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Recently, special attention has been paid in developing methodologies and systems for embedding autonomy within smart devices (Things). Moreover, as Things typically operate in an interconnected IoT ecosystem, autonomous operation must be performed in a cooperative fashion so the different Things coordinate their autonomous actions towards meeting high-level objectives and policies. Embedding Things with cooperative autonomy typically requires a tedious and costly effort not only during the original ecosystem deployment but throughout its lifetime. The current study describes CAO (Cognitive Adaptive Optimization)—and its distributed counterpart L4G-CAO (Local for Global Cognitive Adaptive Optimization)—which can overcome this shortcoming. CAO and L4G-CAO—which have recently been introduced and tested in a variety of IoT applications—can embed Things with cooperative autonomy in a plug-n-play fashion, i.e., without requiring the aforementioned tedious and costly effort. Results of the application of the aforementioned approaches in three different application domains (smart homes and districts, intelligent traffic systems and coordinated swarms of robots) are also presented. The presented results demonstrate the potential, of both approaches, to exploit the IoT automation functionalities in order to significantly improve the overall IoT performance without tedious effort.