Ağ kodlu nesnelerin internetinde bilgi yaşı

The unique nature of the Internet of things (IoT), with potentially millions of interconnected devices featuring varying data rates, power levels, bandwidth, and range specifications, demands different performance metrics compared to traditional communication systems. In conventional wireless communication like cellular networks, performance markers such as data rate and spectral efficiency are paramount. However, in energy-constrained real-time IoT applications with low data rates, the timeliness of information, measured as age of information (AoI), takes center stage. AoI represents the time elapsed since the last packet update originated at its source and has garnered significant research attention. In this regard, This thesis provides an overview of AoI's role in designing and optimizing IoT applications, including AoI-based optimization, scheduling for IoT networks, application of learning methods in large-scale IoT systems, real-life applications and experimental results, together with a synopsis of potential future applications and research challenges, is provided in this thesis. Additionally, the timeliness in delivering updates within a multi-source multi-hop IoT networks via multicast transmissions with or without employing network coding is considered in this thesis. The effect of network coding on the average AoI is investigated employing a completely probabilistic model in a two-stage transmission scheme. The theoretical findings demonstrate that network coding has great potential to improve data freshness in multi-source multi-hop IoT networks, which closely represent the spine of real-life scenarios, and extensive simulation results corroborate the theoretical findings.