Internet of Things (IoT) has gained significant importance due to its flexibility in integrating communication technologies and smart devices for the ease of service provisioning. IoT services rely on a heterogeneous cloud network for serving user demands ubiquitously. The service data management is a complex task in this heterogeneous environment due to random access and service compositions. In this article, a machine learning aided information management scheme is proposed for handling data to ensure uninterrupted user request service. The neural learning process gains control over service attributes and data response to abruptly assign resources to the incoming requests in the data plane. The learning process operates in the data plane, where requests and responses for service are instantaneous. This facilitates the smoothing of the learning process to decide upon the possible resources and more precise service delivery without duplication. The proposed data management scheme ensures less replication and minimum service response time irrespective of the request and device density.

中文翻译：

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服务集中式物联网中的机器学习辅助信息管理方案

物联网（IoT）因其在集成通信技术和智能设备方面的灵活性而变得非常重要，以简化服务供应。物联网服务依赖于异构云网络来普遍满足用户需求。在这种异构环境中，由于随机访问和服务组合，服务数据管理是一项复杂的任务。在本文中，提出了一种机器学习辅助信息管理方案，用于处理数据以确保不间断的用户请求服务。神经学习过程获得对服务属性和数据响应的控制，以将资源突然分配给数据平面中的传入请求。学习过程在数据平面中进行，其中服务的请求和响应是瞬时的。这有助于平滑学习过程，以决定可能的资源和更精确的服务交付而无需重复。所提出的数据管理方案可确保更少的复制和最少的服务响应时间，而与请求和设备密度无关。