## 9 Conclusions and Future Work  
This article introduces a framework which facilitates automatic and autonomous adaptation to application requirements within the network. This ensures the network is capable of delivering the best possible performance for the particular applications objectives. The framework uses different routing protocol and metric types as tools to create alternate communication solutions which elicit certain responses from the network. The framework then chooses the best solution to use given the network environment and the requirements stipulated by the application. The aim of which is to aid in wide-scale industrial IoT network deployment where choosing an appropriate solution for an application in a particular deployment might prove too expensive and time consuming. To achieve this task each solution is evaluated and compared for their compliance to the applications preferences within a certain deployment. With automatic and autonomous behaviour essential, the framework effectively compares the solutions with minimal testing and without human exposure. Central to this concept is the idea that an IoT network can be modelled accurately to allow performance prediction. The article introduces a novel method of modelling the network using the physical features exhibited by a network and implements this with in the framework. Using this modelling technique the expected performance of a solution for each of the evaluation metrics can be determined. Developing pre-built models for use in the framework allows the assessment and comparison of solutions during network runtime. As the models are formed around physical features which are easily extracted from the network, a deployment assessment can be achieved autonomously and without excessive testing within a deployment. The framework introduces a number of novel concepts for IoT communications:  
* Taking advantage of the wealth of network tools already available to provide alternative performance characteristics while considering a macroscopic perspective on how each can facilitate the applications requirements in the network. * Using pre-built network models to allow effective comparison between solutions during runtime. * Proving the use of physical environment for IoT network modelling and determining a set of physical features from which network modelling is possible. Using the modelling technique presented mixed results in terms of prediction accuracy are achieved when comparing the relative performance of evaluation metrics for each solution using a test set in simulation. The framework also presents a strategy for attaining the required features during runtime, matching and choosing the most appropriate solution as per the application requirements and deploying the solution for use in the network. The strength of the framework lies in a compartmental design where each facet can be replaced with an updated or alternative equivalent without compromising the overall framework. The future direction for this work will be focused on introducing real testbed and production network data into the sample data set and refining the model to better characterise the different solutions. The simulations have presented some interesting results. The logical next step is to obtain a rich data set from real networks that display the range of physical features from which solution modelling can be achieved. From this extended data set a more refined model can be created. The framework is designed to be modular so that future performance models can be integrated easily. As well as this an extended rage of solutions will be tested using the same approach to build a set of solutions which are directly compared to each other. this will present a rough guideline for application developers when choosing a solution for a network deployment. This work was supported by Science Foundation Ireland (SFI) under grant number 07/CE/I1147. The authors would like to thank the editor and reviewers for their valuable contributions to the article  
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