Smart Healthcare System with Light-Weighted Blockchain System and Deep Learning Techniques

Randeep Singh; Bilal Ahmed Mir; Lohith J J; Dhruva Sreenivasa Chakravarthi; Adel R Alharbi; Harish Kumar; Simon Karanja Hingaa

doi:10.1155/2022/1621258

Smart Healthcare System with Light-Weighted Blockchain System and Deep Learning Techniques

Comput Intell Neurosci. 2022 Apr 19:2022:1621258. doi: 10.1155/2022/1621258. eCollection 2022.

Authors

Randeep Singh¹, Bilal Ahmed Mir², Lohith J J³, Dhruva Sreenivasa Chakravarthi⁴, Adel R Alharbi⁵, Harish Kumar⁶, Simon Karanja Hingaa⁷

Affiliations

¹ Department of Computer Science & Engineering, IEC University, Baddi, Solan, Himachal Pradesh, India.
² Department of Mechanical and Intellectual Systems Engineering, University of Toyama, Japan.
³ Department of Computer Science and Engineering, B M S College of Engineering, Bengaluru, India.
⁴ KL Business School, Koneru Lakshmaiah Education Foundation Deemed to be University (KLEF), Vaddeswaram, Guntur District, Andhra Pradesh, India.
⁵ College of Computing and Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia.
⁶ Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia.
⁷ Department of Electrical and Electronic Engineering, Technical University of Mombasa, Mombasa, Kenya.

Abstract

A radio communication sensor system is a collection of sensor modules that are connected to one another through wireless communication. It is common for them to be battery-powered and responsive to a nearby controller, referred to as the base station. They are capable of doing basic computations and transferring information to the base station in most scenarios. They are also in charge of transporting data from distant nodes, putting a burden on nodes with limited resources, and contributing to the quick depletion of energy in these nodes in the process. Nodes in close proximity to the base station are responsible for more than only detecting and sending data to the base station; they are also responsible for transmitting data from faraway nodes. To reward nodes that perform well, a protocol known as the Improved Fuzzy Inspired Energy Effective Protocol (IFIEEP) employs three separate sorts of nodes in order to provide more energy to those who do not. It takes into account the remaining node energy, the node's proximity to the base station, the node's neighbor concentration, and the node's centrality in a cluster when determining node viability. All of these assumptions are founded on a shaky understanding of the situation. Adaptive clustering must be applied to the most viable nodes in order to identify cluster leaders and transmit data to the base station, in addition to disseminating data across the rest of the network, in order to achieve success. In addition, the research provides proper heterogeneity parameters, which describe, among other things, the number of nodes as well as the starting energy of each node. The percentage gain in-network lifetime when compared to current approaches is minor for smaller numbers of supernodes; however, the percentage gain in the area covered 12.89 percent and 100% when more significant numbers of super nodes are used. These improvements in stability, residual energy, and throughput are accomplished by combining these improvements while also taking into consideration the previously neglected energy-intensive sensing energy aspect. The protocol that has been presented is meant to be used in conjunction with applications that make use of blockchain technology.

MeSH terms

Blockchain*
Communication
Deep Learning*
Delivery of Health Care
Technology