Efficient data uncertainty management for health industrial internet of things using machine learning

Abstract

Summary In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self‐governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real‐time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low‐cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e‐health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM‐ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real‐time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi‐objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning‐based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms.

Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

“…Moreover, the encrypted data is mapped with digital hashes to network edges as defined in Equation (6).…”

“…Currently, they are collaborating with computing devices to diagnosis different inflectional diseases and issues of public health such as typhoid, malaria, blood pressure, etc. [4][5][6]. These are biosensors that can collect health-related data from any implanted surgical device, wearable bands, mobile device, etc.…”

Risk assessment model of Internet of Things based on BP neural network algorithm

“…Information security risk management is the application of risk management theories and methods in information systems [5]. Through information security RBI, we can effectively recognize the risks faced by the assessed system and the severity of these risks, help managers fully and deeply understand the causes of risks, and thus select targeted risk measures to reduce and control risks [6]. The equipment risk analysis fully considers the possible factors and consequences of equipment failure.…”

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

“…The technology of WSN is broadly utilized in various applications, such as precision agriculture, healthcare, vehicle transportation, smart cities, etc. [ 4 , 5 , 6 ]. It is comprised of tiny and battery-powered nodes with limited memory and transmission power, and such constraints restrict the amount of computation in facilitating the network users [ 7 , 8 , 9 ].…”