## 3 Methodology
### Correlated Multi-Objective Energy-Efficient Routing Scheme for SDWSN-IoT
#### 3.2.2 Components and Architecture of the Proposed DOS-RL for the SDWSN-IoT Scheme  
Prior to introducing the architecture of the proposed multi-objective energy efficiency routing scheme, it is essential to understand the components contributing to its functionality. The following subsection provides a comprehensive description of the proposed scheme, as depicted in Figure 2. Given the substantial deployment of a vast number of sensor nodes in IoT networks, an effective solution is needed to address scalability and management challenges. To tackle this, we implement an SDN architecture as the underlying framework for the multi-objective energy efficiency routing scheme. This hierarchical structure ensures optimized decision-making and coordinated communication, thereby facilitating the achievement of energy-efficient routing in a resource-constrained IoT network environment. The proposed architecture consists of four layers: the sensing layer, the data layer, the control layer, and the application layer. The sensing layer comprises various IoT sensing devices that are extensively deployed to gather sensing data. Meanwhile, the data layer is composed of wireless sensor nodes capable of performing a multi-hop transmission. These nodes facilitate the transfer of a substantial volume of data collected by the sensing nodes (SNs) in the sensing layer and relay it to the base station (BS). The proposed architecture incorporates the IEEE 802.15.4 [39] protocol without any modifications to the existing hardware platform, ensuring a seamless integration with the network sensor devices. The control layer of the system is composed of four key modules: the _Topology discovery_, _Network status_, _Flow installation_, and _Device management_ module.The four modules are implemented by the network controller (NC) to constantly monitor the status of relay nodes (RNs) on the data layer. By implementing the SDN architecture, resource-intensive  
Figure 2: Architecture of the proposed DOS-RL routing scheme for SDWSN-IoT. tasks such as routing, data aggregation, and network management are removed from the RNs leaving them with only data forwarding functionalities. By default, the RNs have the capability to sense and collect data from the SNs, and subsequently, forward it to the BS. We suggest that the RNs with residual energy less than a certain threshold should not be part of routing. Furthermore, we adopt a strategy of deploying multiple distributed controllers instead of relying on a single controller framework to enhance control overhead and accelerate the network configuration speed. The application layer of the proposed framework incorporates an intelligent agent that leverages intelligent techniques to transform raw network information into actionable knowledge. The primary objective of our proposed routing scheme is to automate traffic routing based on network information, aiming to achieve multi-objective energy-efficient routing. To accomplish this, our scheme employs the RL to determine the optimal routes for all source-destination pairs, taking into account several correlated link-state metrics related to the energy efficiency of the RNs such as residual energy, estimated link quality, and queue congestion. By leveraging a centralized network view, our routing scheme dynamically adapts to traffic changes and adjusts its routing strategies accordingly. Shaped rewards are employed in the RL to guide the selection of appropriate behaviors for adaptive routing, enabling the effective handling of changes in network conditions. The computed routes are stored in the route repository, accessible to the flow installation module of network controllers (NCs). This proactive approach allows the module to populate the flow tables of the RNs in advance for all anticipated traffic matches. A sample route entry would be represented as follows: (source = n1, destination = n30, next hop = 14, route lifetime = 3 s).