# Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks +
## 4 Experimental Evaluation
### Scenario 1--Evaluation of the Reactive Processes  
Here, we demonstrate the functionality of the reactive strategy, targeting the flexibility and adaptability improvements that may bring, rendering NDN deployments over dynamic WMN topologies feasible. In this scenario, we focus on the first IoT application scenario, i.e., short flows with delay demands. More precisely, we consider 150 different IoT contents generated from the Producer node, while the Consumer node transmits 1000 interest packets one by one to fetch the generated IoT contents from the Producer node. The content requests follow the Zipf distribution [36], (with \(\alpha=1.5\)). We set the freshness period to 10 s, which is the time that the cached content is valid. First, we discuss the results over stable networking conditions. In practice, we consider a multi-hop network topology consisting of seven wireless network nodes from the 9 th floor of the w-iLab.1 Fed4 FIRE+ [18] office lab, illustrated in Figure 5. The experiment assumes one Consumer (Node9-13), one Producer node (Node9-21), and the Controller (Node9-3) participating in the same wireless topology, i.e., the latter performs centralized control of the entire network. Although the selected devices are located in close proximity, we adjust the transmission power (TP) of the wireless nodes to 3 dBm and managed to shape a multi-hop scenario. The monitoring data of each node include information about the BATMAN neighbors and originators enabling the global view of the network, and, are sent to the Controller through the A.L.F.R.E.D. tool [37]. The Controller configures the NDN nodes by transferring messages to the NFD container over TCP. These messages include information about the next hop and the particular content prefix and, thus, enable NDN face creation and prefix registration. Moreover, the nodes are not located nearby, thus, their connectivity is accomplished through the WMN. Results are collected over 10 repetitions of the experiment. Furthermore, the evaluation includes three metrics: (i) the performance of the NDN network in terms of communication delay (i.e., the Interest-Data exchange procedure between the Consumer and the Producer); (ii) the path establishment delay, i.e., the elapsed time at which the Controller makes the best path decision and configures the NDN network, and (iii) the performance of SDN decision making over the WMN. A detailed description of the metrics used, follows. * \(RTT_{NDN}\) is obtained from the NDN Consumer node and represents the round trip time (RTT) between the interest packet transmission and the data packet reception, \[RTT_{NDN}=RTT_{NDN_{c}}+RTT_{NDN_{p}},\] where \(RTT_{NDN_{c}}\) corresponds to the RTT values in cache-hit cases and \(RTT_{NDN_{p}}\) is the measured RTT in case of Producer-fetched content. Note that, the Consumer can fetch  
Figure 4: Evaluation of the wireless links. the requested content either from the Producer node or from the on-path nodes, due to the in-network caching. * _Total Delay:_ denotes the elapsed time between the Consumer's request to the Controller until the Data packet reception, including the path establishment delay. * _Best Path Changes (BPC)_: represents the total number of best path changes in the whole duration of the experiment. We now move on to discussing our results. In Figure 6, we present the outputs of the reactive approach, considering the \(RTT_{NDN}\) and the _Total Delay_ metrics. The low average \(RTT_{NDN}\) value, indicates that a reactive approach may ensure the seamless operation of the NDN in volatile network topologies, without compromising the NDN performance. On the other hand, the high average _Total Delay_, especially in contrast to the average \(RTT_{NDN}\), reveals the additional network control overhead introduced by the Controller, as detailed in Section 3.1.1, which could be avoided in the case of proactive path selection strategies.