# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 1 Introduction
One of the principal challenges in advancing fog computing over NDN is the current lack of support for multi-hop wireless networks. As of now, a single NDN Forwarding Daemon (NFD) face is employed for communication with all the neighboring devices in wireless setups. This face is bounded by a specific wireless interface (e.g., WiFi board/network interface card (NIC)) configured in broadcast mode. Given that the default NDN technology imposes restrictions on sending a packet via the face from where it was received, wireless communication in NDN is limited to a single hop. This issue also raises problems related to communication reliability in wireless environments, as broadcast wireless communications typically do not support retransmissions. Furthermore, it is important to highlight that the forwarding strategies proposed for NDN have been designed with wired networks in mind, where changes in network topology are infrequent. The potential applications of Named-Data Networking (NDN) and fog computing span diverse scenarios, each demonstrating the strengths of this technology in dynamic and resource-constrained environments:  
1. Mobile network nodes in smart cities: A quintessential example is the smart city infrastructure, where mobile nodes such as vehicles or mobile devices continuously move, dynamically altering the network topology. Leveraging NDN with fog computing in such environments can facilitate efficient data dissemination and retrieval. This combination is particularly beneficial in reducing latency and enhancing data availability at the network's edge, crucial for real-time applications like traffic management and event streaming. Drones in Mesh Networks: Another promising application involves the use of drones in mesh networks for agricultural monitoring, disaster management, or delivery services. Integrating Fog Computing with NDN here enables drones to share vital information in a robust and decentralized manner. The information could be, for example, weather data or emergency signals. This approach is especially beneficial in areas with limited infrastructure, as it supports autonomous and resilient drone operations. Further enhancing these scenarios is the application of a multi-stage machine-learning (ML) overlay, which brings capabilities like predictive analytics and adaptive routing. This advancement improves the efficiency and adaptability of the systems, significantly elevating the overall performance and user experience. This paper addresses the identified gaps and refines existing solutions by proposing two mechanisms that enable efficient NDN-based fog computing services in wireless mesh systems: (i) dynamic face management capability and (ii) adaptive, learning-based forwarding strategy. The first mechanism enhances efficient communications by utilizing unicast transmission mechanisms, such as request-based medium access (RTS/CTS) and acknowledging packets successfully received in WiFi. The second, an adaptive learning-based forwarding strategy, efficiently reduces network interference by forwarding Interest packets towards their intended destinations. Importantly, these two functionalities maintain compatibility with existing NDN stacks, ensuring full backward compatibility with current NDN implementations. Having implemented this system in ndnSIM, an extension module for NS-3 (a network-level simulator, NLS), we subsequently evaluate and characterize the performance of the proposed solution. The main contributions of our study can be summarized as:  
* joint implementation and evaluation of the unicast Ethernet method faces in NFD, enabling reduced overheads (as compared to broadcast) when forwarding Interest packets in dynamic mesh networks;
* performance evaluation of the adaptive learning strategy and dynamic Face management system in dynamic network conditions showing that the suggested enhancements in NDN system design efficiently support fog computing in multi-hop wireless mesh systems. The paper is organized as follows. Section 2 overviews the prior art.