## 1 Introduction
The article uses the framework to evaluate the suitability of using the physical features exhibited by the network as the condition variables that best characterise the response elicited by a communications solution in the network. If the physical features selected are suitable for this stated purpose, a set of solutions tested under a range of these physical features will give a rich and accurate response suitable for directing solution selection. Modelling is performed using linear regression modelling techniques on the test data. Using the framework we can begin to answer two important questions:  
* Is it possible to effectively predict how well a communications solution will perform compared to another solution within an arbitrary deployment using the physical features as predictor variables. * From such predictions is it possible to either advise on, or automatically deploy the most appropriate solution for an application. The questions above are subject to how we define the term _well_, how we characterise an _arbitrary deployment_ and how we determine _most appropriate_. For the purpose of this article we use four evaluation metrics: latency, reliability, efficiency and stability to determine how _well_ a solution performs. An _arbitrary deployment_ is a previously untested network which is characterised by a number of measurable physical attributes of the network based around the network size, node density, network diameter and link conditions. The _most appropriate_ solution is chosen using a set of requirements,the same as those used for evaluation, which the application requests from the network. Even with a defined set of requirements it is difficult to determine what is _most appropriate_ for the application. We begin with a strict but narrow definition for most appropriate; the best performing communications solution for each evaluation metric using the prediction model. Simply, from our set of prediction models can we generate a ranked list of the best performing solutions for each evaluation metric. This is then expanded to a more general discussion on selecting the solution based on how the whole response matches the application requirements. It is important to note that as a modular framework, each facet is interchangeable for use with another technique. For example, in this article linear regression modelling is chosen to model the data obtained from the experiments. Other modelling techniques can be easily substituted in place of linear regression modelling should they present better representation for a particular data set. Experiments were conducted using the TOSSIM [5] simulator in order to examine the suitability of physical features to define the response of a communications solution in a network. The experiments examine three different solutions over a range of four main physical features: network size, node density, network diameter, link conditions and two secondary physical features: node density (standard deviation) and network diameter (standard deviation). A use case emulating a BEMS deployment is used for evaluation. Experiments are conducted in simulation using the TOSSIM [5] simulator. Each of the communications solutions tested use the Routing for low Power and Lossy networks (RPL) routing protocol within a BEMS deployment scenario. A range of communications solutions were tested each based on the RPL routing protocol. Different solutions and responses from the network are achieved by comparing different metrics with which the RPL protocol creates it's routes. While the solutions evaluated in this article are based around the RPL protocol, the framework and testing process described is general enough to accommodate a wider set of solutions. The main contributions of the article include:  
* Introducing a framework for consistent evaluation, comparison and autonomous selection of communications solutions within a given deployment. * Evaluating a set of physical features to determine which features have the greatest effect on the response of a communications solution in the network.