# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 6 Conclusions and Future Work  
The development of IoT based on LLNs motivated the scientific research work to improve routing through multiple instances. It opened the possibility of bringing the context of multiple applications in IoT with the feasibility of multiple instances and evolving the state of the art with new solutions that consider these devices with limited resources. Related works present multi-instance usage assessments; however, they do not take full advantage of multiple instances towards multiple application scenarios.  
DYNASTI is a solution of multiple instances towards multiple applications, which aims to bring a more flexible and dynamic management of multiple instances, enables the suitability of normal applications while critical applications have priorities in traffic, and coexistence between regular and sporadic applications. In addition, DYNASTI enables coexistence between multiple instances and single/multiple applications competing for available network resources while taking into account each application's set of requirements. Hence, the DYNASTI solution achieves service differentiation by combining all developed mechanisms, such as managing the sending of control and application messages of instances (message controller), scheduling of instances (instance scheduler), using different messaging intervals and, routing metrics, which makes applications more flexible and can free up resources and reduce network overhead.  
The results obtained by DYNASTI showed an overall efficiency improvement for the network, since there was an enhanced optimization of resources, mainly in the reduction of the control messages and energy consumption. DYNASTI results in fewer lost applications' messages and shortest delay for critical applications compared to the state-of-the-art approach in which there is no interruption of control and application messages and scheduling of instances. For future work, it is desirable to run the DYNASTI solution on a real prototype to assess its results with real sensor devices as well as to apply machine learning techniques to instance scheduling.  
[PERSON] developed the work (implementation and testing of the computer code), carried out the simulation assessment (validation), data curation and, writing the article. [PERSON] assisted in the investigation, conceptualization as well as critical review. [PERSON] helped in the writing of the most of the sections and made many suggestions to prepare and/or present this work, specifically critical review and commentaries. [PERSON] assisted with important critical review and commentaries. [PERSON] was the co-advisor that helped to investigate, work on methodology, and write and review the article. [PERSON] was the advisor that worked in the investigation, conceptualization, methodology, and supervision. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Fundacao para a Ciencia e a Tecnologia (FCT) under Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM, NORTE-01-0145-FEDER-000026), Rede Nacional de Ensino e Pesquisa (RNP), and scholarship given by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES-Brazil).  
The authors declare no conflict of interest.