## 4 Results
### Results of Individual Studies and Synthesis
#### 4.1.3 Resource Allocation and Scheduling Strategies  
Research on resource allocation and scheduling in IoT networks has examined various strategies to optimise task management, aiming to reduce energy consumption while meeting Quality of Service (QoS) requirements. By implementing adaptive scheduling and resource allocation mechanisms, these studies address the challenge of managing computational resources across cloud, fog, and edge environments in IoT systems. * [PERSON] et al. [29] introduced the Energy-Aware Fog Resource Optimisation (EFRO) model, designed to allocate computing resources based on workload demands. The EFRO model considers energy efficiency alongside QoS requirements, prioritiising tasks based on both their energy demands and the service quality they require. This approach enables fog nodes to dynamically adjust their resource usage, optimising energy consumption without compromising performance. By minimising energy usage through strategic workload distribution, EFRO demonstrates the potential of resource-aware models in achieving a balanced, energy-efficient IoT environment that still meets end-user demands for reliability and responsiveness. * [PERSON] et al. [30] compared the energy efficiency of containers versus virtual machines in fog computing environments. Containers, which are more lightweight than virtual machines, showed improved energy performance by reducing service delays and enhancing resource utilisation. The study concluded that containers, due to their lower overhead and faster deployment times, are more suitable for IoT applications where rapid, efficient resource allocation is needed to handle fluctuating workloads. This comparison highlights the advantage of containerisation in fog computing, which supports IoT networks by allowing for more flexible and efficient use of computational resources, ultimately reducing overall energy expenditure. * [PERSON] et al. [31] applied a metaheuristic algorithm for task scheduling in Industrial IoT (IIoT) applications to improve QoS and reduce energy consumption. This algorithm dynamically allocates tasks based on environmental factors and resource availability, allowing the system to adapt to changing demands in real time. The study demonstrated that metaheuristic scheduling provides a robust approach to task management, especially in complex industrial environments where workload patterns are highly variable. By tailoring task allocation to specific contexts, this strategy minimises unnecessary energy use while ensuring that critical tasks are prioritised, making it highly applicable for IIoT settings where both efficiency and performance are critical. * [PERSON] et al. [32] proposed a dual-algorithm approach combining ant mating optimisation with an optimised distribution mechanism for scheduling tasks. This hybrid approach proved effective in fog environments, significantly decreasing processing time and reducing energy consumption by managing tasks in a manner that balances load across available resources. The ant mating optimisation algorithm mimics the natural process of mating in ant colonies to select optimal paths for task distribution, allowing the system to adaptively respond to workload fluctuations. This dual-algorithm strategy shows promise in reducing energy usage in fog computing by allocating tasks to nodes in a way that minimises both computational delay and power consumption. * [PERSON] et al. [33] developed a service placement policy tailored for cloud and fog environments, designed to reduce response times and conserve energy by prioritising specific service categories in resource allocation. This policy categorises tasks based on their resource intensity and urgency, allocating higher priority to services that are time-sensitive while conserving energy by delaying non-urgent tasks. By optimising service placement across cloud and fog layers, this approach enhances the overall energy efficiency of the network.