A Chaotic Model‐Based Dynamic Resource Allocation and Adaptive Task Offloading With Lightweight Encryption in Multi‐Access Edge Computing

ABSTRACT Multi‐Access Edge Computing (MEC) provides a lot of computing with low latency within the nearby portion of the network. Nevertheless, current MEC frameworks have considerable resource management and offloading issues when there are dynamic and uncertain tasks. These are poor flexibility to live with time changes, heavy backhaul traffic during data communication, the use of a static approach of encryption, and a lack of integration with chaotic models on resource achievement. Due to the complexity of encrypting all resources, the developed framework employs a novel MEC paradigm of integrating the chaos modeling of systems to both adaptively distribute resources and dynamically change the task offloading as well as lightweight chaos‐based encryption. In this study, Multi‐Access Edge Computing (MEC) is used for fast task handling near the end user. The present MEC models still face resource and offloading problems when tasks are dynamic or uncertain. In this paper, a new chaotic model‐based method is developed for resource allocation and adaptive task offloading with lightweight encryption. The system applies chaotic sequence features such as initial sensitivity and randomness to decide task distribution among different servers. This helps in balancing the load, reducing delay, and lowering energy use. A secondary chaotic key generator is used to secure data transfer without heavy computation. Simulation results show that the proposed model achieves an average latency of 0.37 s and task processing time of 0.21 s, which are better than the standard MEC methods. Overall, the chaotic idea gives flexibility and safety for real‐time MEC operation.