We have developed an expert system comprising a self-aware framework for resource-efficient and accurate data transmission within a low-power lossy sensor network (LLN) deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision-making under dynamic environmental conditions and employs a multi-criteria decision-making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM-RPL with a standard RPL and a fuzzy-based RPL, where the results show that our framework is found to be better with gains of 13, 25 and 13%, respectively.

中文翻译：

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用于低功耗和有损室内传感器网络的专家系统

我们已经开发了一个专家系统，该系统包括一个自我感知的框架，可在部署于室内监控的低功耗有损传感器网络（LLN）内实现资源高效且准确的数据传输。我们获得了个人和团体的感知，这可以确保每个传感器对其资源，邻居和网络环境的感知。所提出的专家系统有助于在动态环境条件下进行决策，并采用多标准决策（MCDM）模型来确定通向汇聚节点的最佳路径的选择，同时要了解现有的网络环境。通过在Contiki Cooja模拟器中构建6LoWPAN网络来验证所提出的系统。MCDM用于为LLN（RPL）的IPv6路由协议生成自适应目标函数，并有助于对节点进行排名以选择最佳可用的相邻节点，而簇头通过其之间的数据相关性来确保数据准确性。相关成员。通过针对各种传感器分析数据包的传输速率，吞吐量和能耗，并通过将我们建议的MCDM-RPL与标准RPL和基于模糊的RPL进行比较，来评估网络性能，结果表明我们的框架更好分别增长13％，25％和13％。