To achieve URLLC in industrial wireless networks, a priority of the data, which is being transmitted from source to base station through a relay node, is very important. Therefore, two-phase priority aware MAC scheduling (TP-Prior MAC) is considered in which aggregated data is organized for the transmission based on the priority. For this, we propose four substantial processes: cluster formation, optimal route selection, priority-aware scheduling, and optimal antenna selection. Extensive simulations confirm that the TP-Prior MAC method achieves 50% better performance concerning the URLLC latency (end to end). Additionally, the proposed method exhibits a better impact on the throughput, packet delivery ratio, packet loss rate, and energy consumption.
Traditional methods are explicitly programmed, making it difficult for them to dynamically react. This results in the unbalanced allocation of the network resources. To overcome such a scenario, deep Q-learning (DQL)-based resource allocation strategies as per the learning of the experienced trade-offs¡¯ and interdependencies in industrial wireless nodes (IWN) are proposed. This not only finds out the best performing measure to improve the allocation of the resources in fairness manner but also achieves ultra-reliable and low latency IWN.