The ever-increasing demand for media streaming together with limited backhaul capacity renders developing efficient file-delivery methods imperative. One such method is caching, which is enabled by the asynchronous content reuse property of multimedia content. To realize the potential of caching, the most popular contents are saved at suitable locations in the network, and are delivered upon demand. The problems become challenging when there is lack of information about the popularity of the contents, network, and other impactful factors. In this thesis, the goal is to optimize the content caching in peer-to-peer network under a lack of information, using methods from machine learning and artificial intelligence.
In federated learning, several participants (clients) contribute to model development. The participants receive a model and determine its parameters using their local data. Then they send their parameters' update to a central unit. The central unit combines all the updates, for example, by averaging, and develops a new shared model. The iteration continues to guarantee the required model accuracy. To maximize the accuracy of the developed or learned model, federated learning would attempt to benefit from every reliable participant; nevertheless, maximizing the number of participants is often inefficient, for example, due to communication constraints or by financial reasons when the participants receive reimbursement. Therefore, a more efficient solution is to select the best set of participants that satisfies the required constraints.
Situational awareness or situation awareness consists of three elements: (i) The perception of environmental elements and events concerning time or space; (ii) Comprehension of the meaning and relation of the perceived events; (iii) Look ahead of the future status by using the obtained knowledge. Although the concept dates back to the nineties, recent research works leverage machine learning and artificial intelligence to enhance and enrich the concept and its associated methods. Besides, situational awareness plays a crucial role in different scenarios of the Internet of Things, which is a highly-dense network consisting of humans, machines, and processes. In this thesis, the goal is to study AI-enabled situational awareness and to investigate its application in IoT-related scenarios.