###### Abstract  
The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems. With the proliferation of IoT devices, there has been a vast increase in the volume of data generated by these devices. This massive influx of data presents both challenges and opportunities, as traditional data analysis methods may not be sufficient to handle the sheer volume of data. As a result, there is a growing need for advanced data analysis techniques and artificial intelligence (AI) methods to process, analyze, and extract valuable insights from IoT data. In this context, data analysis and AI play a critical role in enabling the effective implementation of IoT technologies and realizing their full potential. This Special Issue explores the importance of data analysis and AI for IoT. It discusses the various techniques and approaches that can be used to leverage IoT data for actionable insights effectively. Therefore, this Special Issue covers nine papers, summarized as follows:  
The paper \"Application of Machine Learning Algorithms for the Validation of a New CoAP-IoT Anomaly Detection Dataset\" [1] addresses the need for developing an anomaly-based intrusion detection system for the CoAP-IoT environment, which is vulnerable to security threats due to its power-, bandwidth-, and memory-constrained sensing devices. The paper presents a complete and labeled CoAP-IoT anomaly detection dataset (CIDAD) based on real-world traffic, including three types of anomalies in the CoAP data. The dataset is validated using five shallow machine learning techniques, and the evaluation metrics used in the performance comparison are accuracy, precision, recall, F1 score, and kappa score. The results show that the system achieved 99.9% accuracy for decision tree models, and random forest established itself as the best model, obtaining a 99.9% precision and F1 score, 100% recall, and a [PERSON]'s kappa statistic of 0.99. The paper provides a valuable contribution to the field of anomaly-based intrusion detection systems, which are crucial for securing the CoAP-IoT environment against security threats. The proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved to be effective in building training data for machine learning. The detection accuracy for ambiguous objects was over 95%, and the approach reduced the tracking loss rate to 10%. The paper \"An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos\" [2] proposes a novel feature selection and extraction approach for anomaly-based intrusion detection systems in the Internet of Things (IoT) environment. The approach uses entropy-based methods to select and extract relevant features, followed by mathematical set theory to extract the best features. The proposed model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms. The approach resulted in 11 and 28 relevant features on IoTID20 and 15 and 25 relevant features on NSL-KDD, using intersection and union, respectively. The performance of the proposed model was compared with other state-of-the-art studies, and it was found that the model outperformed others, achieving a classification accuracy of 99.98%. The proposed feature selection and extraction approach addresses the challenge of exploiting all features and attributes for IDS self-protection, making it a competent and superior model for IoT intrusion detection. In conclusion, the proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved effective in building training data for machine learning. The detection accuracy for ambiguous objects was over 95%, and the approach reduced the tracking loss rate to 10%.