Providing the Markov chain equation model to reduce temperature prediction errors using the Internet of Things (IOT)

(IoT) is one of the most important networks with many applications. In this network, the objects are capable of connecting to the network and sending information to the server and the server can control objects remotely. Nowadays temperature control by the IoT is very important and widespread and network sensors send the received temperature to the server at intervals. Temperature monitoring and surveillance systems are control systems that are created as a network based on the (IoT) by placing sensors in the desired environment. Procedures in these data collection models include assigning monitoring tasks to sensors, acquiring data transmission monitoring data, and controlling data accuracy. The Procedures in these data collection models include assigning monitoring tasks to sensors, acquiring data transmission monitoring data, and controlling data accuracy. Due to the huge growth of smart objects and their application, the need to collect and analyze sensor data has become one of the main challenges. The data set used in this paper contains records of temperature measured in a commercial building. In this paper, while comprehensively examining the methods of temperature control and monitoring in the (IoT), an attempt is made to provide a method that can perform a data aggregation related to temperature measurement based on the accuracy of the data sent by nodes in previous periods. In the proposed method of this research, the received data is stored as a Markov chain and by examining the data in the past periods, the accuracy of the current data can be obtained.