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In recent days, vehicles usage and speed are highly increased that leads to an increase in energy consumption, delay, and overhead in the network. In this paper, a novel trajectory is introduced to achieve maximum reliability namely trust and mobility-based evaluation for intelligent collaborative (TMIC)-UAVs assisted VANETs. Reactive multipath greedy routing protocol (RMGR) is the hybrid routing protocol and it is the combination of ad hoc on-demand multipath distance vector (AOMDV) with greedy geographic forwarding (GGF) which is used for routing in frequently changeable network topology. To protect the network from malfunctions, effective trust evaluation (ETE) is performed by calculating the direct trust and indirect trust. Finally, to achieve effective communication among the UAVs, hybrid optimization is performed which is the combination of the genetic algorithm (GA) and the crow swarm optimization (CSO) algorithm. For validation network simulator (NS3) is used and the results show that this approach achieves high energy efficiency, delivery ratio, and reduction in delay when compared with the earlier research. [ABSTRACT FROM AUTHOR]

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model. In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas. The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and other non-metaheuristic algorithms like Round Robbin (RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint. However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling. To address this issue, a multi-objective scheduling framework is proposed. In this paper, the framework performs dynamic workflow scheduling using universal unique identification- Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network. The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3). Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment. Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce makespan the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability. Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines. We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bitbrains datasets for comparing our proposed framework with existing works. Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO, PSO and RR has achieved better performance.