## 2 Related Work
### The Task Scheduling and Resource Optimisation in IoT Networks  
Managing task and resource management in IoT networks, especially across cloud, fog, and mist computing environments, poses a significant challenge. However, [PERSON] [4] proposed a solution by introducing a multi-level IoT task scheduling (MLITS) model. This model adeptly handles resource allocation for IoT tasks, distributing them across relevant environments based on task deadlines and urgency. Notably, the MLITS model has showcased remarkable efficacy, surpassing other algorithms like Min-Min, credit-based scheduling (CBS), and Earliest-Feasible-Deadline-First (EFDF) when evaluated with CloudSim 3.0.2. It boasts accelerated task completion, reduced wait times, and enhanced throughput. Despite its successes, the MLITS model faces hurdles that warrant attention. One such challenge lies in its limited distribution criteria, particularly its oversight of the diverse capabilities inherent in fog, cloud, and mist computing environments. Moreover, the process of offloading tasks from mist to fog and onward to the cloud can incur unnecessary energy expenditures. A more efficient approach would involve tasks autonomously locating suitable resources, with resource capabilities factored into allocation decisions. Nevertheless, the MLITS model represents a pivotal advancement in resource management in IoT networks, offering a promising trajectory for future solutions. [PERSON] et al. [5] presented the Rao-1 algorithm, devoid of metaphors, aiming to enhance energy efficiency in IoT networks. This algorithm comprises two pivotal phases: firstly, minimising the count of idle microcomputers, and secondly, intelligently assigning tasks to eligible microcomputers. Leveraging Python 3.7, the researchers meticulously implemented the algorithm and conducted a comparative analysis against the MaxMaxUtil algorithm. The findings revealed the superior efficiency of the proposed algorithm over MaxMaxUtil in terms of power consumption and overall performance. However, to further refine their approach, the authors should prioritise workload balance. Ensuring an equitable distribution of tasks across all available microcomputers is essential to alleviate the burden on cloud and fog computing environments while optimising resource utilisation. Energy conservation is crucial in IoT networks, given the real-time processing demands and energy constraints of IoT devices. In this context, [PERSON] et al. [6] introduced a pivotal method known as decisive task scheduling for energy conservation (DTS-EC). This method classifies tasks based on their energy requirements, with the goal of efficiently allocating and queuing data for processing. Validation of the proposed approach was conducted using the Cooja simulator. The findings demonstrated superior performance of DTS-EC compared to existing methods such as scalable energy efficiency scheme (SEES), composition based on concurrent request integration optimisation-particle swarm optimisation (CRIO-PSO), and deep reinforcement learning-based offloading (DRLO) across metrics including data dissemination, energy utilisation, and latency. However, for further refinement, the authors should consider additional factors beyond energy requirements in scheduling, particularly focusing on the accuracy of data arrival and task completion. This broader perspective would enhance the comprehensiveness and effectiveness of the DTS-EC method. Efficient task scheduling in fog and cloud environments is essential for ensuring the productivity and efficacy of IoT systems. In this vein, [PERSON] et al. [7] devised the hybrid meta-heuristic (MH) algorithm to proficiently allocate and schedule tasks across the fog cloud system, with the primary criterion being the minimisation of the makespan. The evaluation of the optimal virtual machine (VM) for task allocation is based on this metric.