# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 2 Background
### Artificial Intelligence and Machine Learning (AI/ML)  
Simply put, AI is the art of enabling machines with human-like thought, reasoning, and decision-making power. It is the grand quest to breathe life into algorithms, allowing them to perceive the world, learn from experience to build a model that can reflect the target system, adapt to changing circumstances, and provide solutions to complex problems [7; 41; 42]. The ML, which is a subset of AI, learns from the given data about the past to make better futuristic predictions. It is the digital detective, discovering patterns and insights that elude human senses in pursuing more intelligent and autonomous machines.  
In this context, the typical applications of AI/ML methods include dynamic control decision-making and pattern recognition/classification techniques as described below.  
**Reinforcement learning (RL)** is a branch of the dynamic ML paradigm where software agents learn to make decisions in an environment by receiving feedback as rewards [43]. Through a cycle of observations, actions, and learning, the agents gradually improve their decision-making abilities to achieve long-term goals. Several RL types range from value-based, policy-based, deep RL, or actor-critic learning, which combines the two for optimized decision making in dynamic environments.  
RL is applied in the O-RAN context to optimize the management and performance of the network. For example, RL dynamically allocates resources (bandwidth, power, frequencies, etc.) to different IoT devices or network slices based on real-time conditions and demands. Similarly, RL can control power consumption by optimizing transmission power levels and sleep modes of devices while ensuring connectivity and QoS. Again, RL agents can be trained to detect network anomalies and failures and then decide to implement self-healing mechanisms. This proactive approach ensures network resilience and reduces downtime. Despite RL's promising capabilities for optimizing O-RAN and the emerging O-RAN-based IoT systems, challenges related to training data, model robustness, and real-time adaptation, among others, remain open for the research community to solve [43; 44].  
**Deep neural networks (DNNs):** This refers to the stacked neural network architectures that recognize patterns and extract information from structured or unstructured data. They are well suited for tasks involving images, sequences, and graph data [41]. Convolutional neural networks (CNNs), as a good example, are applied to tasks involving grid-like data, such as images and videos. They are proficient at capturing spatial relationships within data. Recurrent neural networks (RNNs) on the other hand, are designed for handling sequential data. The architecture has connections that allow information to persist over time, which makes it exemplary at capturing temporal relationships within the data. Graph neural networks (GNNs) are essential for data representation involving graphs, i.e., data consisting of several nodes and edges. They are mostly used in applications with essential relationships between data points, such as network traffic analysis [45].  
In this context, these techniques are applied to learn the latent characteristics of the network and its users, which will guide the effective management of the network in several ways. For example, these techniques can learn the operating pattern of the network from its data to predict traffic and performance degradation, detect anomalies and intrusions, and optimize energy efficiency. However, despite DNN's potential, striking a balance between their complexity and the complexity of the emerging O-RAN-based IoT system to satisfy the real-time performance requirement of their operating environment remains open for further research [41; 46; 47].