## 2 Literature Review  
[PERSON] et al. [16] efficiently implement resource allocation and compute offloading to reduce request time and energy usage. Reliable processing between IoT devices is approximated through computation offloading and resource allocation. The IoT-based fog-cloud environment reduces the cost and time required for this work. Consequently, the various sets of IoT-based devices experience higher energy consumption. Iterative searching-based job offloading in multi-access edge computing for effective ultra-dense IoT devices was proposed by [PERSON] et al. [17]. In this research, task offloading addresses the complexity of processing and transmission power. Offloading is performed on numerous edge servers to estimate power allocation efficiently. The computer task is identified for energy-conscious task offloading, and the power allocation is estimated. The development of the delay-based allocation of workloads (DBWA) method addresses the problems of environmentally friendly and delay-guaranteed task allocation [18]. The Lyapunov drift-plus-penalty theory generates the latency guarantee in edge cloud systems based on the IoT. Enhancing green computing's energy consumption is the goal of this effort. In the IoT platform, the quality of service is preserved between the edge servers. A dynamic microservices scheduling technique is presented for computational complexity in mobile edge computing. The network delay is reduced by increasing the number of IoT-based services utilized. Scheduling, in addition to queuing, Ref. [19] improves the pace of energy consumption. This paper aims to increase energy efficiency for the best available resources in mobile edge computing. A multiple-input multiple-output non-orthogonal multiple access system was presented by [PERSON] et al. [20] to convey the signal to the access control with high connectivity. This paper aims to enhance ultra-low latency and reliability. Zero-forcing beamforming, a beamforming optimization approach, is introduced for power allocation. The IoT network addresses the best local solution by optimizing the technique. Ref. [21] describes a two-stage dimensional matching method for estimating the joint power control and time allocation platform. Here, the optimal allocation is determined to address the joint choice of channels and peer discovery. This method examines three problem-matching scenarios: linear programming, nonlinear fractional computer programming, and alternating optimization. By matching the priority-based data, this technique reduces its complexity. A three-tier design is suggested, which raises the inquiry from the source and implements scheduling for the IoT devices. Mixed-integer programming is developed with the optimization model to shorten the deadline. The round-robin and scheduling of priorities are estimated using a genetic algorithm. The entire processing is created in [22] and assessed at the specified time. Effective computation offloading and allocation of resources are introduced in [23] to reduce energy usage and request time. Computation offload and resource allocation are estimated for accurate processing between the IoT devices. This approach reduces the time and cost in an IoT-based fog-cloud scenario. As a result, the energy consumption of the various IoT-based devices is rising. [PERSON] et al. [24] proposed a systematic review examining how wireless sensor networks have evolved from simple sensory monitoring to sophisticated, service-oriented applications. By comparing various architectural styles and technological eras, it provides an understanding of how technology has advanced. It also outlines the directions for future research to direct additional creativity and investigation in this quickly developing sector. [PERSON] et al. [25] suggested architecture prioritizes resource provisioning while ensuring the network's quality of service requirements are met. Jitter, latency, and throughput were compared between the SDN network strategy and the conventional method.