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Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy Framework

机译:基于ARSy框架的无线传感器网络资源优化技术和安全级别

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摘要

Wireless Sensor Networks (WSNs) with limited battery, central processing units (CPUs), and memory resources are a widely implemented technology for early warning detection systems. The main advantage of WSNs is their ability to be deployed in areas that are difficult to access by humans. In such areas, regular maintenance may be impossible; therefore, WSN devices must utilize their limited resources to operate for as long as possible, but longer operations require maintenance. One method of maintenance is to apply a resource adaptation policy when a system reaches a critical threshold. This study discusses the application of a security level adaptation model, such as an ARSy Framework, for using resources more efficiently. A single node comprising a Raspberry Pi 3 Model B and a DS18B20 temperature sensor were tested in a laboratory under normal and stressful conditions. The result shows that under normal conditions, the system operates approximately three times longer than under stressful conditions. Maintaining the stability of the resources also enables the security level of a network’s data output to stay at a high or medium level.
机译:电池电量有限,中央处理器(CPU)和内存资源有限的无线传感器网络(WSN)是用于预警检测系统的广泛实施的技术。无线传感器网络的主要优势在于它们能够部署在人类难以接近的区域。在此类区域,可能无法进行定期维护;因此,WSN设备必须尽可能利用其有限的资源进行操作,但是更长的操作需要维护。一种维护方法是在系统达到临界阈值时应用资源自适应策略。这项研究讨论了安全级别适应模型(例如ARSy框架)的应用,以更有效地利用资源。在正常和压力条件下,在实验室中测试了包含Raspberry Pi 3 Model B和DS18B20温度传感器的单个节点。结果表明,在正常条件下,该系统的运行时间比压力条件下的运行时间长约三倍。维持资源的稳定性还可以使网络数据输出的安全级别保持在较高或中等级别。

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