In order to evaluate three different pass under the network density of the same time, under the condition of sparse, ordinary, dense set different number of data packets, Fig. 3 is based on the node distribution of Fig. 2, when the coordinator node use DCA algorithm, AHCA algorithm, and Orchestra method, transmission time changes over passing packet number of rendering. Figs. 3, 4, and 5, respectively, show the comparison time of information transmission of the three algorithms in the case of 20, 50, and 98 nodes. It can be seen from Fig. 3 that the more packets, the shorter transmission time will be if AHCA algorithm is used than DCA algorithm. Meanwhile, the comparison shows that, within a certain range, when the data amount is the same, the higher the network density, the shorter the transmission time.
According to the node distribution in Fig. 2, when the coordinator node uses DCA algorithm, AHCA algorithm and Orchestra method, the time varies with the number of packets transferred. Figure 4 shows the comparison time of the three algorithms with 50 nodes. It can be seen from Fig. 3 that the more packets, the AHCA algorithm will have a shorter transmission time than THE DCA algorithm. When 500 packets are transferred, the AHCA algorithm needs about 105 s, the DCA algorithm 109 s, and the Orchestra algorithm 116 s. Meanwhile, by comparing Figs. 4 and 5, it can be seen that the more nodes in the same range, the faster the data packet is transmitted.
The second experiment—the influence of transmission rate on network performance
To investigate the impact of application-level transmission rates on the reliability, latency, and energy of the scheduling algorithm evaluated, we ran the reference application scenario in a dense network of 98 nodes. At the beginning, necessary time is reserved to ensure the formation of the network, and then the DAG root [17] starts to send requests to the given node. We change the sending interval on the DAG root through different sending time intervals (0.65 s, 1 s, 2 s, 5 s, 10 s), and reruns the experiment. Each experiment is repeated for 5 times, and the average result is shown in the figure below.
Figure 6 shows the average delivery ratio of Orchestra, AHCA, and DCA algorithms. It is observed that when the sending interval is greater than 2 s, these three methods provide high reliability. However, when the sending interval is small, DCA decreases Orchestra by 27% and 28%, respectively, while AHCA reduces by 25%, due to the channel congestion rate caused by high-sending frequency. In fact, at this transmission rate, although the sender has started sending new requests, previous requests are still not being responded. There is a solution to this by increasing the buffer size of the transfer at the expense of increasing the transfer latency, but this solution is affected by node performance and memory capacity.
Comparing the three scheme of PRR, one can find Orchestra and AHCA is more reliable than DCA; this can be explained by the Orchestra and the AHCA increase in the time slot frame the concept of priority, giving higher priority address, higher priority time slot frames, then is also planning a routing priority, allowing the division of priority, network to resynchronize, network transmission and routing maintenance, and DCA not set priority, may lead to a large amount of data of disposable inflows, and such information may be lost due to routing failure.
Although DCA is not as reliable as AHCA and Orchestra, it achieves better latency, as shown in Fig. 7, which can be explained by the small number of time slices for time slot frames in DCA. AHCA has larger time slot frames than DCA, and Orchestra has larger time slot frames, so DCA and AHCA have greater advantages in information transmission than Orchestra.
Finally, Fig. 8 shows the radio duty cycle of the three algorithms. The radio duty ratio of the three algorithms increases with the increase of transmission rate. Although both AHCA and Orchestra have larger time slot frames, they consume less energy than DCA, because most of the time slot intervals are arranged as sleep slots, thus consuming less energy.
The third group of experiments—the influence of channel configuration ratio and information transmission amount on the clogging rate
TSCH network has 16 channels at 2.4 GHz. Ratio and amount of information are needed in order to study the channel configuration for plugging rate. In the case of the number of nodes in a dense network (98), we realized the AHCA algorithm, the relation of Y between channel configuration ratio, different channel configuration ratio, volume change, plug rate formula (10), and corresponding enumerated channel configuration ratio static channel, and obtained the following simulation results as shown in Fig. 9 and 10.
According to Figs. 9 and 10, it can be seen that
(1) In the case of less node information transfer, AHCA congestion rate is generally lower;
(2) In the case of a large amount of node information transmission, the congestion rate of HCA is generally high. When the data amount is about 5M, when Y = 4:0, it reaches its peak at about 46%, while when Y = 0:4, the congestion rate is only 18%.
(3) With the increase of information transmission, the AHCA scheme with large Y has better performance.
(4) The total number of channels is constant, and at this time, there is a uniquely determined Y under different information amounts, which minimizes the network congestion rate.
In view of the above conclusions, as long as the amount of information transmitted in the network is counted at any time, the channel allocation can be adjusted to the optimal value according to the proportion to follow the change of the amount of information transmitted.
It can be seen from Figs. 3, 4, and 5 that the adaptive hybrid channel allocation strategy has made good progress in reducing the information transmission time, and from Figs. 6, 7, and 8 that it has significant advantages over the traditional single TSCH frequency-hopping channel dynamic programming and Orchestra algorithm scheduling in reducing end-to-end delay, improving network reliability, and reducing radio load ratio. It can be seen from Figs. 9 and 10 that AHCA schemes with different proportions of public and private channels exhibit different performance characteristics under different information transmission volumes, so it is sometimes impossible to completely guarantee their good performance under different loads.
It is worth noting that our statistical period should be determined according to the variation of the amount of data transmitted in the channel. When the data transmitted in the channel changes dramatically, we need to set a shorter statistical period.