# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
## 3 IoT Technologies in Smart Cities of the Future
### Challenges and Enabling Technologies
#### 3.1.1 Need for Convergence of IoT Technologies
The convergence of tactile internet technologies with IoT slicing and IoT object mirroring technologies appears as promising for tackling these needs. The term \"tactile internet\" was defined by the IEEE P1918.1 [39] as: \"A network or network of networks for remotely accessing, perceiving, manipulating or controlling real or virtual objects or processes in perceived real time by humans or machines.\"  
Involving human-in-the-loop (HITL) by considering human perception and enhancing the collaboration and interaction between humans and machines in real, virtual, and remote environments based on explainable AI mechanisms, is crucial. Augmented and virtual reality (AR/VR) technologies and intelligent IoT devices can undertake a significant role in this process. The potential delivery of physical tactile experiences, remotely or locally, enables the delivery of skills in digital form. This novel domain, named the Internet of Skills (IoS) [40], will revolutionize the way we generally interact with our surroundings, creating a new perspective for the smart cities of the future, based on the evolving concept of CPS. Of significant importance is the consideration of more realistic user behaviors in accessing the available communications and computing resources, thus, giving rise to risk-aware and cognitive data offloading approaches in MEC environments and, thereby, realizing an HITL methodology. 3.1.5. Efficient IoT Data Storage, Representation, and Management  
Data management is key to a viable IoT strategy. The nature of IoT data is frequently heterogeneous and unstructured when collected at the cloud. Data lakes have become popular for storing, crawling, cataloging, and indexing data from IoT devices (e.g., Delta Lake [41]). One step further is the converting of data lakes to knowledge graphs [42]. Knowledge graphs include the power of semantics, making possible intelligent analysis of the data and further data monetization. Based on the collected data, IoT devices also need to be able to learn from each other, which means that there is a need for leveraging on all the local AI models created on IoT/edge devices. Federated learning techniques make this possible since the IoT end devices use their local data to train the machine learning models required by central services. IoT devices send the model updates rather than raw data to the centralized services for aggregation [43]. Federated learning has proven to help in terms of the data privacy and accuracy of machine learning solutions. More generally, this is valid for all the distributed learning programming techniques that may take advantages from the IoT facilities exploitation, as made in [44] with the support of the framework presented in Section 6. Typically, the data streaming from a few connected devices may be manageable; however, additional storage and privacy needs can become an issue as more devices come online. A blockchain-based design for the IoT makes possible a distributed access control and data management by skipping a centralized trusted authority and instead empowering the users with data ownership.