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GINGER: a minimizing-effects reprogramming paradigm for distributed sensor networks

机译:姜:分布式传感器网络的最小化效果重新编程范例

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Reprogramming the applicative behaviour of distributed sensor networks is becoming a more and more important ability in real-working monitoring applications to include new functionalities or modify the existing ones. Several approaches for reprogramming have been presented in the literature suggesting efficient reprogramming mechanisms. The aim of this paper is to address a problem that received less attention in the scientific community: minimizing the reprogramming effects on the applications running at the networks nodes. This issue is of paramount importance in complex distributed monitoring systems, where the network's behaviour is characterized by several (possibly concurrent) applications running at nodes. This paper suggests a novel reprogramming paradigm, called GINGER, designed to minimize the reprogramming effects on the nodes of distributed sensor networks. Critical aspects, advantages and open points are deeply described and critically commented.
机译:重新编程分布式传感器网络的应用行为正在成为实际监控应用程序的越来越重要的能力,以包括新功能或修改现有功能。 在文献中提出了几种重新编程方法,提出了有效的重编程机制。 本文的目的是解决一个问题,在科学界中受到不那么关注的问题:最大限度地减少对在网络节点上运行的应用程序的重新编程效果。 此问题在复杂的分布式监视系统中是至关重要的,其中网络的行为是在节点上运行的几个(可能的并发)应用程序的特征。 本文建议新颖的重新编程范式,称为姜,旨在最大限度地减少分布式传感器网络节点的重新编程效果。 关键方面,优点和开放点深受描述和批判性评论。

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