# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 9 Conclusions  
This paper demonstrates the application of node clustering results using the K-means method for cell allocation in the 6 TiSCH network utilizing node position data and segmenting based on OF0 and MRHOF, which influences routing paths and network organization. Our method involves positioning nodes, clustering node coordinates, determining cluster centers, designating centroids as cluster heads, and associating other points with the nearest centers without repeated iterations. The goal is to minimize packet hops in packet routing and reduce neighbor discovery, improving performance efficiency and network traffic stability in the 6 TiSCH network. RX and TX cells are essential for creating an efficient slot schedule in a slot frame, while the system allocates each node a time slot for receiving RX cells and transmitting TX cells. Performance can vary while adhering to the standard scheduling function in the 6 TiSCH network. Utilizing node position patterns allows K-means clustering to optimize resource allocation in order to manage resources based on cluster density. The clustering results determine the number of slots arranged in a time frame, while cell operations are adjusted through 6p transactions using K-means. The algorithm forms clusters of nodes based on proximity, optimizing specific cell allocation. In high-density scenarios, it adds dedicated cells to accommodate increased demand and removes redundant cells in low-density situations. At the same time, the strategy prioritizes allocating unicast cells immediately after the receiving cell, which is intended to minimize latency. Our performance results regarding packet delivery ratio, packet latency, node joining time, throughput, and data transmission success are promising. Evaluations across different node scenarios and comparisons with other clustering methods show significant improvements in average performance metrics. Under the Objective Function Zero (OF0) routing protocol, we achieved a 30.01% decrease in latency, a 15.95% reduction in joining time, an 8% increase in packet delivery ratio, and a 13.82% increase in throughput. Using the Minimum Rank with Hysteresis Objective Function (MRHOF) routing protocol, the average packet delivery ratio increased by 12.34%, latency decreased by 21.06%, joining time decreased by 12.68%, and throughput speed increased by 25.97%. The number of nodes in the network was assumed to be constant in order to maintain the focus on optimizing cell allocation and clustering techniques. In real-world scenarios, node loss from hardware failure, signal weakening, or environmental shadowing can alter network topology. Future research could incorporate models that address dynamic conditions and varying network sizes, or explore hybrid systems that combine multiple clustering techniques involving multi-objective allocation. These advancements could enhance algorithm performance by achieving energy savings through optimal time slot selection and improving scalability for faster response times with the alignment of other objective functions. Conceptualization, F.X.K.K. ; Methodology, F.X.K.K. ; Software, F.X.K.K. ; Implementation, F.X.K.K. ; Validation, F.X.K.K. and S.-H.C.; Formal analysis, F.X.K.K. and S.-H.C.; Investigation, F.X.K.K. and S.-H.C.; Resources, F.X.K.K. and S.-H.C.; Data curation, F.X.K.K. ; Writing--original draft preparation, F.X.K.K. ; Writing--review and editing, F.X.K.K. and S.-H.C.; Visualization, F.X.K.K. ; Supervision, S.-H.C.; Project administration, S.-H.C.; Funding acquisition, S.-H.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2024-RS-2023-00260098) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation).