A wireless body area network (WBAN) collects several tiny sensor nodes in the human body. They are intended to observe data constantly forward and perform as critical structures for remote healthcare monitoring and healing. These protect the preferred reliability and efficiency of data transmissions of individual body sensors. However, with the restricted sensor energy, unreliable link connection, and complex channel environment, the design of efficient routing in Multihop WBANs is demanding. This article, Efficient and Reliable Multihop Routing (ERMR) for IoT Healthcare Applications, is proposed. The main concept is to incorporate a fuzzy-logic system to handle multiple cross-layer input variables independently. This approach uses the fuzzy logic method to select the optimal route in the WBAN. The fuzzy input like sensor node distance, link strength, energy, and node quality of Service and produce the output is a reliable forwarder selection. Simulation results demonstrate the proposed approach increases the network throughput and minimizes the network delay in the WBAN.

As a result of the fast development of technology, healthcare systems have rapidly morphed into an all-pervasive environment that is replete with a variety of difficulties and possibilities. Threats and assaults with severe societal effects have been launched due to the increasing popularity of WBANs and their features and applications. The data is encrypted using a Ciphertext-Policy Attribute-Based Encryption algorithm and signed at the data sink in Secure and Efficient data transfer protocol, guaranteeing the data’s safety at all times. The computational cost and complexity to solve this issue string are both raised by this technique. Security code that the public may access In WBAN, cryptography allows Kerberos-based authentication. Public-key cryptography can be used to secure the initial authentication procedure. The procedure’s goal is to let the user safely send the required certificate (including the TGT and session key) to the sensor. The simulation results demonstrate that this approach improved the detection ratio, and minimized the false positive ratio compared to the baseline protocol in the network.