## 3 Related Work  
Several 6 TiSCH SFs have been proposed in the literature, employing centralized, distributed, and autonomous management approaches. While centralized schedulers are consistent, they introduce higher signal overhead due to the need for network information exchange and connectivity maintenance [14; 15]. Consequently, they limit network scalability and are more suitable for static environments. On the other hand, distributed schedulers [16; 17] are well-suited for dynamic networks, offering lower signal overhead, improved scalability, and enhanced reliability. A Distributed Broadcast-based Scheduling (DeBraS) algorithm [18] has been introduced for dense deployments. DeBraS reduces network traffic during control information exchange, minimizing collisions and improving throughput. Lowering collision rates reduces retransmissions, thereby decreasing latency and enhancing scalability in highly dense networks. In LDSF [19] distributed scheduler, two neighbors negotiate dedicated cells for communication. It divides a slotframe into smaller blocks that repeat over time. It allocates one primary cell in a specified block and several ghost cells for retransmissions in consecutive blocks. The transmitter uses ghost cells when it needs retransmission. This technique maintains the lower end-to-end delay by utilizing smaller block sizes. Distributed schedulers are appropriate for a dynamic network to some extent, as they involve cell negotiation signal overhead between pairs of nodes. DeBraS and LDSF suffer from higher energy consumption since cells are active more often to negotiate with neighbor nodes. The Stripe is a distributed algorithm [20] that minimizes packet delay by placing cells chronologically. It considers node location in the RPL tree and sub-tree weight. The algorithm has two phases: relocation and reinforcement. Relocation moves cells to Stripe along the route, while downward traffic is combined into broadcast cells. The reinforcement phase allocates supplementary cells based on node traffic. Through optimized cell allocation, Stripe achieves faster convergence, minimal delay, and improved PDR. Wave [21] is a distributed scheduler that aims to minimize delays by limiting the cell count in the schedule. The algorithm schedules cells in consecutive waves, allocating them to nodes with packets for transmission. Subsequent waves are optimized versions of the initial wave. Cells are replicated in the next wave for nodes with multiple transmissions. Wave maintains a minimum number of cells, allowing packets to be forwarded from source to root in the same slotframe and ensuring all nodes have enough cells for their own and descendant packets. The third approach, autonomous SF, is more suitable for highly dense networks. Several autonomous scheduling algorithms have been developed that do not comprise central management, information negotiation, signaling, or path resource reservation [22; 23]. Nodes build and manage their schedule locally and autonomously by using their own and/or neighbor MAC addresses. Orchestra [24] is a node-based state-of-the-art autonomous scheduling scheme. It uses different slotframes for the TSCH beacon, RPL control messages, and application data. It uses one common shared slot at a fixed coordinate, [0, 1] of a slotframe for all nodes in the network to transmit and receive control messages. It defines different scheduling mechanisms: Receiver-Based Shared Orchestra (RBSO) and Sender-Based Shared Orchestra (SBSO). In RBSO, any node builds a single Rx slot by using its own MAC address, and Tx cells correspond to each neighbor by using the MAC addresses of neighbors. RBSO may result in a contention problem and packet drop as the node schedules a single Rx cell to receive packets from all the neighbors. In SBSO, a node uses its own MAC address to build a single Tx cell, and the sender's MAC address is used to build one Rx cell per neighbor. This approach offers fewer contentions compared to RBSO, as it employs an Rx cell per neighbor.