# Mobility Support 5G Architecture with Real-Time Routing for Sustainable Smart Cities
## 2 Related Work  
In [35], the authors defined the required infrastructure and the protocol for the secure implementation of the IoT framework. They identified several new methods that could be used to address IoT security problems using machine learning and blockchain-based approaches. Advance 5G wireless sensor technologies [36; 37] facilitate mobile communication for complex and highly dynamic environments. These technologies acquiring the information by sensing the data from a real-world environment and transmit to a sink node to fulfill the demands of the application users. To collaborate with mobile IoT networks, the solution should be robust and more reliable to support the requests of application users. In [38], the authors investigated the outage probability (OP) and predicted wireless communication. It was based on the improved grey wolf optimization algorithm and an Elman neural network was proposed. The simulation-based results illustrate that the prediction accuracy was higher than other solutions and managed the wireless transmission system more accurately for mobile IoT networks. The authors in [39] proposed an Internet of Things-based WBAN for disaster cases. It ensures life savings and smooth communication using technologies of the wireless network. In addition, a gateway selection algorithm using fuzzy logic was developed that aims to select a suitable wireless communication technology. The authors in [40] maximized the task throughput for the IoT-enabled 5G network in the presence of heterogeneous task demands and constraint resources. They utilized multi-graph coloring and proposed an efficient two-stage process. The computational complexity and correctness of the proposed algorithm were analyzed. The simulation-based results demonstrate its efficacy against existing work. The authors in [41] suggested a rigorous prediction model using a rule-based machine learning classification method, i.e., the decision tree, on the noise-free accuracy dataset for real-life cell phone data of individual users. The Naive Bayes classifier and Laplace estimator were used to increase the model's prediction accuracy by minimizing noisy instances in the results. In [42], for Industrial Internet of Things (IIoT) devices, a machine learning-based anomaly detection system was proposed to detect cyber threats such as backdoor, order injection, and Structured Query Language (SQL) injection attacks. A distributed ledger-based blockchain (DLBC) technology was recommended in [43] to fix IoT protection and privacy problems such as spoofing and false authentication. In [44], a distributed intelligence system was proposed to reduce unnecessary data transfer to the cloud through immediate decision-making. They also resolved several security issues in the IoT environment using blockchain technology. In [45], classified devices with an ML approach were used to boost IoT environment security by identifying malicious data in the blockchain network. Similarly, a trust protection mechanism was introduced in [46] to provide stable and effective access control to identify and remove intrusions in a distributed IoT system. The authors in [47] presented a Safe Private Blockchain (SPB) that allows energy prosumers to negotiate energy rates and share energy with an IoT smart grid deployment. It consists of a three-layered trust management system in which trust is tracked based on the interactions between supply chain members, and trust. Finally, credibility is dynamically allocated based on these interaction scores [48]. In [49], a blockchain-based, privacy-preserving update protocol was suggested that allows users to update apps but also preserve their privacy. It increases the security level as compared to an existing solution with improved network performance. The authors in [50]proposed a clustering perturbation algorithm to preserve privacy for social networks that aims to introduce a strategy of exchanging attributes among vertices of the same degree randomly.