# Global IoT Mobility: A Path Based Forwarding Approach
## 3 Overview of Related Work
In [36], the authors propose an ICN based mobility solution with a special focus on network mobility, where network segments/domains comprising of various networking nodes, consumers and producers can also experience mobility. Evaluation results show that the proposed solution significantly reduces the amount of signalling traffic, routing updates, and path inflation compared to existing solutions while ensuring connectivity for mobile nodes. In [37], the authors present a device-level information centric networking architecture that is able to perform intelligent content distribution operations according to necessary context information on mobile user mobility and content characteristics. The authors further introduce device-level online content caching and offline helper selection algorithms in order to optimize the overall system efficiency, which is demonstrated through simulation experiments and modeling. In [38], the authors design a mobility-aware proactive caching scheme in ICN to provide delay-sensitive services on Internet of Vehicle (IoV) networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units is modeled using a Markov process. The authors apply Mobility aware proactive edge caching decision that maximizes network performance while minimizing transmission delay. Numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20-25% and by 15-23% in terms of cache hits. In [39], the authors propose a novel vehicle tracking-based Data packet forwarding scheme (VTDF) to improve the successful delivery rate of Data packets in Named Data Network (NDN) based Vehicular Ad Hoc Networks (VANETs). In this approach, the urban road structure is divided into complex multi-junction and straight lane scenarios and Data packets are forwarded according to vehicle movement information. Simulations indicated that this vehicle tracking scheme provides a lower average data transmission delay, shorter handover delay between roadside units, and higher data delivery rate for Consumers compared to the standard methods. In [40], the authors propose a reinforcement learning-based data source selection scheme (RLSS) for efficient High Definition (HD) map distribution in vehicular named data networking (NDN) scenarios. The proposed scheme aims at seeking the best data source (roadside infrastructures or nearby vehicles) in accordance with the map data requests. Specifically, the scheme adopts a deep reinforcement learning-based architecture to learn a neural network as an agent to make the decision of data source selection. The authors have demonstrated through extensive simulations that RLSS can significantly improve the transmission performance in terms of delay, throughput, and packet loss when compared with state-of-the-art data source selection schemes. All of the above have shown promising results with using an information-centric approach, albeit with the requirement to change the end point networking stack. On the other hand, there are other proposals that enable applications running on IP protocol stacks to utilize ICN networks. They can be summarized as follows. A general purpose tunneling protocol (IPoC) is presented in [41], and uses Named Data Network (NDN) semantics and cellular communication as the driving example. IPoC is transparent to the IP applications on either end that remain unchanged. The authors compare their protocol performance with native IP and show that IPoC protocol overhead and performance degradation is minimal. In return, they show how NDN and IPoC can bring ICN benefits to 5G mobile networks by simplifying the mobility plane and introducing intelligent functionality. The authors in [42] present a TCP/ICN proxy capable of carrying TCP traffic between TCP/IP endpoints over NDN/CCN. They evaluate several alternative TCP/ICN proxy designs in a simulation environment.