# Coalition Formation among the Cooperative Agents for Efficient Energy Consumption
## 6 Conclusions  
The present study has mainly focused on coalitional value calculation in the coalition formation process, which includes distributional limitations. DCVC algorithm's basic version was used to distribute the coalitional value calculations among cooperative agents. DCVC was modified to reflect variation in the agent's computational speed. After developing the proposed algorithm, it was used to distribute the coalition value among cooperative agents by collecting relevant data from a residential area in Saudi Arabia. The performance of the proposed DNsys was calculated by comparing its results to NNsys. The results showed a reduction in cooling consumption by 20% after applying optimization algorithms. The amount of reduction in the cooling consumption reflects a 31% reduction in expected cooling costs, without affecting the household comfort. Therefore, the study concludes that DNsys provided better performance than the NNsys.  
The need for alignment throughout wireless networks emerges naturally when enhancing the network efficiency. To this end, a number of practical conditions that motivate this study are the collection of information from the sink of wireless sensor networks. In fact, the use of the UAVs system, during the last decade, has been regularly elevated for cooperatively monitoring a predefined area with the use of a single drone. Small UAVs, in such systems, can remotely cooperate, take actions, and make decisions for fulfilling the objectives of a specific mission. Another essential application for WSN is called the distributed data storage, deployed in hostile environments. Sensors have to accumulate, in such application, and retrieve the sensed data until the visit of the mobile sink for collecting it. The proposed framework is also essential for a roadside base station in order to broadcast data to vehicles that can miss some packets because of their high-speed mobility. Lastly, the distributed surveillance camera network is monitoring moving targets into a predefined area with a number of cameras. All cameras are exchanging their own local information regarding each target for recovering all the conditions over the entire network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Agent** & **NNsys** & **DNsys** \\ \hline House 1 & 53 & 26.5 \\ House 2 & 53 & 31.8 \\ House 3 & 53 & 37.1 \\ House 4 & 53 & 10.6 \\ House 5 & 53 & 0 \\ House 6 & 53 & 42.4 \\ House 7 & 53 & 31.8 \\ House 8 & 53 & 5.3 \\ House 9 & 53 & 5.3 \\ House 10 & 53 & 21.2 \\ Mosque & 74.2 & 74.2 \\ Hospital & 346 & 346 \\ TOTAL & 950.2 & 632.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy Distribution in DNsys and NNsys.  
Figure 15: Energy Distribution in DNsys and NNsys.  
**Author Contributions:** Conceptualization, A.A.M.; Formal analysis, M.M.A.L.; Funding acquisition, D.A. and D.A.; Methodology, A.A.M.; Project administration, A.A.M.; Validation, M.M.A.L.; Visualization, M.M.A.L.; Writing D.A.; All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, under grant no. (9-15-1432-HiCi). The authors, therefore, acknowledge technical and financial support of KAU.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data will be available for review from the corresponding author, on request.  
The authors declare no conflict of interest.