## 9 Conclusions and Future Work  
This paper introduced a data traffic control scheme over IWSNs for polling-based data collection from multiple IIoT applications and its data collection scheduling. Our algorithm enables the uniform generation of network traffic load for periodical data collection. Polling request packets from a central node to end nodes can be uniform even if the polling cycles are multiple. This is achieved through a data traffic control scheme that generates a polling query at a fixed interval according to a schedule which is decided to make the occurrence of transmitting a polling request for periodical data collection, its retry in case of wireless communication failure or an interruption of higher priority request, and non operation by a GA-based algorithm. At the same time, our scheduler can give maximum opportunities for retransmitting polling requests and data collection ratio of periodic data packets achieves higher than 90%. We adopted a GA based algorithm to create a schedule, but our data traffic control scheme, of course, allows an algorithm that is not GA-based to decide a schedule.  
In this paper, we did not address dynamic adaptation of our scheme to handle dynamic or unexpected changes in application and system requirements. For example, some applications will be installed after the deployment phase. In this case, a scheduler should modify a current schedule. Updating a whole schedule is one of the simplest solutions, but the applications should wait to execute this for at most \(T_{M}\). Moreover, in our scenario, all nodes reply a response packet corresponding to a polling request, but an application such as error log monitoring on an end device may transmit more information (including system log data) than that which can be delivered in a reply packet. Therefore, in order to make the data traffic of all applications uniform, a scheduler should consider both uplink traffic and downlink traffic at the schedule creating phase. We plan to tackle these issues as future work.  
Conceptualization, Y.I., R.N. and N.W.; methodology, Y.I., R.N. and N.W.; software, Y.I.; validation, Y.I., R.N. and N.W.; formal analysis, Y.I.; investigation, Y.I.; resources, Y.I.; data curation,  
Figure 16: Transition of standard deviation slot type value of the top 20 individuals, where \(N_{all}=100\).  
Y.I.; writing--original draft preparation, Y.I.; writing--review and editing, N.W. and R.N.; visualization, Y.I.; supervision, Y.I.; project administration, Y.I.; funding acquisition, Y.I. and R.N.  
This research received no external funding.  
The authors extend their gratitude to all the reviewers for their constructive comments and suggestions, which have significantly improved this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ IIoT & Industrial IoT \\ IWSNs & Industrial Wireless Sensor Networks \\ PA & Process Automation \\ DA & Distribution Automation \\ AMI & Advanced Metering Infrastructure \\ SCADA & Supervisory Control And Data Acquisition \\ TSCH & Time Slotted Channel Hopping \\ PER & Packet Error Rate \\ TDMA & Time Division Multiple Access \\ GA & Genetic Algorithm \\ PSO & particle swarm optimization \\ QF & QueueFrame \\ \end{tabular}