A novel Privacy Oriented White Shark Encompassed hierarchical auto-associative polynomial Convolutional Neural NetwoRk (POWER) framework for task scheduling has been proposed. Initially, the Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN) for scheduling the healthcare task by considering the parameters. The HAP-CNN has been optimized using White Shark Optimization (WSO) for enhancing the accuracy in generating the schedule. The proposed task scheduling model is calculated based on several characteristics, including task migration, reaction time, transmission time, makespan, and cost. Recall, specificity, accuracy, precision, and F1 score were utilized to assess the proposed method’s efficacy. With the suggested model, 99.32% classification accuracy was attained. The proposed model enhanced the total accuracy by 2.29%, 1.07% and 7.37% better than Task Scheduling utilizing a multi-objective grey wolf optimizer (TSMGWO), Prioritized Sorted Task-Based Allocation (PSTBA), and Large-Scale Industrial Internet of Things asynchronous Advantage Actor Critic system (LsiA3CS) respectively

Recent studies have highlighted the importance of implementing clustering schemes in Wireless Body Area Networks (WBANs) to address challenges such as scalability, network topology changes, spectrum scarcity, and management. However, many existing approaches focus only on conventional performance metrics and overlook the integration of spectrum trading and efficient spectrum utilization. This paper proposes a novel clustering control scheme based on fuzzy logic and Nash equilibrium to enhance scalability, network stability, and resource management in WBANs. Our approach employs multi-criteria decision-making to optimize cluster head (CH) selection and routing strategies using reinforcement learning to achieve quality of service (QoS). Additionally, a secure lightweight Diffie-Hellman key exchange is used to protect data transmission. The proposed protocol outperforms existing protocols, including TAFLR, EQRSRL, and SEBA, in terms of throughput (3.2 kbps), packet delivery ratio (93%), delay (0.31 s), cluster efficiency (95%), and energy consumption (0.43 J).