**4. Performance Analysis**

After the completion of the simulation process, the output results were collected according to the given scenario, and simulation setting parameters are shown in Table 2. These parameters maintain the stability of the execution process, and the fixed values, for instance, number of networks, the specified area distance among the devices, transmission communication range, number of devices for each network, and the transmission rounds, etc., are indeed in real-time perception. The execution process directly depends on these parameters, which might change the output of the results. For instance, transmission starting energy has been fixed to 7 Joules for the entire round. If at some stage this energy level changes, it would definitely affect the results and could cause dysfunction. The NS2 simulator is the best approach for wireless communication when the real-time data transmission would be required in an efficient manager. The proposed SDS model was compared to three state of the art protocols: high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR). The output performance is analyzed on the basis of round trip time (RTTP), network throughput, and energy consumption.



#### *4.1. Computing Round Trip Time*

The RTTP represents the amount of time it takes for a source device to send a request to the destination device and to receive acknowledgment. Usually, this technique is used to determine the health of a network connection. Analyzing the results shown in Figure 4, the overall response time of HSPA, CCN, DSR, and the proposed SDS protocols can be observed more scrumptiously. It can be seen that the network devices are responding quickly and remain active. If these devices take longer, in contrast to the standard time, it means that there are some anomalies that hinder the packet movement as well as the network lifespan.

**Figure 4.** Round trip time computation.

When compared to the rest of the protocols, the SDS takes substantially less time. In reality, our suggested technique uses LSA to identify the best link between the source and the following device. The chosen communication link carried the most energy, but the identical devices coupled with other protocols carried far less energy and were judged inoperable. It produces tangle-free routing and seamless network functioning.

It can be observed that as the number of devices increased, i.e., to 20, the RTTP for CCN somehow became greater, but when approaching device 30, it decreased. This happened due to some unavoidable changes in the routing port, which sometimes blocks the traffic but soon releases. Although HSPA worked better than the other protocols in certain ways, since devices with modest distances were picked more frequently, the data reveal a fast delay when reaching device 50. For DSR, transmission appeared to be more crucial, reducing network lifetime and requiring additional resources to alter response time. It is worth noting that device responsiveness appears to be lower in CCN than in others. The source device continues to deliver data packets until the energy level falls below a certain threshold, putting a heavy pressure on the server. The SDS, on the other hand, changes the momentum during packet transmission and responds quickly. From device 10 to 50, the RTTP time remained less than all other protocols.

#### *4.2. Network Throughput*

Throughput is the rate at which a packet or information is successfully transmitted over a network and recognized by the destination device. Network throughput and the packet dissemination ratio both assess network strength, and throughput is directly proportional to the packet dissemination ratio in general. When the network became denser and the number of devices increased, the extravagant communication load immediately impacted the SDS's performance metrics. In this case, the LSA's judicious link selection considerably influences the throughput ratio. The result shown in Figure 5 vouches for the tremendous achievement made by the proposed SDS as compared to the HSPA, CCN, and DSR. At IoT device 10, the throughput of the SDS reached more than 75%, followed closely by HSPA. When SDS reaches device 50, the amazing results can be illustrated by its throughput maintaining high energy levels while the rest of the protocols are about to lose energy.

**Figure 5.** Proposed SDS network throughput.

#### *4.3. Energy Consumption*

The overall energy consumption by all IoT devices during packet transmission is known as system energy consumption. As the SDS establishes the communication link by considering the predefined values available in the LSA corpus table, only the prudent links are chosen, and the rest of the links are ignored, consequently, it prevents energy wastage and energy is only utilized for the prudent link. In Figure 6, it can be seen that the SDS has only utilized a confined energy level when the link was selected by the IoT device 10 whereas HSPA, CCN, and DSR utilized exorbitant energy. Similarly, at IoT device 50, the SDS still has a substantial energy level while the rest of the protocols' energy is almost empty. Considering the above discussion, it can be concluded that the proposed SDS mechanism has extraordinary performance as compared to the rest the protocols (HSPA, CCN, and DSR). This only became possible due to adopting a prudent LSA technique.

**Figure 6.** Overall system energy consumption.

#### **5. Conclusions**

This study was focused on streamlining the better dataflow mechanism by establishing the prudent communication link among IoT devices for a community based wireless network. The proposed scrumptious dataflow strategy (SDS) has achieved this by developing an LSA data corpus that possessed the pre-defined link parameters. The results were obtained through an NS2 simulator. The output performance of this system has vouched for the statement that was made in the methodology section about the performance. The results were obtained on the basis of a round trip time (RTTP), network throughput, and system energy consumption, and were compared with the results of HSPA, CCN, and DSR protocols. The comparison proved that the SDS performed much better than the rest of the protocols. To compute round trip time, the SDS utilized only 16.4 milliseconds to reach IoT device 50, and was first to do so. Similarly, for network throughput, at IoT device 50, the throughputs are recorded at 40% while the rest of the other protocols died. Finally, when

the energy consumption used to reach IoT device 50 was computed, the proposed SDS was functional and possessed more than half of its energy compared to other protocols. The SDS only utilized 302 joules while the rest of the protocols were about to die as they had consumed all of their energy.

**Author Contributions:** S.A. implemented the idea and designed images and tables. Z.R. solved the topology design problem and wrote the algorithm; N.A.I. accepted the entire module. Comprehensive proofreading has been performed by P.K.B. and E.H.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The associated data are available within the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

