Opportunism in the Internet of Things is the latest necessity arising in the IoT network communication development when we encompass a sustainable approach to routing. Such a requirement arises from the need for device-hop based networking to eliminate a dedicated end-to-end physical network for pervasive communication and promotes local data and computation sharing mechanisms as well as sustainably utilising the ubiquitous mobility aspect of such applications in the contemporary times. However, the functionality of opportunism runs into its own set of impasse; intermittent connectivity, limited resources, need for intelligence in routing, to name a few. Leveraging the ability of ML to tailor represent features to our advantage, this paper proposes a scheme to sectionalize the network topology based on node characteristics and employ grouping in intelligent transmission. The proposed scheme HiLSeR enables message routing using a combination of controlled-parameterized flooding and opportunistic sector-based decentralized transmission. Hierarchical learning, a multi-dimensional data conduct based soft clustering paradigm, is used for topology sectionalization and routing decision making. The performance of the proposed scheme is evaluated against contemporaneous RLPRoPH, GMMR, KNNR and Firefly PRoPHET protocols with ONE based simulations. The performance and sustainability performance is compared on various parameters such as Energy Unit per message, Dead node Percentage, Overhead Ratio, Average Latency and Success Ratio to show the enhanced performance. HiLSeR has an average successful delivery rate of 0.911 averaging out at 0.86775, in comparison to RLPRoPH, GMMR, KNNR, Firefly PRoPHET, the proposed scheme performs 12.85 %, 5.59 %, 61.29 %, 18.50 % and 88.33 % better respectively.

当我们采用可持续的路由方法时，物联网中的机会主义是物联网网络通信发展中出现的最新必需品。这样的需求源于对基于设备跳变的联网的需要，以消除用于普适通信的专用端到端物理网络，并促进本地数据和计算共享机制，以及在现代环境中可持续利用此类应用程序无处不在的移动性方面。次。但是，机会主义的功能陷入了自己的僵局。间歇性连接，有限的资源，路由智能需求等等。借助ML的优势，我们可以根据自己的需要定制代表功能，本文提出了一种基于节点特征对网络拓扑进行分段，并在智能传输中采用分组的方案。所提出的方案HiLSeR通过结合使用控制参数泛洪和基于机会扇区的分散传输来实现消息路由。分层学习是基于多维数据行为的软聚类范例，用于拓扑分段和路由决策。通过基于ONE的仿真，针对同期的RLPRoPH，GMMR，KNNR和Firefly PRoPHET协议评估了所提出方案的性能。在各种参数（例如，每条消息的能量单位，死节点百分比，开销比率，平均延迟和成功比率）上比较了性能和可持续性性能，以显示增强的性能。