## 2 Related Work  
Many researchers have worked on the virtualization of wired networks, such as the local area network (LAN) or data center. The virtual local area network (VLAN) or virtual private network (VPN) are well-known examples of the virtualization for wired networks [16]. The overlay network that has a virtual topology constructed on the physical topology is also this kind of example [17]. Constructing an overlay network can be seen as a virtual network embedding problem, which consists of virtual node mapping and virtual link mapping. Virtual nodes are allocated in physical nodes in virtual node mapping, and virtual links connecting virtual nodes are allocated in physical paths connecting the corresponding physical nodes in the virtual link mapping. In virtual network embedding, the request of constructing an overlay network is described as a graph with the required resources of virtual nodes and virtual links, such as CPU or bandwidth. In the wireless scenario, however, resource abstraction of links, such as bandwidth, is challenging because of unstable channel quality [16]. Interference and fading of wireless signals are inevitable without sophisticated time synchronization of nodes and can degrade the performance of virtual networks. Moreover, in the future IoT environment, there are various applications, and burst, periodic and time-varying traffic coexist. Therefore, it is not realistic to reserve the finite size of resources in the environment, and the existing methods for wired networks are not applicable straightforwardly. In the topology control of a WSN, tree-based and clustering tree-based approaches are proposed for energy-efficient data collection and node-to-node routing [18]. In such a cluster-based topology, however, packets for inter-cluster communication need to go through cluster heads, which can be a single failure. [PERSON] et al. applied a multi-objective optimization scheme to the topology control of a WSN [19]. Their focus is to provide a set of Pareto-optimal solutions that optimize four competitive objectives, the number of active nodes, coverage, connectivity and energy conservation, by adjusting the communication ranges of sensor nodes and sleep scheduling of sensor nodes. In addition to the multi-objective optimization algorithm, they introduce a learning automata for the dynamical configuration of the topology according to the environmental changes. Although the simulation evaluation showed the best performance compared to the existing approaches, the discussion about routing overhead and computational overhead is lacking. For decoupling IoT networks into the control plane and the data plane, software-defined networking (SDN) technologies are discussed. [PERSON] et al. proposed an SDN controller for managing heterogeneous IoT networks [20]. They define a resource matching strategy between abstract task description and resource specifications and flow scheduling by using a heuristic algorithm until the algorithm finds a feasible solution for the request. Because the controller estimates the end-to-end flow performance before resource allocations, their proposal shows better throughput, delay and jitter than existing ones. [PERSON] et al. proposed a software-defined IoT framework, which exploits several software defined systems, such as SDN, software-defined storage and software-defined security [21]. Their focus is to simplify the IoT management and to solve problems involved in the big data scenario. However, specific evaluations of the model were not examined. Other virtualization schemes for eliminating the tight-coupling of applications and physical substrates in IoT networks are also proposed. However, to our knowledge, how to construct the VWSN topology that supports reliable communication has not been discussed enough.