Abstract

Abstract
The rapid expansion of mobile broadband networks and the proliferation of Internet of Things (IoT) applications have substantially increased data transmission and processing demands. However, the application domains of IoT-enabled models often face resource limitations, requiring rapid responses, low latency, and large bandwidth, surpassing their inherent capabilities. To address these challenges, we propose a fishnet approach-based packet scheduling and resource allocation system, termed Fishnet-6G, to optimize network resource allocation in the proposed 6G networks. Initially, we constructed a Sierpinski Triangle-based network in a 6G-IoT environment, enhancing device connectivity. We utilize the Quantum Density Peak Clustering (QDPC) algorithm to perform clustering for IoT devices, establishing Cluster Head (CH) and Substitute CH (SUB CH) based on actual metrics. Furthermore, traffic prediction is achieved through two processes, grouping, and fair queue status, using the Improved Deep Deterministic Policy Gradient (IMPDDPG) algorithm with a variable sampling rate, resulting in well-organized packet scheduling. Subsequently, we perform optimal packet scheduling by employing the Willow Catkin Optimization (WCO) algorithm, and the scheduled packets are managed within a Fishing Net Topology to reduce energy consumption and system complexity. Finally, we allocate the scheduled packets to the desired resource blocks using the Bayesian Game-Theoretic Approach (BGTA). The proposed approach is implemented using Network Simulator-3.26, and the performance of the Fishnet-6G model is evaluated based on time, transmission rate, energy efficiency, average throughput, latency, and Packet loss rate. Numerical analysis demonstrates that Fishnet-6G outperforms existing approaches across these metrics, showcasing its effectiveness in addressing the challenges of 6G-IoT networks.

Introduction

The rapid expansion of IoT devices has led to notable progress in data generation, contributing significantly to the growth of linked objects inside the IoT systems. It is essential to highlight that this development is experiencing a sustained increase as many objects and devices are interconnected in IoT applications [1,2]. As 5G networks continue to improve and expand, concerns arise about their capacity to support the constantly evolving needs of emerging IoT applications fully [3]. The impending era of sixth-generation (6G) mobile communication is marked by the widespread adoption of IoT devices and cellular networks, contributing to a substantial increase in energy consumption and network traffic [4]. The ever-growing demands of intelligent and self-sufficient systems challenge their capabilities and spark the evolution towards the 6G-IoT vision [5]. By 2025, it is projected that there will be over 25 billion IoT devices, putting immense pressure on current multiple access techniques and necessitating the development of B5G wireless systems. In light of this uncertainty, attention has turned towards the potential of 6G wireless technologies to overcome these limitations and propel the transformation of existing networks [6]. Envisioned as a vital component in the development of sustainable smart cities, the 6G-IoT vision incorporates cutting-edge elements such as intelligent edge computing, health analytics, and multidimensional design technology, with a firm emphasis on critical features such as scalability, wireless multi-access, personalized AI, machine learning, cyber security, blockchain, and augmented sensing [3,[6], [7], [8], [9]]. The strategic placement of small cells in a 6G system optimizes coverage and data transmission efficiency, while sensor nodes collect essential information communicated to IoT devices, enabling diverse applications like home automation and healthcare, showcasing the network’s sophistication [10,11]. Network architecture and administration must mitigate complexities in the 6G network to provide exemplary performance. The construction of a 6G network presents numerous advantages, encompassing enhanced capacity, extensive coverage, cost-effectiveness, load balancing, and energy efficiency [12].

Conclusion

This study proposes a fishnet technique to overcome the significant issues related to packet scheduling and resource allocation in the context of the 6G-IoT environment. The primary objective of this study is to implement the Sierpinski triangle-based network construction in a 6G-IoT environment to mitigate the issues of excessive energy consumption and communication overhead. The edge server utilizes QDPC to provide clustering for IoT devices. The suggested study aims to group IoT devices based on relevant metrics. This clustering process involves the identification of cluster heads and sub-cluster heads, which are executed by an edge server. Subsequently, the process of traffic prediction is conducted through two distinct stages, namely grouping and fair queue status assessment, employing the IMPDDPG algorithm with variable sampling rate. Packet scheduling is executed by considering the relevant metrics and employing the WCO algorithm. Following the establishment of a schedule, the management of packets is conducted inside the fishing net architecture, resulting in a reduction in energy consumption, complexity, and overall process weight. The allocation of scheduled packets to the requested resource blocks is accomplished using BGTA, while taking into consideration the operational indicators. The proposed approach was tested using Network Simulator-3.26, and its effectiveness was assessed by comparing its performance to existing methods taking into account various permanence metrics, including time, transmission rate, energy efficiency, average throughput, latency, and packet loss rate. The efficacy of our methodology is examined by quantitative analysis, which substantiates that our technique surpasses existing methodologies across all metrics. The future work will consider cases high availability in cases of Base Station failures and massive IoT V2X environment to support the next generation of wireless networks.