## 2 Related Work
### The Task Scheduling and Resource Optimisation in IoT Networks
Executing simulations using MATLAB R2018b, the authors demonstrated the superior performance of their proposed approach compared to traditional methods such as particle swarm optimisation (PSO), firefly algorithm (FA), and Harris Hawks optimisation (HHO) in terms of makespan and throughput. However, for a more comprehensive evaluation, it is imperative for the authors to broaden their focus beyond makespan and throughput. Targeting additional performance metrics, such as task completion success rates and energy consumption, would provide a more holistic assessment of the MH algorithm's efficacy in enhancing IoT network performance. Balancing loads and optimising power consumption are critical challenges in IoT environments, given the vast geographical distribution and sheer abundance of IoT sensors and devices. Ensuring minimal energy consumption by these devices and pinpointing optimal computing resources are crucial endeavours. Addressing these concerns, [PERSON] et al. [8] introduced the dynamic energy-efficient load balancing (DEELB) algorithm. The DEELB algorithm comprises three key phases: identifying nodes' storage capacities, allocating resources, and balancing load. To assess its efficacy, the authors employed CloudSim for validation. The simulation results unequivocally demonstrated the superior performance of DEELB over existing methods such as energy-efficient opportunistic (EEFO), dynamic energy-efficient resource allocation (DEERA), efficient load-balancing security (ELBS), and delay energy balanced task scheduling (DEBTS). Notably, DEELB exhibited advantages in terms of delay, response time, packet loss, reliability, and throughput. However, while having these achievements, it is imperative for the authors to consider energy consumption. Ensuring that the DEELB algorithm operates within sustainable energy usage bounds is paramount, as energy efficiency stands as a cornerstone in IoT network management. By integrating energy considerations into their algorithmic framework, the authors can further fortify the viability and sustainability of DEELB in real-world IoT deployments. Ensuring the optimal activation of IoT sensor nodes is pivotal for maximising the performance of IoT networks. Recognising this, [PERSON] et al. [9] introduced the spatio-temporal scope information model (SSIM), designed to facilitate informed decisions regarding sensor activation by leveraging the spatio-temporal characteristics of valuable sensor data. Through the analysis of space and time parameters, the SSIM enables the system to schedule sensor activation effectively. The experimental findings underscored the efficacy of the proposed SSIM, showcasing improvements in sensor activation scheduling that translated into enhanced overall system sensing accuracy and energy efficiency. However, to further refine their approach, the authors should integrate considerations of task performance alongside sensor activations. This comprehensive approach would offer a more holistic perspective, ensuring that system optimisation efforts address both sensor activation strategies and task execution performance, thereby advancing the effectiveness and efficiency of IoT networks. The influx of data received by fog devices in IoT edge networks can significantly impact system performance, leading to delays and increased power consumption. Addressing these challenges, efficient task scheduling plays a pivotal role. [PERSON] et al. [10] devised a two-step approach, leveraging advanced algorithms to enhance energy efficiency and reduce time delays in IoT networks. Initially, the authors employed particle swarm optimisation (PSO) to achieve optimal load balancing among fog nodes, considering factors such as energy cost and processing time. Subsequently, they designed a particle swarm genetic joint optimisation artificial bee colony algorithm (PGABC) to schedule tasks among fog clusters, building upon the outputs of the PSO algorithm.