In this paper, GSA-based Exigency Routing (GSA-ER) and CSA-based Near Exigency Relaying (CSA-NER) models have been proposed that support Multiple SS-based Optimized Cross-layer Routing (MB-OCR) framework. The GSA-ER model uses channel statistics, delivery estimates, cluster population, and energy parameters to shortlist an optimized multi-hop path to the static Base-Station (SS) held responsible for critical data-delivery within WSN. The CSA-NER model defines an optimized route for the mobile-sink (MS) to gather near-exigency support data within the network. With GSA-ER and CSA-NER models employed, the MB-OCR-configured WSN can adhere to the critical data deadlines while also achieve reliability under any unanticipated situation. MB-OCR framework further alleviates the hot-spot problem. As evident from the simulation analysis, MB-OCR outperforms the MS sojourn path based existing frameworks. Furthermore, MB-OCR performance has been investigated for varying critical scenarios to limelight the essence of employing MS and SS in WSN.

本文提出了基于GSA的紧急路由（GSA-ER）和基于CSA的近紧急中继（CSA-NER）模型，它们支持基于多个SS的优化跨层路由（MB-OCR）框架。GSA-ER模型使用信道统计信息，传递估计值，集群数量和能量参数来筛选出通往负责无线传感器网络内关键数据传递的静态基站（SS）的优化多跳路径。CSA-NER模型为移动接收器（MS）定义了一条优化的路由，以收集网络内的紧急呼叫支持数据。通过使用GSA-ER和CSA-NER模型，配置了MB-OCR的WSN可以遵守关键数据的截止日期，同时在任何意外情况下也可以实现可靠性。MB-OCR框架进一步缓解了热点问题。从仿真分析中可以明显看出，MB-OCR优于基于MS sojourn路径的现有框架。此外，已经针对各种关键场景研究了MB-OCR性能，以突出在WSN中采用MS和SS的本质。