## 6 Conclusions and Future Works  
In our work, we use the LDROA to jointly provide smart offloading decisions and optimize resources for each task being offloaded at an edge server. This approach ensures that the total available resource of the MEC server is not lesser than the that of the adjusted resource for each task and that the available resource is greater than zero, thus finding the minimum resource provided by the MEC server for computing the task. The execution task must meet the time constraint of mission-critical delay-sensitive signaling, for which the time constraint is 60 ms, while saving energy on user devices. We can see that the performance of our algorithms is much better than that of the models showcased in other existing work, such as Random Offloading, Load Balancing, and Greedy Latency schemes, in terms of total latency and total cost, which means that the proposed method can both save energy in user devices and obtain optimal total latency for IoT applications. The LDROA can make intelligent offloading decisions and optimize resources for tasks on edge servers. This approach ensures that the total available MEC server resources meet the task requirements and time constraints while saving energy on user devices. Our method achieves superior performance by reducing energy consumption in user devices and achieving optimal latency for IoT applications.  
In our future work, we will extend our simulation based on the two main conditions of different system bandwidths and the transmission power of user devices. We will conduct simulations to evaluate the performance of Lagrange Duality optimization based on the system bandwidth provided by the base station for each user device. We will vary the transmitting power of user devices, the bandwidth capacities, and the congestion condition to analyze how these factors affect the system's overall performance. By extending the simulation to cover different scenarios, we can gain more insights into how the system performs under various conditions and identify potential problems that may affect system performance.  
Conceptualization, C.E. and S.K. ([PERSON]); methodology, C.E. and S.R.; software, S.K. ([PERSON]), I.S. and P.T.; validation, S.R., S.K. ([PERSON]), I.S. and P.T.; formal analysis, C.E. and S.R.; investigation, S.K. ([PERSON]); resources, S.K. ([PERSON]); data curation, S.R, I.S., P.T. and S.M.; writing--original draft preparation, C.E. and S.R.; writing--review and editing, S.R. and S.K. ([PERSON]); visualization, S.R., S.K. ([PERSON]) and S.M.; supervision, S.K. ([PERSON]); project administration, S.K. ([PERSON]); funding acquisition, S.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. RS-2022-00167197, Development of Intelligent 5G/6G Infrastructure Technology for The Smart City), in part by the National Research Foundation of Korea (NRF), Ministry of Education, through Basic Science Research Program under Grant NRF-2020R1 IIA3066543, in part by BK21 FOUR (Fostering Outstanding Universities for Research) under Grant 5199990914048, and in part by the Soonchunhyang University Research Fund.  
**Data Availability Statement:** Derived data supporting the findings of this study are available from the corresponding author on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.