## 3 Proposed Optimization Model  
This section presents a detailed discussion of the proposed optimization model with the integration of SDN architecture and mobile edges. It improves the efficacy of the delay constraint multimedia applications and supports the system in reacting trustworthy in case of unknown objects. Figure 1 depicts the workflow of the proposed model. It is comprised of three main blocks: (i) network sensing; (ii) network edges; and (iii) software-defined network architecture. In the first block, the sensors sense the multimedia data and achieve a QoS-aware algorithm to lower the overheads on constraint resources. The second block offers mobile edges that can collaborate with the sensing layer and SDN controller. It decreases the delay factor while routing the multimedia traffic and offers the delay-tolerant solution. In the last, SDN architecture is utilized to centralize the overall control on the network infrastructure. It not only provides better resources management, but also supports data security with the secret sharing scheme. This block increases the trust among network applications and facilitates the boundary nodes to perform lightweight data encryption with mutual authentication. The proposed model contains three main phases. All of them operate independently and interact with each other to support the network application with an affordable load on the IoT network. The communication of the proposed model is divided into IoT sensing, network edges, and SDN levels. In the beginning, we consider various MIoT sensors to sense and transmit the multimedia traffic using mobile edges. The mobile edges are movable in a fixed radius and have high resources for processing and data storage. MIoT nodes are not able to directly interact with SDN sink node. They can transfer the multimedia data towards the sink node using the nearest mobile edges. In the proposed model, the mobile edges perform a very crucial role in decreasing the excessive response time and improving the delivery performance for delay-tolerant applications. MIoT nodes, before initiating data forwarding, share their statistics, establish forwarding tables to retain up-to-date information in their proximity, and train themselves for optimum outcomes. The information comprises of identities ID, residual energy, distance, and link disturbing. Afterward, they perform an authentication phase to verify with each other, using the certificate tokens. All of the nodes exchange the certified tokens that are signed by the master key \(\mathrm{k_{m}}\) of each node. Upon successful reception of the token, each node marks the entities to their forwarding tables. Moreover, each node forwards its tokens to the nearest edge nodes, so their association can be created in the upper layer. Mobiles edges advertise their identities \(IDs\) and positioning coordinates on a regular interval, so the nearest nodes could detect their latest positions and update the forwarding tables. Moreover, the mobile edges transmit their local tables to SDN controllers using deployed switches and routers. In this way, the control plane gets the updated information of the network layer and manages the resources efficiently for the constraint-oriented devices. Afterward, the proposed model adjusts the data flow among MIoT nodes using intelligent decisions. The source advertises the route request RREQ packet to identify the initiate route for data routing. Upon receiving, the neighbor nodes respond with status information \(n_{s}\) along with their identities \(ID_{i}\). The status information is determined by utilizing the distance \(D_{t}\), received signal strength indicator \(RSSI\), and re-transmission interval \(Rt_{int}\) factors, as given in Equation (1). \[n_{s}=min(D_{t}+Rt_{int})+max(RSSI), \tag{1}\]  
In Equation (2), \(D_{t}\) is the integration of a two-level distance \(dist\), i.e., the Euclidean distance, from the source node to neighbor \(\alpha\) and from neighbor \(\alpha\) to mobile edge \(\beta\), as given below.