This research work proposes a method for managing, securing, and validating the health data distribution records using a genetic-based hashing algorithm in a decentralized environment. The reason behind choosing blockchain is to secure the transaction of health data and protect the data from manipulated fraudulent movement and corruption by the contributor of the chain or any individual. The path of this technology approach provides an efficient surveillance measure such as transparency of record, fraud immunity, and protection to tempering and sustaining the order of data. In medical research, this paper provides a genetic-based hashing algorithm for data security which has lower computational complexity, low space coverage, higher security and integrity, and a high avalanche effect. The simulation will show data records' validity, immunity, and integrity. The technique modified in this secure decentralized network is a cryptographic hashing algorithm for 512 bits. In this study genetic algorithm is used to generate a key that needs to be used in the encryption and decryption of medical data. A genetic algorithm is a metaheuristic approach that is inspired by the laws of genetics and is generally used to generate high-quality solutions for complex problems. Applications of genetic algorithms encourage the significance in many medical fields such as radiology, oncology, cardiology, endocrinology, surgery, oncology, and radiotherapy in health care management.

Wireless Sensor Networks (WSNs) are commonly used in various pervasive applications. Wireless communication is the fastest growing segment of the communication industry that has captured attention of the media and imagination of the public. The purpose of this study is to develop a Genetic algorithm that provides a proper deployment of sensors in order to optimize the coverage problem. The genetic algorithm optimizes the operational modes of sensors nodes for transmitting signals. In this paper we will present the optimum method for the mobile sensors nodes placement, so that transmission of signals and the field coverage can be improved. One of the critical issues of today’s world is to ensure that the quality of services requirement is at an acceptable level and genetic algorithm give the best solution for the above discussed problem. The algorithm has been implemented in MATLAB using its genetic algorithm tool box along with the custom code.