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| Ref/year | Techniques employed | Tool for evaluation | Benefits | Disadvantage |
|
| [9]/2021 | DCP algorithm (distributed algo for convergence to PNE) | MATLAB | A strategy to offload the data using partial offloading technique is offered. | Certain aspects of UAVs, like network coverage and overall energy availability, are not considered. |
| [10]/2021 | Intelligent reflecting surface (IRS) aided MEC | HAP and MEC server are colocated. | The new algorithm can improve system performance. | The cost of energy and the time it takes to complete a task have not been calculated. |
| [11]/2020 | Heuristic-based algorithm | MATLAB | The behaviour of an interconnected cloud-fog-edge architecture was explored in this paper. | The proposed algorithm does not have a rational approach. |
| [12]/2020 | Reinforcement learning (RL) under SDN controller | OMNET++ | The suggested technique aids in load balancing amongst SBS and reduces data communication lag. | There is a problem of balancing various parameters. |
| [13]/2020 | TDO, GTDO (greedy TDO) RG-TDO (reorganise TDO) | Real world data set | Real-world data sets are used to assess the performance of the proposed algorithms. | The suggested solution’s accuracy is not verified. |
| [14]/2020 | BRD (best response dynamics) | | Under many conditions, the overall structure achieves efficiency and effectiveness. | There has never been a study on energy consumption. |
| [15]/2019 | PDO & SPEA2 | Real-time setup | The dependability of the privacy entropy and transmission efficiency is verified through evaluations. | Overhead is not investigated. |
| [16]/2019 | SPEA2 (strength Pareto evolutionary algorithm) | Real-time setup | The proposed solution is dependable, with minimal time consumption and maximum privacy. | Scalability issue is not addressed. |
| [17]/2019 | Graph theory and heuristic method | Simulation | The use of an offloading mechanism can significantly reduce vehicular cellular traffic. | Only one application is allowed. |
| [18]/2019 | FAR (forecast and relay), HSM (hybrid scheme for message replication), UBS (utility-based scheme) | One simulator | The proposed three systems all demonstrate considerable gains in performance. | Complexity of computation high |
| [19]/2019 | HOM (heuristic offloading method) | Total latency, running time, no. of tasks, and data volume | Reduces the time it takes for deep learning assignments to be transmitted | There is no assurance that the components will work together. |
| [20]/2019 | Collaborative data offloading protocol | Custom Python simulator | Reduces the rate of data loss in IoT by a significant amount | The amount of energy consumed has not been calculated. |
| [21]/2019 | DEED (dynamic energy efficient data offloading scheduling algorithm) | Simulation | Energy usage is reduced while task dependability is maintained. | No proper simulation |
| [23]/2018 | AELAO (anchoring effect and loss aversion on data offloading) | Repast | The use of an algorithm can enhance the amount of data offloaded while also increasing involvement. | The method has not been tested in a real-world context. |
| [24]/2018 | Graph theory and heuristic method | Simulation based | The use of an offloading mechanism can significantly reduce vehicular cellular traffic. | Only one application is allowed. |
| [25]/2019 | HIF algorithm (highest water level interval first policy) | Simulation | Emphasis to minimize energy consumption | Lack of an appropriate simulation |
| [26]/2018 | Stabilized green cross haul orchestration algorithm | Network model | The algorithm ensures that workload execution is energy efficient. | The issue of scalability has not been handled. |
| [27]/2017 | Greedy algorithm and two-step algorithm (TSA) | Simulation | The technology reduces bandwidth while also lowering cellular network expenses. | Scalability overhead problem is not considered. |
| [28]/2017 | Algorithm with genetics (greedy first fit heuristic) | Ifogsim | Genetic algo is used to solve the fog service placement problem (FPSS). | The algorithm has yet to be tested in the real world. |
| [29]/2017 | For a single smart device, the TPM offloading technique is used. | Simulation | The algorithm can be used to reduce the total power consumption of storage devices. | Only the NB-IoT system can use this algorithm. |
| [30]/017 | Attractor selection algorithm | Simulation | When compared to Wi-Fi and on-the-spot offloading via a PLC network, the proposed approach provides better performance and scalability. | Communication overhead |
| [31]/2016 | Threshold-based and dynamic-based rate control algorithm | Simulation-based on traces | Algorithms are useful in decreasing computing time. | Data traffic solution confined only to cellular network and not IoT network |
| [32]/2017 | Dynamic programming and heuristic algorithm | Python 2.7 was used to create the simulation. | A simulation analysis reveals that the HA algorithm performs similarly to the DP algorithm. | Theoretically, HA has not been proven to be the best option. |
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