Building Distributed Systems for Fresh and Low-latency Data Delivery for Internet of Things
Abstract: Internet of Things (IoT) is a system of interrelated computing devices with the ability to transfer data over the network and collected by the applications that rely on fresh information, where the freshness of data can be measured by a metric called Age of Information (AoI). Age of Information is the time that is measured by the receiving node from the time the data has generated at the source. It is an important metric for many IoT applications such as, collecting data from temperature sensors, pollution rates in a specific city. However, the bottleneck problem occurs at sensors because they are constrained devices in terms of energy (power via battery), and also have limited memory and computational power. Therefore, they cannot serve many requests at the same time and thus, it will decrease the information quality which means more unnecessary aging. As a result, we suggest as a solution a distributed system that takes into account the AoI transmitted by the sensors so that IoT applications will receive the expected information quality. This thesis describes the three algorithms that can be used tobuild and test three different topologies. The first algorithm builds a Random graph while second and thirds algorithms shapes Clustered and Hybrid graphs respectively. For testing, we use Python based SimPy package which is a process-based discrete-event simulation framework. Finally, we compare Random, Clustered and Hybrid graphs results. Overall, the Hybrid graph delivers more fresh information than other graphs.
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