# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 2 Related Work  
The authors in [3] proposed resource management and small cell cluster establishment of low complexity for fog computing. This article addressed the load-balancing issues by improving the user's quality of service (QoS) in fog computing. In Ref. [5], [PERSON], [PERSON] et al. formulated an optimization problem for resource allocation using a genetic algorithm. The authors have also provided the framework and architecture for resource management, latency reduction, fault tolerance, etc. in fog computing. Authors in [9] developed a tree-based algorithm to uniformly disperse the workload among the fog nodes for fog computing, which progress towards the reduction in energy usage of IoT devices in contrast with the cloud model. The authors in [10] devised a scheme that converges fog computing with an energy-efficient hierarchical routing algorithm in wireless networks. Fog computing has the potential to optimize the available power efficiently. This article also used the ant colony optimization mechanism to find the optimal route for efficient data transmission, leading to energy usage reduction and delay reduction, thus extending the network lifetime. In [11], the authors devised a solution for balancing the load on the Fog of Things platform using SDN. It provides the provision of storing and processing the IoT services on edge devices and optimizing the cloud services. Authors in [12] developed an integrated model of the \"Cloud of Things\" (CoT), which uses fog computing for dealing with delays and energy consumption efficiently. The authors claimed that the proposed energy-aware allocation strategy saves more energy in comparison to the existing methods. In [13], the authors devised an energy-aware load balancing strategy that proficiently distributes the load among fog nodes. Using the simulation, the authors claimed that the power usage of fog and IoT nodes was curtailed significantly. In [14; 15], the authors examined the trade-off between transmission latency and power usage in an IoT-fog-cloud environment. The optimal task allocation problem is formulated to minimize energy consumption with limited service delay. The authors proposed the decomposition of the primal problem into subproblems and solved them individually using optimization techniques to achieve the optimal result. In [16], the authors proposed the utilization of queuing models to investigate the transmission delays, power usage, and cost incurred during offloading tasks in the cloud-fog environment. A multiple-objective strategy is developed to minimize the payment cost, execution latency, and energy usage by finding the optimum probability for offloading and transmitting energy for all nodes. In Ref. [17], [PERSON] et al. devised a strategy for heterogeneous networks to improve green energy utilization and flow level throughput. Similarly, in [18], the authors focused on load balancing for fog environments efficiently and devised a strategy called Dynamic Energy-Efficient Resource Allocation (DEER). In [19; 20; 21; 22], the authors considered the amalgamation of cloud and fog to minimize the resource cost, viz., bandwidth and link level utilization. The authors proposed an optimized strategy that takes into account bandwidth and server resources and provides efficient solutions for the three-layer fog-cloud model. In [23], the authors developed an IoT-oriented router with load metrics and also designed an algorithm for cascade failure for both local and global routing modes. Extensive experimentation and simulation proved that the local routing mode is more reliable in comparison with the global routing mode. The authors in [24] perform an extensive study of cascading failure in edge-assisted IoT. Real-time data packets, congestion status of nodes, and experimental simulation under varied compression ratios provide an optimal solution to prevent cascading failure.