# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
For instance, a specific region of a smart-city deployment may suffer from unstable wireless conditions, such as frequent  
Figure 1: Example of NDN communication over CityLab testbed [4]. topology rearrangements, wireless node failures, or high signal interference in crowded areas [4], affecting the NDN performance. In this context, efficient NDN packet forwarding over WMN networks with unstable conditions and low-quality wireless links is remaining an open issue [12]. Here, we argue that Software-Defined Network (SDN) may provide the missing features of intelligent centralized control and programmability to facilitate efficient NDN operation in challenging communication environments, such as the smart-cities WMN [2; 13]. For example, in decentralized (e.g., non-SDN) NDN routing solutions, each NDN router is only aware of the state of its own paths and ignores other paths in the network. The SDN-NDN integration enables the centralized network view making easier the path selection/finding considering more sophisticated forwarding decisions based on several metrics (e.g., network state, RTT, hop counts, cash contents) [14]. Furthermore, the state of NDN routing nodes is maintained in low complexity as the routing decisions, and the network configuration functions are taken over by the SDN controller. Thus, the integration of SDN with NDN over WMNs may bring the following features: (i) global view of the network topology and centralized monitoring data collection; (ii) allows the decision-making (including NDN best-path selection) based on the \"global\" network view; and (iii) the dynamic NDN configuration in groups of network nodes. In this context, in our previous work [15], we proposed an SDN controller which utilized a WMN routing protocol, providing a reactive solution for NDN path selection. On the other hand, the centralized control of SDN brings communication overhead which is an important tradeoff between the cost and the benefits of designing SDN forwarding strategies [14]. Nevertheless, there are several studies that target reducing the SDN overhead, especially over low-power SDN wireless networks, e.g., [16]. Additionally, a reactive solution, such as [15], may handle dynamic changes in the communication performance, this comes with the cost of increased management overhead, due to the frequent (and potentially unnecessary) NDN path changes. The latter implies that this approach could be inefficient over network typologies consisting of static wireless nodes [4]. Motivated by this fact, we discuss the limitations of a one-fits-all NDN path selection strategy, claiming that an appropriate solution should take into consideration the respective network environment, even in the context of a smart-city environment. In this work, we discuss the trade-offs between two logically-centralized NDN strategies over WMN smart-city networks. More precisely, we consider the following SDN-based NDN path selection solutions, which they target efficient end-to-end NDN communication in terms of performance (i.e., delay): (i) a _reactive approach_, which is an extension of our previous work in [15] (this approach is utilized also in our work [17]) and re-adjusts the NDN path based on the BATMAN protocol; and (ii) a _proactive approach_, that a priori defines the NDN path, based on a combination of partitional clustering and similarity-based measures. The key contributions of the specific work are summarized as follows:  
* Implementation of an SDN-based system that supports two NDN path selection strategies (i.e., a reactive one and a cluster-based proactive), built on top of real WMN networks. * Real experimentation over the w-iLab.t testbed [18], evaluating the performance impact of our SDN-based solutions considering experimental scenarios with stable and unstable conditions. * Collection of real measurements related to RSSI, Delay, and Packet loss from indoor [18] WMN network. We record the performance of links among real wireless nodes and provide the measurements as released open-data [19].