With the rapid growth of mobile applications, large volumes of private data are continuously generated on mobile devices and increasingly outsourced to cloud storage services. Due to the limited computational and energy resources of mobile devices, enabling efficient and secure periodic auditing of cloud stored data remains a challenging task. Existing cloud data auditing schemes, such as Provable Data Possession and Proof of Retrievability, require computation-intensive operations that are impractical for resource-constrained mobile environments. To address this challenge, we propose MIDAS, a mobile-friendly data auditing scheme that supports secure outsourcing of auditing metadata with balanced resource utilization. MIDAS adopts a two-phase auditing metadata structure, in which computation intensive operations are offloaded to edge nodes, while mobile devices only perform lightweight processing. In addition, identity information of edge nodes is embedded into the auditing metadata to support traceability and accountability. To further improve scalability, we design a resource scheduling model for Multi MU and Multi-EN scenarios and propose the proportional load balanced scheduling algorithm to achieve efficient and load balanced metadata generation. The security of MIDAS is rigorously proven by reducing it to the discrete logarithm problem in the RSA quadratic residue subgroup within the random oracle model. Experimental results in a real-world environment demonstrate that MIDAS significantly reduces computational overhead on mobile devices, maintains acceptable communication cost, and achieves superior scalability compared with existing schemes.

The wireless sensor health monitoring system integrates biomedical engineering technology and wireless sensor network, which can monitor human physiological information in real time. The monitoring system takes the network technology as the platform and sends the status information of the monitoring object to the coordinator in real time through a mobile-edge computing server to realize data collection. A mathematical model of wireless sensor health monitoring network energy consumption optimization is established in this article, which takes into account the constraints of the actual network system, such as computing task offloading, power, bit error rate, and actual delay. To improve the performance of the method, ensure the complete decline of the function value and the convergence of the iteration matrix, an energy optimal resource allocation algorithm for health monitoring system based on improved quasi-Newton method BFGS is proposed. It reduces the computational complexity during the iteration process, maintains superlinear convergence, and has strong numerical stability in calculations. Simulation results show that the proposed resource allocation algorithm effectively reduces the time delay to process computing tasks and energy consumption of health monitoring sensor nodes, which enables the health monitoring system to process large-scale medical data and information more effectively and timely.