Sustainable energy efficient networking models are needed to satisfy the increasing demands of the information and communication technologies (ICT) applications like healthcare, smart cities, education, and so on. The futuristic sustainable computing solutions in e-healthcare applications are based on the Internet of Things (IoT) and cloud computing platform, has offered numerous features and real time services. Several studies revealed that the amount of energy spent on transmitting data from IoT devices to a cloud server is considerably high and resulted in rapid energy depletion. In this view, this paper presents an Energy Efficient Particle Swarm Optimization (PSO) based Clustering (EEPSOC) technique for the effective selection of cluster heads (CHs) among diverse IoT devices. The IoT devices used for sensing healthcare data are grouped into a form of clusters and a CH will be elected by the use of EEPSOC. The elected CH will forward the data to the cloud server. Then, the CH is responsible for transmitting data of the IoT devices to the cloud server through fog devices. Next to that, an artificial neural network (ANN) based classification model is applied to diagnose the healthcare data in the cloud server to identify the severity of the diseases. For experimentation, a systematic student perspective healthcare data is produced utilizing UCI dataset and medicinal gadgets to foresee the diverse student levels of disease severity. A detailed comparative analysis is carried out and the simulation outcome ensured the goodness of the EEPSOC-ANN model over the compared methods under various aspects.

需要可持续的节能网络模型来满足医疗保健，智慧城市，教育等信息和通信技术（ICT）应用不断增长的需求。电子医疗应用中的未来可持续计算解决方案基于物联网（IoT）和云计算平台，提供了众多功能和实时服务。多项研究表明，从物联网设备向云服务器传输数据所花费的能源相当高，导致能源快速消耗。鉴于此，本文提出了一种基于能效粒子群优化（PSO）的聚类（EEPSOC）技术，用于在各种IoT设备之间有效选择簇头（CH）。用于感测医疗保健数据的IoT设备被分组为集群，并且将通过使用EEPSOC来选择CH。当选CH将数据转发到云服务器。然后，CH负责通过雾设备将IoT设备的数据传输到云服务器。紧接着，基于人工神经网络（ANN）的分类模型被应用于诊断云服务器中的医疗数据，以识别疾病的严重性。为了进行实验，利用UCI数据集和药用工具产生了系统的学生视角医疗数据，以预测学生疾病严重程度的不同程度。进行了详细的比较分析，仿真结果确保了EEPSOC-ANN模型在各个方面的比较方法的优越性。