# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 2 Related Work  
This section provides an overview of studies on clustering techniques in wireless sensor networks and related research. Our method employs group nodes for administration and distributed job execution, including management of resources such as the congestion level of neighboring nodes. Although the primary benefit of clustering systems is lower energy consumption, clustering also addresses network heterogeneity and mobility, load balancing, and energy conservation. One application of cluster formation in WSN, as demonstrated by [11], employs particle swarm optimization and Tabu algorithm techniques. These methods improve energy efficiency, network longevity, and protocol performance by rotating cluster head roles based on energy levels, utilizing multi-hop routing algorithms, and incorporating fault tolerance mechanisms. However, the selection of cluster heads remains constrained by the parameter settings, which can result in elevated energy consumption. Space partition in K-means clustering using optimal K-means was employed in [12] to address area partition by forming space partitions in a wireless sensor network, although the node issuance algorithm remains unclear. A modified grid-based wireless sensor network utilizing the K-means approach [13] has been tested to enhance energy efficiency and extend the lifespan of large-scale wireless sensor networks. This method reduces overall energy consumption and improves network longevity by dividing the network area into grids and applying K-means clustering within each grid cell; however, it may impose limitations on deploying heterogeneous sensor networks that rely on a static parent node. Hierarchical protocols such as Low-Energy Adaptive Clustering Hierarchy (LEACH) reduce the amount of data transmitted across the network by dividing it into clusters with cluster heads responsible for data aggregation and relaying. This protocol conserves energy and improves scalability. Modifying LEACH, as proposed by [14], combines the midpoint algorithm with the K-means approach to enhance network lifespan in wireless sensor networks, aiming to create balanced clusters and reduce cluster head load. Further optimization of this method could improve overall network efficiency and prolong network lifespan, addressing the minor declines in the packet delivery ratio observed in initial tests. As mentioned earlier, LEACH can be adapted to anticipate reduced data transmission, as suggested by [15], where an algorithm was developed to select a primary and secondary cluster head in order to maintain network performance and efficiency while ensuring data flow continuity even in the event of node failure. Another method [16] focuses on improving the average energy consumption per node in each round and the number of surviving nodes per round, although routing algorithms still need to address high latency issues. To minimize frequent reclustering and associated energy overhead, [17] introduced a multi-hop routing algorithm to optimize energy use across the network, incorporating a fault-tolerant mechanism to address CH and relay node failures. However, this experiment's routing process emphasized close transmission distances without considering the need for nodes to be utilized, aiming to minimize energy consumption even if the receiver node remains inactive without any expected packet reception. In [18], the authors employed a joint optimization strategy using the A3C learning algorithm to minimize the system energy consumption of the NOMA cluster while ensuring safe and low-delay computational loading in dynamic vehicle networks. Our research focuses on cell allocation and its impact on wireless sensor network implementation, unlike previous works that typically concentrate on node localization. We examine how node grouping performance affects each cell allocation, particularly in TSCH, as well as its indirect relation to the cell allocation system in WSN, while considering node location, energy efficiency, and network performance.