The Internet of Things (IoT), which enables common objects to be intelligent and interactive, is considered the next evolution of the Internet. Its pervasiveness and abilities to collect and analyze data which can be converted into information have motivated a plethora of IoT applications. For the successful deployment and management of these applications, cloud computing techniques are indispensable since they provide high computational capabilities as well as large storage capacity. This paper aims at providing insights about the architecture, implementation and performance of the IoT cloud. Several potential application scenarios of IoT cloud are studied, and an architecture is discussed regarding the functionality of each component. Moreover, the implementation details of the IoT cloud are presented along with the services that it offers. The main contributions of this paper lie in the combination of the Hypertext Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) servers to offer IoT services in the architecture of the IoT cloud with various techniques to guarantee high performance. Finally, experimental results are given in order to demonstrate the service capabilities of the IoT cloud under certain conditions.
Supply chain inventory optimization problem under uncertain environment is concerned. Fuzzy chance constrained programming model for multi-item joint replenishment is proposed, which can take into account fuzzy demand quantity, as well as the constrained conditions are not satisfied to a certain degree. Demand quantity is a triangular fuzzy number, combined with the possibility measure theory. The objective function is to minimize the total cost of the supply chain. The model is solved by particle swarm optimization (PSO), and the fitness function value of the particle is the objective value of fuzzy chance constrained programming model. The feasibility of the model and the effectiveness of the algorithm are proved by simulation numerical examples, and comparisons of results under different probability level are made.
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