# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 2 Related Work  
The system architecture and design criteria for real-world applications such as health and environmental monitoring were evaluated in relation to the WSN testbed. Several researchers have highlighted key testbed elements that can be customized to design specifications, such as hardware deployment capabilities [10; 11; 12; 13; 14]. Some researchers have emphasized the use of different hardware components such as Raspberry Pi and Arduino [15], as well as use cases such as sensor node distribution for environmental applications. Considering a use case of data collection from scattered traps on the ground in New Zealand, the forest vegetation region is separated into four parts based on [16] (Claverley, North Taupo, Leader Valley, Purakaunui). At these four sites, the density of effective traps ranges from two to four traps per hectare. The spacing between traps varies based on the species targeted, according to the New Zealand Department of Conservation [17], and it determines the distance between traps in a trap line for rats, stoats, and possums. According to [17], the initial spacing between possum traps is 20 to 40 m, but can be expanded to 100 m if possum population is low. The work in [18] used 108 brushtail possum monitoring sites to estimate possum occupancy rates in wildlife, and the transect of traps is recognized at a distance of 200 m apart. Therefore, the distance between possum traps needs to be specified. At the node level, trap sensors capture field-level data before sending them to the central gateway via entry points such as unmanned aerial vehicles (UAVs). According to [PERSON] et al. [19], the embedded hardware collects and processes data from each sensor using the Raspberry Pi single-board computer as a gateway. The work in [20] employed ultralow-power TelosB nodes as ground sensor nodes distributed in the field to improve accuracy when spraying pesticides while lowering the risk of human exposure to these products. To reduce energy consumption, a precise and scalable data gathering scheme was designed to enable long-distance communication at low bitrates. In [21; 22], the authors employed an Arduino-based data collection implementation for both WSN and UAV, as well as a Raspberry Pi at the base station to collect environmental data. Owing to the high cost and risk of damage associated with hardware testing without prior operational and functional network design, virtualization has long been a widely accepted solution for performing software-based simulation testing and obtaining adaptations for use in physical networks. Efficient use of underlying physical functions is mainly achieved by abstracting them into logical or virtual functions [23]. Software-driven virtualization offers a testing ground for conducting and analyzing soft trials of network scenarios, such as dynamic behavior. Such parallel co-simulation running in the cloud backend can significantly aid in leaning out the network configuration process by means of obviating the hardware requirement (during the testing process). For example, the network simulator Contiki Cooja was adopted as a virtualization platform for certain target hardware (Motes such as TI CC2538 Evaluation Module) [24]. [PERSON] et al. [24] emphasized the importance of virtualization in driving towards a flexible IoT-based WSN organization. Herein, by accessing real physical data for the purpose of modelling and simulating virtual networks, the organization herein benefited from improved flexibility and reduced latency by deriving appropriate feedback generated by the virtualization unit. Cloud-based virtualization has been adopted to plan and test various WSN reorchestration scenarios when a dynamic event occurs prior to actual implementation. Network performance (i.e., packet loss, network downtime, etc.) can be analyzed so that the most appropriate reorchestration structure can be applied to the physical network [3; 25]. This can support flexible network operation and lessen the impact of unexpected network behavior [7].