## 8 Conclusions  
The implementation of a comprehensive solution for the whole IoT lifecycle was presented in this research. The lifecycle presented fit into four phases: data capture, communication, analysis, and implementation. A generic framework architecture, based on the above layers, was defined in the DevOps paradigm. The architecture complied with the main system requirements of all proposed IoT solutions.  
The proposed IoT solutions were discussed in the building fire prevention sector, to improve building comfort and security. Different challenges were discussed concerning the scalability, updating, and performance of IoT devices with low computing capabilities. The IoT devices were installed in buildings, accessing the building's electrical power resources.  
In the proposed framework for data transmission and management, the nonfunctional requirement (QoS) was considered to address all critical cases to be solved by IoT solutions. It also showed that some computation was possible at the edge side, minimizing the decision time delay.  
Data transmissions were considered by ensuring data transfer in real-time using MQTT-based protocols, along with data packaged in JSON format--which has been considered lightweight--to ensure low network bandwidth consumption. For services, scalability, and reusability, the container-based and microservices solution was proposed, using Docker. It satisfied the horizontal scalability of the framework. The sensor nodes, considered Docker workers for updating and easy connectivity, were under the control of the Docker Swarm manager. Data sent from sensor nodes were managed by the Nginx web server, which also acted as a load balancer in case of the huge amount of user requests coming from the front-end.  
The IoT framework presented in this research was validated and used in a smart building scenario to contribute to fire prevention situations. The system was deployed in the UR-KALSIMBI block site for three months to experiment with the challenges of the data capture process. The performance metrics were extracted to visualize if they were suitable for adoption by the whole University.  
Finally, for future work, the authors will improve the IoT interoperability for disparate and heterogeneous sensor devices, data, and the standard IoT models. Additionally, the  
Figure 24: Performance of the scaled system by 3 nodes: memory and transmissions (Rx, and Tx).  
extensions will concentrate on an advanced custom visualization layer with machine learning techniques included for decision making.  
Conceptualization, E.H. and G.B.; methodology, E.H.; software, E.H.; validation, K.J., R.M. and L.S.; formal analysis, G.B.; investigation, E.H.; resources, E.H.; data curation, K.J.; writing--original draft preparation, E.H.; writing--review and editing, E.H. and G.B.; visualization, E.H.; supervision, G.B.; project administration, G.B.; funding acquisition, E.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by African Centre of Excellence in the Internet of Things (ACEIoT) running under the University of Rwanda, College of Science and Technology (UR-CST).  
Not applicable.  
All data used to achieve the research objectives are available online (ACEIoT portal at the University of Rwanda).  
This research was under the support of the African Centre of Excellence in the Internet of Things (ACEIoT) running under the University of Rwanda, College of Science and Technology (UR-CST).  
The authors declare no conflict of interest.