# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Topology Management
#### 5.2.1 Scalability and Localization Management  
The architectures reviewed so far are based on a centralized controller in a flat topology which is easy to deploy and manage. [PERSON] et al. [14] for instance introduce smart management of SDWSNs to improve efficiency and overcome inherent difficulties of ordinary WSNs. The management scheme is based on a proposed base station architecture for WSNs with an integrated controller. The controller creates forwarding rules that are placed in flow tables from location data obtained through localization techniques processed in the application layer of the architecture. However, flat topologies like this are limited to short range and small scale applications and as networks scale up to very large numbers of nodes there is need to integrate topologies that are hierarchical or provide a virtual overlay of the physical network [87] to support efficient scalability and localization. A few architectures are available for network management designed towards improving SDN-based WSN management efficiency by using distributed multiple controllers rather than a single centralized controller. This also enables efficient scalability and a reduction in overhead control data as it is not necessary to communicate all control data centrally. Distributed controllers also allow for effective security and QoS management as the WSN is not solely dependent on one controller, making the system more reliable during attacks or failures in the link to the central controller. A hierarchical architecture called Software Defined Clustered Sensor Networks (SDCSN) has been proposed [88] to use multiple base stations as controllers that also play the role of cluster heads. A large-scale group of nodes is divided into clusters and there is a cluster head for each. The cluster head controls and coordinates the sensor nodes in each cluster and all the information processed in each cluster is routed to the cluster head. Management of such an architecture requires enabling the sensor nodes to function as controllers effectively enabling multiple controllers in the network. [PERSON] et al. [69] design and implement an architecture based on multiple controllers within a WSN in a framework called TinySDN based on Tiny-OS with a design structure that consists of SDN-enabled sensor nodes and an SDN controller node. This addresses issues such as in-band control, higher communication latency, and limited energy supply. The downside to this management approach is that the cluster head can also become vulnerable to attack posing a network security risk [87] however self-stabilisation techniques for re-selecting a new cluster head upon such an event have been proposed [89]. To manage the network with more flexibility for integration of various functions an architecture based on network virtualisation has been proposed [59; 87; 90]. Basically, a virtual overlay topology can be built to certain specifications based on an actual physical WSN based on SDN [87] as shown in Figure 3.  
Another hierarchical architecture proposed to manage scalability is context based logical clustering [91; 92]. This is based on logically clustering sensors according to context (gathered data type) regardless of their physical distribution based on HyperFlow [93] unlike other clustering methods proposed which allow clustering of adjacent nodes. Clustering sensors according to context would allow for data and resource sharing regardless of network expansion. Each cluster has a local controller which maybe physically distributed but logically synchronised hence referred to as logical sink in the virtual network overlay. The performance study shows that this technique improves network stability effectively [92]. Sensor data without location information is not useful especially in large-scale applications with hundreds to thousands of nodes. SDN in WSNs provides the possibility of obtaining location information with a good level of accuracy by implementing proposed localization algorithms [94; 95; 96; 97; 98; 99].