Nowadays, rapid developments of Internet of Things (IoT) technologies have increased possibilities of realizing smart cities where collaborations and integrations of various IoT application systems are essential. However, IoT application systems have often been designed and deployed independently without considering the standards of devices, logics, and data communications. In this paper, we present the design and implementation of the IoT server platform called Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) for integrating IoT application systems using standards. SEMAR offers Big Data environments with built-in functions for data aggregations, synchronizations, and classifications with machine learning. Moreover, plug-in functions can be easily implemented. Data from devices for different sensors can be accepted directly and through network connections, which will be used in real-time for user interfaces, text files, and access to other systems through Representational State Transfer Application Programming Interface (REST API) services. For evaluations of SEMAR, we implemented the platform and integrated five IoT application systems, namely, the air-conditioning guidance system, the fingerprint-based indoor localization system, the water quality monitoring system, the environment monitoring system, and the air quality monitoring system. When compared with existing research on IoT platforms, the proposed SEMAR IoT application server platform offers higher flexibility and interoperability with the functions for IoT device managements, data communications, decision making, synchronizations, and filters that can be easily integrated with external programs or IoT applications without changing the codes. The results confirm the effectiveness and efficiency of the proposal.

The improved technology of big data and the internet of things (IoT) increases the number of developments in the application of smart city and Industry 4.0. Thus, the need for high-performance cloud computing is increasing. However, the increase in cloud computing service providers causes difficulties in determining the chosen service provider. Therefore, the purpose of this study is to make comparisons to determine the criteria for selecting cloud computing services following the system architecture and services needed to develop IoT and big data applications. We have analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine learning. We use these parameters to compare several cloud computing service providers. The results present that the parameters able to use as a reference for choosing cloud computing for the implementation of IoT and big data technology.

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The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.