# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 1 Introduction  
Fog computing is an emerging data-processing concept poised to significantly influence future networks and Internet of Things (IoT) applications [1; 2]. It notably facilitates data processing at end-user devices, as opposed to relying solely on dedicated edge or cloud servers [3]. The recent unprecedented proliferation of wireless connectivity, encompassing direct device-to-device (D2D) communications technologies supported by the IEEE (WiFi-direct [4]), and cellular standards (LTE- and NR-sidelink [5; 6; 7]) raises the question of how to efficiently extend fog computing architecture to exceptionally dynamic wireless systems, like mobile mesh networks. Conventionally, fog computing relies on an IP-based architecture, such as in [1; 8; 9], and requires the domain name resolution system (DNS) for resource discovery. Although several approaches are enabling this functionality in wireless mesh systems [10; 11], the performance of these is still far from optimal due to the intermittent nature of wireless links. Named-Data Networking (NDN) offers new opportunities for enhancing user experiences in scenarios where traditional IP networking is challenged [12; 13; 14; 15]. NDN eliminatesthe necessity of maintaining communication sessions between explicitly addressable hosts and shifts the focus to the requested content instead [16]. This is achieved by utilizing an 'Interest' packet to request specific data, irrespective of its location. When a node possesses the requested, it responds to the Interest packet with the data. Notably, the NDN architecture is also inherently agnostic concerning the type of the requested content, rendering it a fitting solution for fog computing applications [17; 18]. Fog computing in wireless mesh networks represents an exceptionally dynamic system characterized by its heterogeneous resources. The core factors contributing to this dynamism include node mobility, continuously changing wireless link states, fluctuating availability of computing resources at nodes, and the variability in user requests. Resources, including computing resources, storage, raw sensor data, and functions, are distributed among various proximate end devices. The most efficient way to address such dynamism is to harness all locally available resources and enable multi-tenancy for the resources and computations of the nodes with inherently different capabilities. The authors of [19] propose a framework encompassing heterogeneous IoT resources in a given region and variations in processing and communication offloading that could be leveraged by the fog IoT architecture. The architecture relies on an IoT-in-the-fog controller that can probe local resources and communicate directly with a local fog mediator, which could be the cloudlet/edge access point. In [20], the authors considered an inter-NDN MANET scenario and overcame session disruptions when crossing MANETs, which ran over different wireless technologies and protocols. The authors of [21] extended the conventional fog-to-cloud communication paradigm by introducing a horizontal fog-to-fog communication layer. This layer utilizes information-centric networking (ICN) to lessen cloud dependency and enhances the fog layer with name-oriented communications, in-network caching, and built-in mobility support to achieve lower latency, better mobility, and higher data communication efficacy for fog computing. Furthermore, to take full advantage of ICN, a fog caching scheme has been enhanced with the in-network caching capability of ICN end-user devices to facilitate content delivery in the IoT environment [22]. The author of [23] developed user-centric and delegated mode-based solutions for service provisioning in NDN systems, where the latter approach presumed outsourcing the service search functionality to the network node. An automated in-network service construction over NDN was provided in [24]. Finally, [25] introduces advancements in service discovery within NDN, enabling exhaustive exploration of network segments.