# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
## 4 Results Analysis  
The performance of the proposed DRM strategy is evaluated by means of numerical simulation resorting to Cooja simulator and SDN-WISE framework [34], the latter being a specific SDN solution targeted to WSN domain. It is based on IEEE 802.15.4 Physical and MAC layers, while the network elements can be distinguished into Sinks and Nodes, where only the former can directly communicate to the SDN Controller. It is worth noting that the SDN Controller usually represents a single point of failure, in the sense that its temporary (_outage_) or permanent (_failure_) damage could compromise the correct forwarding of data flows. However, to partially mitigate this problem, we adopted an SDN-WISE based control messaging in background, comprised of Beacon, Report, Request and Response packets [35]. This allows the WSN to survive to the loss of its Controller, as far as the _upstream_ flows are concerned. Particularly, network's nodes periodically send, with the characteristics summarised in Table 1, Report messages to the SDN Controller, which contain information about the battery level of the source node, the distance from the Sink node, the neighbour list and some other important features (battery level and RSSI) of the neighbour nodes. These messages are collected by the Controller, which accordingly updates its network view and modifies the traffic flows between nodes. To change the network paths, the Controller sends specific flow rules to the nodes. This is performed using specific messages called Openpath packets as specified in SDN-WISE framework. Nodes will use these messages to update their flow entries, which are mainly composed of the following fields: (i) Matching rules; (ii) Action; and (iii) Statistics. The nodes are spatially distributed according to a _bottleneck_ topology which represents the worst case scenario for network lifetime. Specifically, only few nodes fall inside the Sink coverage area, thus they are supposed to forward to it the messages originated by other devices, which, instead, resort to multi-hop communications. As a consequence, the nodes belonging to the _first_ tier are prone to quickly depleting their batteries. The simulation playground is 200 m\({}^{2}\) and the transmission range of sensor nodes is assumed to be 100 m. An example of bottleneck topology with 50 nodes in the playground is shown in Figure 2. All nodes are characterised by an identifier (ID) and a colour. The green node represents the Sink while purple and yellow elements identify all the other devices. Specifically, purple nodes are the only ones inside the Sink coverage area and subject to a higher packet retransmission. In particular, we refer to the MICAz more model in terms of CPU states, transmit and receive power levels, as reported in Table 2.  
In addition to this, we assumed that the Beacon and Report messages rates are increased respectively to 20 and 40 s, as soon as the \(T_{s}\) threshold is matched, while, when \(T_{c}\) is overcome, the above-mentioned rates are, respectively, updated to 30 and 60 s. Moreover, after an exhaustive measurement campaign, the obtained results have shown that, in static/quasi-static scenarios, the maximum applicable sending period of Report messages, which does not entail a considerable degradation of PLR and RTT, is about 60 s.  
We start our evaluation by focusing on the average residual energy that the proposed routing policies running on SDN Controller can achieve. In deriving our results, we assumed that all nodes present the same initial energy value except for the Sink node, which  
Figure 2: Bottleneck topology with 50 randomly deployed nodes. is not battery powered and, therefore, is not considered in the performance evaluation. In Figure 3, the residual energy is presented with respect to the simulation time ranging from 100 to 900 s for the MaxBattery, DRM and MinHop strategies.