# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 2 Background and Related Work
### NDN Forwarding Strategies
#### 2.2.2 Related Work  
In the field of Information-Centric Networking (ICN), IP-based models significantly influenced the development of routing protocols, such as the Named-data Link State Routing protocol (NLSR) referenced by [PERSON] et al. (2018) [PERSON] et al. (2018). NLSR operates on two primary Link State Advertisements (LSAs): Adjacency LSA for broadcasting live link connections between an ICN/NDN router and its neighboring nodes and Prefix LSA for announcing registered name prefixes. To maintain network integrity, routers periodically exchange Link State Database (LSDB) hashes, facilitating the identification and rectification of any inconsistencies. This strategy of hop-by-hop synchronization effectively reduces unnecessary network traffic. Utilizing the LSAs received, NLSR generates a prioritized list of forwarding choices for each name prefix, thus supporting NDN's flexible forwarding strategies. However, NLSR's method of spreading neighbor information to all nodes can introduce significant routing overhead, particularly problematic in environments with dynamic node mobility and frequent topology changes, as NLSR was originally designed for more stable, wired networks.  
The Content Connectivity and Location-Aware Forwarding (CCLF) protocol, designed for vehicular ad hoc networks (VANETs) within the Named-Data Networking (NDN) framework, offers an innovative approach to network routing [35]. In CCLF, vehicles act as intermediate nodes, employing broadcast transmission for forwarding. The protocol determines the forwarding path by calculating scores based on the vehicle's current location, the previous hop's location, the destination's location, and the ratio of satisfied content requests. This method assumes knowledge of the destination's location, a challenging requirement in edge networks where precise locations of computing nodes or data producers may be unknown.  
Broadcast-based self-learning forwarding, a method used in both wired and wireless networks, represents a significant advancement in NDN. This technique involves broadcasting the initial packet along an unknown path and subsequently establishing forwarding routes based on the responses. In NDN environments, the Named-data Forwarding Daemon (NFD) uses the longest prefix match against the Forwarding Information Base (FIB) to decide on forwarding paths. This decision-making process may also incorporate criteria like energy efficiency [45] or load balancing [14]. Self-learning in NFD involves flooding the network with Interests when no FIB entry matches and learning from the paths of returned Data packets to create new FIB entries. Despite being advantageous, this method can cause network congestion due to the initial flooding necessary for path discovery.  
E-CHANET, as discussed by [PERSON] et al. (2013) [PERSON] et al. (2013), extends the AODV routing protocol for ICN in wireless networks. This protocol uniquely broadcasts both Interest and Data packets, an approach well-suited to the dynamic nature of wireless environments and the mobility of nodes. The broadcasting aspect of E-CHANET eliminates the need to continuously identify and update information about neighboring nodes. Additionally, it employs a timer-based forwarding strategy to control the potential issue of a broadcast storm effectively. Despite these advancements, E-CHANET faces challenges in practical wireless multi-hop network applications, particularly due to the high interference causedby unscheduled simultaneous transmissions and the lack of acknowledgment mechanisms, impacting its effectiveness.