Vol.:(0123456789) 1 3 Mobile Networks and Applications https://doi.org/10.1007/s11036-023-02115-9 Privacy Preserving Blockchain with Energy Aware Clustering Scheme for IoT Healthcare Systems José Escorcia‑Gutierrez 1,2,6  · Romany F. Mansour 3  · Esmeide Leal 4  · Jair Villanueva 5  · Javier Jimenez‑Cabas 6  · Carlos Soto 7  · Roosvel Soto‑Díaz 8 Accepted: 8 June 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 Abstract Due to advancements in information technology, the healthcare sector becomes benefcial and provides distinct methods of managing medical data and enhancing the quality of medical services. The advanced e-healthcare applications are mainly based on the Internet of Things (IoT) and cloud computing platforms. In IoT enabled healthcare sector, the IoT devices usu- ally record the patient data and transfer it to the cloud for further processing. Energy efciency and security are treated as critical problems in designing IoT networks in the healthcare environment. As IoT devices are limited to energy, designing an efective technique to reduce energy utilization is needed. At the same time, secure transmission of medical data also poses a major challenging design issue. This paper presents a novel artifcial intelligence with a blockchain scheme for IoT healthcare systems named AIBS-IoTHS. The AIBS-IoTH model aims to achieve secure and energy-efcient data transmis- sion in IoT networks. The IoT devices are primarily used to collect patients’ medical data. The AIBS- IoTH model involves a metaheuristic-based modifed sunfower optimization-based clustering (MSFOC) technique to achieve energy efciency. Then, the blockchain empowered secure medical data transmission process is carried out for both inter-cluster and intra- cluster communication. At last, the Classifcation Enhancement Generative Adversarial Networks (CEGAN) model performs the diagnostic process on the secured medical data to determine the existence of the diseases. The design of MSFOC and CEGAN techniques shows the novelty of the work. An extensive experimental analysis of the benchmark dataset pointed out the superior performance of the proposed AIBS-IoTH model over the other compared methods. Keywords Artifcial intelligence · Healthcare system · Internet of Things · Energy efciency · Blockchain 1 Introduction Recently, the Internet of things (IoT) and its interrelated medical applications have been gradually developed and assisted as an efcient and efective system for the user in which the medical resources are manually accessible [1]. Remote Patient Management (RPM) was positioned by dif- ferent medical facilities such as arrhythmia detection, regu- larization of glucose level, oxygen maintenance, fall investi- gation, chemotherapy response, observation of the pregnant women, prominent signal observation using implantable sensors, among others [2]. Even though IoT is assisted with a massive quantity of resources, the comprehensive ability is lagging due to fault-tolerant, security, and inexistence of stability. In e-Health, the biological details of the patient are gathered using a medical IoT machine, and the obtained details are transmitted to Cloud/Edge unit, i.e., expended by the attackers and results in security problems. Lastly, the * José Escorcia-Gutierrez jose.escorcia23@gmail.com; jescorci56@cuc.edu.co 1 Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, Colombia 2 Biomedical Engineering Program, Corporación Universitaria Reformada, Barranquilla, Colombia 3 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt 4 Computer Engineering Program, Universidad Autónoma del Caribe, Barranquilla, Colombia 5 Mechatronics Engineering Program, Universidad Autónoma del Caribe, Barranquilla, Colombia 6 Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, Colombia 7 Mechanical Engineering Program, Universidad Autónoma del Caribe, Barranquilla, Colombia 8 Mechatronics Engineering Program, Universidad Simón Bolívar, Barranquilla, Colombia