Power Optimization in Wireless Sensor Network Using VLSI Technique on FPGA Platform

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Saranya Leelakrishnan, Arvind Chakrapani
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Abstract

Nowadays, the demand for high-performance wireless sensor networks (WSN) is increasing, and its power requirement has threatened the survival of WSN. The routing methods cannot optimize power consumption. To improve the power consumption, VLSI based power optimization technology is proposed in this article. Different elements in WSN, such as sensor nodes, modulation schemes, and package data transmission, influence energy usage. Following a WSN power study, it was discovered that lowering the energy usage of sensor networks is critical in WSN. In this manuscript, a power optimization model for wireless sensor networks (POM-WSN) is proposed. The proposed system shows how to build and execute a power-saving strategy for WSNs using a customized collaborative unit with parallel processing capabilities on FPGA (Field Programmable Gate Array) and a smart power component. The customizable cooperation unit focuses on applying specialized hardware to customize Operating System speed and transfer it to a soft intel core. This device decreases the OS (Operating System) central processing unit (CPU) overhead associated with installing processor-based IoT (Internet of Things) devices. The smart power unit controls the soft CPU’s clock and physical peripherals, putting them in the right state depending on the hardware requirements of the program (tasks) being executed. Furthermore, by taking the command signal from a collaborative custom unit, it is necessary to adjust the amplitude and current. The efficiency and energy usage of the FPGA-based energy saver approach for sensor nodes are compared to the energy usage of processor-based WSN nodes implementations. Using FPGA programmable architecture, the research seeks to build effective power-saving approaches for WSNs.

Abstract Image

在 FPGA 平台上利用 VLSI 技术优化无线传感器网络的功率
如今,对高性能无线传感器网络(WSN)的需求与日俱增,其功耗要求已威胁到 WSN 的生存。路由方法无法优化功耗。为了改善功耗,本文提出了基于 VLSI 的功耗优化技术。WSN 中的不同要素,如传感器节点、调制方案和数据包传输,都会影响能量的使用。在对 WSN 功耗进行研究后发现,降低传感器网络的能耗对 WSN 至关重要。本手稿提出了无线传感器网络的功率优化模型(POM-WSN)。所提出的系统展示了如何利用 FPGA(现场可编程门阵列)上具有并行处理能力的定制合作单元和智能电源组件,为 WSN 建立和执行省电策略。可定制的合作单元侧重于应用专用硬件来定制操作系统的速度,并将其传输到软 intel 内核。该设备降低了与安装基于处理器的物联网(IoT)设备相关的操作系统(OS)中央处理器(CPU)开销。智能电源装置控制软 CPU 的时钟和物理外设,根据正在执行的程序(任务)的硬件要求,将它们置于正确的状态。此外,通过接收协作定制单元的指令信号,有必要调整振幅和电流。基于 FPGA 的传感器节点节能方法的效率和能耗与基于处理器的 WSN 节点实现方法的能耗进行了比较。这项研究利用 FPGA 可编程架构,力求为 WSN 建立有效的省电方法。
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来源期刊
Neural Processing Letters
Neural Processing Letters 工程技术-计算机:人工智能
CiteScore
4.90
自引率
12.90%
发文量
392
审稿时长
2.8 months
期刊介绍: Neural Processing Letters is an international journal publishing research results and innovative ideas on all aspects of artificial neural networks. Coverage includes theoretical developments, biological models, new formal modes, learning, applications, software and hardware developments, and prospective researches. The journal promotes fast exchange of information in the community of neural network researchers and users. The resurgence of interest in the field of artificial neural networks since the beginning of the 1980s is coupled to tremendous research activity in specialized or multidisciplinary groups. Research, however, is not possible without good communication between people and the exchange of information, especially in a field covering such different areas; fast communication is also a key aspect, and this is the reason for Neural Processing Letters
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