感知扩散:无线传感器网络的半整体路由协议

Kamil Samara, H. Hosseini
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引用次数: 5

摘要

由于无线传感器网络节点的能量和硬件能力的限制,路由在无线传感器网络中是一项具有挑战性的任务。这一挑战促使研究人员开发满足无线传感器网络需求的路由协议。主要设计目标是传输可靠、低能耗和延长网络寿命。在wsn中,路由是基于相邻节点之间的本地信息。路由决策是在本地做出的;每个节点将选择下一跳,而不知道路径上的其他节点。尽管对网络的全面了解会产生更好的路由,但由于内存限制和收集网络中所有节点所需数据所需的高流量,这在wsn中是不可行的。为了克服这一缺点,本文提出了一种感知扩散路由协议。有意识的扩散遵循半整体方法,通过收集有关可用路径的数据,并使用这些数据通过机器学习强制执行更健康的路径。通过添加一个称为数据收集阶段的新阶段来完成数据收集。在此阶段,协议设计者可以确定收集哪些参数,然后根据某些标准使用这些参数来强制执行最佳路径。在这个范例的实现中,我们收集路径上的总能量、路径上的最低能级和跳数。同样,收集的数据是特定于设计器和应用程序的。收集到的数据将用于比较使用非增量学习的可用路径,结果将倾向于满足设计者标准的路径。在我们的例子中,更健康和更短的路径是首选,这将导致更少的功耗,更高的交付率和更长的网络寿命,因为更健康和更少的节点将完成工作。
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Aware Diffusion: A Semi-Holistic Routing Protocol for Wireless Sensor Networks
Routing is a challenging task in Wireless Sensor Networks (WSNs) due to the limitation in energy and hardware capabilities in WSN nodes. This challenge prompted researchers to develop routing protocols that satisfy WSNs needs. The main design objectives are reliable delivery, low energy consumption, and prolonging network lifetime. In WSNs, routing is based on local information among neighboring nodes. Routing decisions are made locally; each node will select the next hop without any clue about the other nodes on the path. Although a full knowledge about the network yields better routing, that is not feasible in WSNs due to memory limitation and to the high traffic needed to collect the needed data about all the nodes in the network. As an effort to try to overcome this disadvantage, we are proposing in this paper aware diffusion routing protocol. Aware diffusion follows a semi-holistic approach by collecting data about the available paths and uses these data to enforce healthier paths using machine learning. The data gathering is done by adding a new stage called data collection stage. In this stage, the protocol designer can determine which parameters to collect then use these parameters in enforcing the best path according to certain criteria. In our implementation of this paradigm, we are collecting total energy on the path, lowest energy level on the path, and hop count. Again, the data collected is designer and application specific. The collected data will be used to compare available paths using non-incremental learning, and the outcome will be preferring paths that meet the designer criteria. In our case, healthier and shorter paths are preferred, which will result in less power consumption, higher delivery rate, and longer network life since healthier and fewer nodes will be doing the work.
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