To gain high energy efficiency and low latency, fog computing can be considered as a promising enabling technology for supporting future IoT networks. The proposed model was simulated in Network Simulator 3, and the outcomes were compared with those of prevailing methods for evaluating parameters, like accuracy, end-to-end delay, energy consumption, network lifetime, packet delivery ratio, and throughput. Hence, the new algorithm developed helps detect intruders through intelligent cross layer analysis with the Markov model. However, the main security issues in this article are addressed using the storage and search optimization approach. Every data communication network, like ad hoc mobile cloud computing, faces security and confidentiality issues. The hidden Markov model is used to maintain the data transition and distributions in the network. Moreover, delay, energy consumption, network lifetime, and link accomplishment are highly addressed by the proposed algorithm. First, the clustering process based on storage and search optimization was formulated for clustering and route maintenance in ad hoc mobile cloud computing networks. The proposed approaches were formed by cross-layer analysis based on intrusion detection methods. ![]() To address these weaknesses, which raise many concerns about privacy and security, this study formulated clustering-based storage and search optimization approaches using cross-layer analysis. These issues may affect several interconnected nodes in an ad hoc mobile cloud computing network. ![]() Given the issues above, route failure is prevalent in ad hoc mobile cloud computing networks, which increases energy consumption and delay and reduces stability. Ad hoc mobile cloud computing networks are affected by various issues, like delay, energy consumption, flexibility, infrastructure, network lifetime, security, stability, data transition, and link accomplishment.
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