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| References | Algorithm/method | Problem discussed | Benefit/achievement | Drawback/limitation |
|
| [25] | Cloud orchestration approach | Dynamic workflow and coordination of services | Flexible and energy efficient | Data mining and filtering techniques are yet to be analyzed |
| [26] | Data prediction | To minimize energy consumption using data prediction | Energy efficient and provides less error rate | Does not support scalability and QoS |
| [27] | Self-managed sensor cloud | Automation and aggregation of data | Energy efficient and fast response in case of an emergency | Hardware testing is yet to be implemented |
| [28] | Publish or subscribe middleware | Satisfying sensing and removing redundant sensors | Reduction in consumption of energy by 40% to 80% | Data analysis is not performed |
| [29] | Balancing energy consumption with respect to data quality | Managing the quality of the reception data while saving energy | Less energy consumption and QoS is maintained | Not scalable |
| [30] | GEMCloud | To support complex and parallel jobs in the distributed computing environment | Energy efficient | Security is yet to be analyzed |
| [31] | Architecture based on virtual sink | Processing and storing the information through many sinks | Energy efficient, less transmission error, and less end to end delay | Not scalable |
| [32] | Push/pull envelope with lazy sampling | Optimal sampling for transmitting the sensor data between edge and sensor | Energy efficient | Energy-aware scheduling is yet to be implemented |
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