes of sensor networks, plans to acquire the hardware equipment that -Builds energy efficient embedded sensor systems;-Characterizes their energy consumption parameters; and-Verifies and evaluates new designs.The goal is to translate ongoing research on energy efficiency to development of advanced sensors that incorporates as many energy-saving features as possible while at the same time remain light, small, inexpensive, and relatively flexible. The investigation covers the spectrum: energy-efficient circuitry, energy-aware architectures, energy-aware embedded systems compilers, energy-efficient system integration, and energy-efficient communication and networking. The research needs to-Compare analog and digital circuit implementations, Modify standard processor architectures,-Develop dynamic memory management schemes, Integrate the components, and-Tailor those (chosen for overall efficiency) to carefully chosen algorithms for processing and transmissionVirtually all wireless networks are crucially dependent on portable finite energy sources. In particular, sensor networks for a large number of applications (such as event detection, monitoring, and data collection) depend on non-renewable energy sources. Both dominant forms of energy used in sensor networks, RF Transmission and Processing, exhibit expenditure modes that depend on a large number of factors, from the bottom layer up to the network protocol stack. Clearly, there is a fundamental tradeoff between energy spent at transmission and energy spent for processing. For example, data collected by sensors can undergo complex compression that reduces the number of transmitted bits and hence saves transmission energy at the expense of increases processing energy; thus the component of the sensor that includes the communication system must also be optimized with respect to energy efficiency. Hence, this work experiments with different scenarios of network communication. Through both simulation and prototype implementation, two particular issues related to communication and networking are studied: To-Integrate communication functions such as modulation and coding with hardware design (e.g., power amplifier) -Evaluate power consumption of transmission vs. processing and longer vs. shorter hops with actual physical tests-To calculate the energy consumption tradeoffs, measurements and display equipment are necessary. Specific research projects include-Energy-efficient circuit design; Micro-controller architectures; Memory hierarchies; -Component integration, and Communication protocols.Broader Impact: The project should have a huge educational impact. While training the next generation of researchers, this work contributes a freely available tool for cycle-level simulation to accurately estimate energy consumption of the design. The need for deployment of long-lived sensor networks is well documented and affects directly the commercial and military sectors. An opportunity exists for significant reductions in energy consumption.