SDS: Scrumptious Dataflow Strategy for IoT Devices in Heterogeneous Network Environment

Abstract

Communication technologies have drastically increased the number of wireless networks. Heterogeneous networks have now become an indispensable fact while designing the new networks and the way the data packet moves from device to device opens new challenges for transmitting the packet speedily, with maximum throughput and by consuming only confined energy. Therefore, the present study intends to provide a shrewd communication link among all IoT devices that becomes part of numerous heterogeneous networks. The scrumptious dataflow strategy (SDS) for IoT devices in the heterogeneous network environment is proposed and it would deal with all link selection and dataflow challenges. The SDS would accomplish the targeted output in five steps: Step 1 determines the utility rate of each heterogeneous link. Step 2 develops a link selection attribute (LSA) that gauges the loads of network features used for the link selection process. Step 3 calculates the scores of all heterogeneous networks. Step 4 takes the LSA table and computes the network preference for different scenarios, such as round trip time (RTTP), network throughput, and energy consumption. Step 5 sets the priority of heterogeneous networks based on the scores of network attributes. Performance of the proposed SDS mechanism with state of the art network protocols, such as high-speed packet access (HSPA), content-centric networking (CCN), and dynamic source routing (DSR), was determined by conducting a simulation with NS2 and, consequently, the SDS exhibited its shrewd performance. During comparative analysis, in terms of round trip time, the SDS proved that it utilized only 16.4 milliseconds to reach IoT device 50 and was first among all other protocols. Similarly, for network throughput, at IoT device 50, the throughputs of the SDS are recorded at 40% while the rest of other protocols were dead. Finally, while computing the energy consumption used to reach IoT device 50, the SDS was functional and possessed more than half of its energy compared to the 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.

Time based Meticulous analysis of pandemic spreading ratio using Simpy framework

“…This is especially true when it comes to assisting health government agencies worldwide in maximizing the efficiency of medical assistance strategies in the face of such a global disaster (Obaid et al, 2024). In the aftermath of the coronavirus pandemic, however, there were 7818 confirmed cases worldwide, with more than 1370 severe cases and 170 deaths (Rasheed et al, 2022). The majority of it was discovered in China.…”

Data leakage analysis in wireless networks using Super-vised and Unsupervised Testing

“…It is no secret that there has been a growing interest in numerical optimization as it relates to wireless resource allocation in recent years. There are still significant challenges in implementing numerical optimization-based algorithms on practical systems, e.g., high computation costs, despite their ability to solve specific resource management problems with tremendous results [1] As neural networks (NNs) [2], memorize features of example data during training and unintentionally reveal them during prediction, it is a major concern for machine learning applications to prevent models from revealing sensitive input data details. There is, however, no easy way to accomplish this.…”