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| Reference | Solution | Advantages |
|
| [33] | A workload-balancing scheme associating the appropriate base stations with user devices | Minimizes the data flow latency for data communication and task processing |
| [34] | An adaptive computation offloading method for the prospective 5G-driven IoCV | Optimizes the task response time and resource utilization efficiency with the optimal solution obtained by utility evaluation |
| [35] | A delay estimation framework for IoT based on fog | Accurately predicts the end-to-end delay in cloud-fog-things continuum |
| [36] | A data placement strategy for fog architecture | Solves data layout problem with generalized assignment problem and develops two solutions |
| [37] | A smart city network architecture based on fog | Divides the communication between devices into three categories to satisfy QoS |
| [38, 39] | Detection algorithms and fog-based medical information systems | Detects individual falls in time |
| [40] | A medical cyber-physical system supported by fog computing and a heuristic algorithm with two phases | Minimizes communication time by optimizing resource utilization |
| [41] | A new nature-inspired smart fog architecture | Provides adaptive resource management and low decision latency by simulating the function of the human brain |
| [42] | A task offloading strategy to reduce service latency | Employs fog-to-fog communication and share workload |
| [43] | A clustering method for offloading | Groups user devices and nodes and uses a matching game to minimize computing delay |
| [44] | An aggregated software defined network and a fog/IoT architecture | Reduces the impact of packet blocking on QoS delivery through more fine-grained control |
| [45] | A greedy scheduling algorithm based on knapsack | Allocates resource to fog nodes considering various network parameters, optimizing time delay and energy consumption |
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