## 2 Related Work  
Several real-time applications have been based on IoT sensors to develop an eHealth system to maintain medical data using wireless paradigms [20; 21; 22]. The eHealth systems are composed of many medical sensors to monitor the patient's physical state regarding different health data such as blood pressure, oxygen level, heartbeat, temperature, etc. The obtained readings of health data are further forwarded to medical experts for disease diagnosis and treatment. IoT-based health data are also stored on cloud machines to improve applications' scalability with efficient resource management [23; 24; 25; 26]. In [27], the authors addressed the various security tasks of big data and proposed an efficient and secure big data storage system for cloud computing. The proposed solution offers a resilient encryption scheme. The analysis of formal security proofs shows that the proposed scheme can ensure users' data privacy even if the partial key is leaked in cloud computing. The performance comparisons illustrate the operability of the proposed solution for big data security in cloud computing. In [28], the authors proposed a secure leakage resilient s-health system, which aims to ensure safe data transmission in the medical field in the presence of data breaches and network attacks. It is secured against chosen plaintext attacks under the standard model using decisional linear and the Diffie-Hellman exponent assumptions. The large-scale concurrent data anonymous batch verification scheme [29] for mobile healthcare crowdsensing is proposed. It is based on an improved certificate-less aggregate signature and provides authentication for sensing bio-information at once in a confidential manner. It offers batch-wise data verification while hiding the actual identity of participants. The performance evaluation demonstrates that the proposed scheme is more highly efficient for mobile healthcare crowd sensing than other solutions. A secure demand-side management (DSM) engine is proposed [30] based on machine learning for an IoT-enabled grid. It provides energy conservation using priorities, and a specific resilient model is proposed to cope with intrusions in the smart grid. The resilient agent identifies false objects by using a machine learning classifier, and the interface of controlling agents is offered for energy optimization. The simulation-based experiments have been performed and illustrate that the proposed scheme is less vulnerable to intrusions with improved power utilization of the smart grid. An anonymous-based user authentication scheme [31] is proposed for e-health applications using wireless medical sensor networks. It aims to address the security issues and decreases the communication overhead of the constraints-oriented applications. It utilizes improved elliptic curve cryptography and offers efficient performance against password guessing and smart card lost/stolen verifier attacks. The proposed solution offers sufficient security along with strong authentication and low computational cost. The authors in [32] proposed a secure and efficient data delivery protocol to decrease the delay ratio and protect against malicious attacks on wireless signals. It is composed of three custom algorithms and the fog gateway association algorithm aids in improving the security and efficiency between the wireless body area network and the remote resources. The experimental results showed its improved evaluation compared to other solutions. A tractable analysis framework is proposed [33] to test and analyze the reliability, security, and secrecy energy efficiency (SEE) performance for wireless networks using mobile sink nodes. It uses the threshold-based access scheme and a multi-antenna technique. The proposed work assumes both the line-of-sight and non-line-of-sight paths with Rayleigh fading for the air-to-ground channel model. The simulation-based results revealed the better performance of the proposed work with mobile sinks and maximized SEE.