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Summary: Internet of Things (IoT) is steadily growing in support of current and projected real‐time distributed Internet applications in civilian and military applications, while Cloud Computing has the ability to meet the performance expectations of these applications. In this paper, we present the implementation of logistics management applications relying on cooperative resources with optimized performances. To dynamically incorporate smart manufacturing objects into logistics management IoT applications within a ubiquitous environment, task scheduling must be provided for resource allocation in an optimized way. Within such environment, we propose a task scheduling algorithm based on a robust Canonical Particle Swarm Optimization (CPSO) algorithm to solve the problem of resource allocation and management in both homogeneous and heterogeneous IoT Cloud Computing. Our objective is to satisfy the Makespan by performing optimal task scheduling while considering different policies of incoming tasks. Performance evaluation from simulation experiments reveals that optimizing the Makespan can be significantly improved by Longest Processing Time (LPT), Shortest Processing Time (SPT), Earliest Computation Time (ECT), Earliest Starting Time (EST), Earliest Deadline First (EDF), and Earliest Duedate (EDD) using our CPSO algorithm as compared with traditional list task scheduling algorithms. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is dramatically growing in support of the recent revolutionary cloud‐based survivability applications. It has to meet the performance expectations for these applications in real‐time while optimizing the available cloud resources. In this paper, we propose a cooperative resource scheduling in energy‐constrained applications for a reliable and fault‐tolerant performance. We present a task scheduling algorithm based on robust canonical particle swarm optimization (CPSO) and fully informed particle swarm optimization (FIPS) algorithm to solve the problem of resource allocation in both homogeneous and heterogeneous cloud‐based IoT applications. Our objective is to satisfy the quality of service in terms of throughput and delay by performing optimal task scheduling while considering the different experienced data traffic categories. Our results show that throughput and delay can be significantly improved while using the FIPS approach in comparison to the CPSO optimization algorithm. [ABSTRACT FROM AUTHOR]