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Managing Data Consistency in IoT Devices



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Since a system can only make a decision based on the information it has at any given time, when the information changes, the decision needs to change as well. For example, a smartphone user decides in the morning to go for a walk later in the afternoon since the weather is sunny and the temperature is above 15 degrees Celsius. The user then records the information and the decision in his smartphone. Here, the user bases their decision on two sources of information, weather condition and temperature, which determine their choice of whether to go for a walk or not. Later, the forecasted temperature changes to 8 degrees Celsius and the decision the user made early in the morning, to go for a walk in the afternoon, may need to change as well. There should be a way for the smartphone to be aware of the temperature change and re-evaluate the decision. If the decision needs to change, the smartphone needs to inform the user that a decision made previously is now invalid, and a new decision has been made. This research presents an architecture called Local Resources’ State Management System (LRSMS) to keep track of the data used in a device or a machine as well as dependencies between data. When the device recognizes a state change in any of its data, the LRSMS will tell the device to propagate the state change to all dependent data and inform the use of the changes. The experimental results in this research demonstrate that the data propagation works correctly in the LRSMS; however, the average data update speed depends on the data dependency structure. In some data dependency structures, the average data update speed is relatively constant, while in other structures the average data update speed increases relative to the quantity of data.



Data Consistency, IoT



Master of Science (M.Sc.)


Computer Science


Computer Science



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