## 1 Introduction
On the other hand, the authors of [5] focus their study on the emerging narrow band NB-IoT radio access technology and propose a multi-objective dynamic configuration for the user equipment to achieve a combination of connectivity metrics including energy efficiency, reduced delay, and throughput. Towards this end, reinforcement learning utilising gradient descent and a genetic algorithm is adopted synchronously with ML and DL algorithms to predict the environmental states and suggest an optimal configuration. A review of intelligent communication systems that is centered on the role of ML is presented in [6] to address the challenge of IoT connectivity. In this work, the authors give an overview of the progress in standardisation including Open Radio Access Networks (O-RAN) and 3 rd-Generation Partnership Project (3 GPP). In this context, the role of the radio intelligent controller (RIC) in the operation and configuration of the O-RAN is discussed, as is the use of ML in its automation for enabling IoT connectivity. On the other hand, the NetWork Data Analytic Function (NWDAF) is examined in the context of 3 GPP and the new related interfaces that allow for the implementation of ML and interoperability with the O-RAN. Leading pilot projects in the implementation of intelligent communication systems are surveyed and lessons learnt for IoT connectivity are highlighted. Security is another overarching challenge that is amplified due to the massive number of IoT devices. The security risk is higher in the case of IoT devices, as these are often limited in computational power, memory, and access to energy sources. It follows that IoT devices are vulnerable to security threats, a challenge that is more pronounced in the case of devices installed in unprotected and/or remote locations and, hence, exposed to tampering. The authors of [7] propose an intrusion detection algorithm that detects malicious behaviour within low-power, low-rate, and short-range IoT networks. A support vector machine (SVM) method is designed that successfully monitors and detects abnormal activity within the smart IoT device. On the other hand, the authors of [8] propose detecting malware attacks in IoT networks using methods inspired by the human immune system. To this end, they present a survey of artificial immune systems (AIS) and draw a parallel between the advantages of AIS and the limitations of IoT devices and systems. Furthermore, a quantitative performance analysis of leading AIS methods in IoT malware detection is discussed in which the negative and positive detector system (NPS) is shown to outperform the state of the art. IoT data privacy is a common concern that affects IoT applications in multiple verticals. Indeed, IoT devices are used for remote sensing and actuating in critical solutions including health, security, and autonomous things (e.g., vehicles, factories). Tampering with data measured and communicated through the IoT puts at risk the privacy of the owner of the data, such as the identity of people using e-health solutions, private information of people captured by security cameras, or enabling targeted tampering using remote actuators. This Special Issue addresses concerns for data privacy in two leading works. The first [9] proposes a countermeasure to the risk of statistical traffic analysis that allows attackers to infer users' online activities. The paper puts forward a data obfuscation mechanism supported by a heuristic algorithm that selects the applications to mutate to and therefore reduces the accuracy of user activity classification to 1.42%. In the second work [1], the authors present a multi-layer digital twin system that allows for privacy-preserving coordination between the central energy provider and edge residential energy consumers. The design employs a smart home IoT gateway that receives dynamic time-of-use energy tariffs from the central server and, accordingly, orchestrates the usage of electric appliances at home.