## 6 Performance Evaluation  
The proposed AAS is implemented in the Contiki-NG operating system and evaluated using the Cooja simulator, which is a widely used tool for IIoT simulations [34; 35]. A network of 60 nodes arranged in an \(8\times 5\) (\(800\) m \(\times\)\(500\) m) grid is modeled, with one node designated as the root and the remaining as sources. A typical IIoT data collection scenario is considered, wherein the root node serves as the sink for all packets transmitted by the sources, with a maximum hop distance of 6. The packet transmission and interference ranges are both set to 100 m. The simulation is carried out with varying packet intervals from 2 s to 15 s and slotframe size from 31 slots to 101 slots, representing realistic IIoT data collection patterns. To evaluate the proposed technique, key performance parameters such as average end-to-end latency, contention probability, PDR, and network lifetime were analyzed by varying the slotframe size and packet generation intervals. As end-to-end latency tends to increase with slotframe size, smaller slotframes are preferable for time-critical applications. However, smaller slotframes lead to higher energy consumption, as nodes remain active more frequently within a shorter time span. Consequently, for energy-constrained applications, larger slotframes are more suitable, though at the cost of increased latency. Additionally, larger slotframes may lead to a reduction in PDR due to an increased contention probability. With fewer transmission opportunities available in such slotframes, packet queues tend to grow longer, thereby increasing the risk of packet drops. The proposed scheduler minimizes end-to-end latency by ensuring the delivery of packets from source to destination within a single slotframe. An additional slotframe is required only in cases of retransmission. Cells are allocated in successive segments along the route from source to destination, with segment lengths dynamically adapted based on the number of nodes at each level and the traffic they handle. The effectiveness of the proposed AAS has been evaluated and compared with state-of-the-art scheduling approaches, including the node-based autonomous technique Orchestra, the link-based ALICE scheduler, and A\({}^{3}\) integrated with ALICE, as illustrated in Figure 5.  
Unlike these schedulers, AAS models latency as a function of segment length rather than slotframe length. The number of nodes determines the segment length at a given rank and the corresponding link PDR. Orchestra allocates a unique cell to each node within a slotframe, using the sender's or receiver's address. Similarly, ALICE assigns a unique cell to each link in the network. These schemes incur higher latency, as multiple slotframes are required for end-to-end packet delivery. In the worst-case scenario, a cell with time offset \(tok_{l}\) may be allocated for the link \((k\to l)\), while \(tok_{l}+1\) is allocated for the link \((j\to k)\). This hop-to-hop scheduling leads to a maximum delay of \(slotframe_{length}-1\) per hop. Figure 5: End-to-end latency versus slotframe length. Consequently, the worst-case end-to-end latency over a path with hops, \(h\), can approach \(h\) slotframes. A\({}^{3}\) partitions the slotframe into multiple zones based on the traffic load at each node. Initially, it assigns primary cells to every link using the ALICE allocation strategy. When the utilization of primary cells exceeds a predefined threshold, secondary cells are allocated to accommodate the increased traffic. However, the limitation of A\({}^{3}\) is that it allocates primary cells anywhere within a slotframe. When the schedule relies solely on primary cells, the resulting latency is comparable to that of Orchestra. The inclusion of secondary cells in A\({}^{3}\) helps reduce latency by providing nodes with additional opportunities to forward packets. As shown in Figure 5, the proposed AAS technique outperforms existing approaches across all slotframe sizes.