# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 2 Related Work
In [8; 25], authors devised a mechanism called DEELB to deal with the IoT resource allocation problem, which considers the vast issues of energy utilization, load balancing, and the cost of computing. The experimental results indicate that the proposed mechanism provides an efficient and effective solution for allocating resources dynamically. [PERSON], [PERSON] et al. suggested a model that implements load balancing at the fog layer to enhance resource utilization. When jobs are initialized in the fog nodes, the scheduling of load takes place, and in fog clusters, load balancing is handled by the local controller [26]. The authors in [27] employed simulation to examine the energy efficiency of two particular routing protocols: the Ad hoc On-demand Distance Vector (AODV) and the Destination Sequence Distance Vector (DSDV). It also contrasted how much power was needed in DSDV and AODV to send the data packets to their destinations. Results showed that AODV was more energy efficient than DSDV because its successful routing activities used less energy. Various state-of-the-art algorithms mentioned in Table 1 deal with a variety of parameters in cloud and fog computing. As IoT devices are generating colossal amounts of data, it becomes very cumbersome to handle that data at nodes on cloud and fog levels. This article presents a novel approach to resolving this issue by integrating IoT, fog, and cloud in a three-tier structure. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Methodology Adopted** & **Features/Parameters** & **Limitation** \\ \hline \multirow{3}{*}{[3]} & Proposed low-complexity techniques for load balancing and cluster formation for fog environment & Quality of experience, network performance, latency & The proposed algorithm is not efficient and works well with large clusters \\ \hline \multirow{3}{*}{[5]} & A framework for optimized resource allocation is proposed and formulated the optimization problem using a genetic algorithm & Latency, optimized resource allocation, privacy, and fault tolerance & Results are computed using a lower number of nodes \\ \hline \multirow{3}{*}{[6]} & Presented an algorithm for allocating tasks using an energy-aware policy & Energy consumption, round-trip delay & Dynamic conditions of the network were not taken into account \\ \cline{2-4}  & Developed TBFC, i.e., tree-based fog computing model & Execution time and total electric energy consumption & Proposed techniques do not consider the imbalanced tree of fog, child nodes, and edge nodes \\ \hline \multirow{3}{*}{[10]} & Fog computing-based energy-efficient hierarchical routing strategies for WSN are proposed & Network lifetime, packet loss, and energy consumption & Lacks optimal resource allocation, and fog node is assumed to be balanced \\ \hline \multirow{3}{*}{[13]} & In fog computing, an energy-aware and load-balanced algorithm is suggested to assign tasks to fog nodes. & Energy consumed, network cost, execution time, and load scheduling & Limited fog devices, and performance was not evaluated under different dynamic scenarios \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of load-balancing techniques used in fog environment.