## 8 Discussion and Critique  
The material presented in this article can be discussed in three distinct sections: the framework, the features and modelling technique used in evaluating the framework, and the dataset used for modelling and evaluation. Figure 14: Results from simulation with regard to the secondary features. (**a**) Solution performance with regard to standard deviation of density; (**b**) Solution performance with regard to standard deviation of scale. A major feature of the framework relies on an assumption that networks share a certain commonality. We assume that if we can find a set of characteristics that can well define the response in the network, this response will be reflected in another network that shares the same characteristics. This is an assumption that can be challenged and the idea of network commonality, or lack thereof, is addressed by [PERSON] et al. (2010) during testing over multiple testbeds. The observation made states that reliability can be heavily effected by a small number of fringe nodes that have tentative connection to the rest of the network. This effect can also have a large impact on network stability and can be highly specific to the deployment. The paper distinguishes reliability of nodes in the the 5 th percentile to show this effect. This observation makes a case that a commonality between different networks may not be found. We believe, however, that the use of more physical metrics may accommodate for these unique traits within networks. For example, measuring the number of fringe nodes and their link quality may absorb this characteristic within the model. The article uses a set of features including network size, scale, density and channel quality. This is not an exhaustive set of features with which to characterise a network. We believe that numerous other metrics, particularly with regard to the range of link qualities displayed within a network will be implemented and will offer useful insight and greater modelling accuracy. The physical features chosen in this article are a coarse means to characterise a network but which are those that have been used consistently in research articles to evaluate communications solutions. This can also be said for the modelling technique used in the article. Multiple linear regression modelling is a simple technique that provides a course but usable model for the purpose of evaluating the framework. It is important to note that the accuracy of any system built within the framework will be heavily dependent on the modelling technique but the framework itself is agnostic to the technique used. One criticism that can be made of the dataset used in this article is that it is comprised solely on simulated data. While this is not ideal and cannot be considered comprehensive and fully conclusive testing it gives an initial indicator so that further investigation can be directed with promising results. As well as this the range of testing in terms of the physical characteristics must be increased. With IoT applications looking at larger scaled deployments these larger scales must be investigated in testing. As the simulation does not allow for LQI or RSSI measurements the channel quality is only measured over a binary scale; heavy or light interference. This issue can be resolved by introducing testbed and additional simulation experiments to the dataset. This can also be said for the homogeneity in the testing phase. Each node in the generated networks exhibits the same communication range. A noise trace is implemented on each network which ensures that communications conditions are not the same on each node however. We believe that heterogeneity with regard to the communications range is well captured in the physical features. Density and density (standard deviation) as well as any link measure would be heavily effected should large differences appear in the network with regard to communication range. One other metric that might help with the indication of range variability is the number of 'leaf nodes' in the network.