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References | Algorithm/methods | Problem discussed | Benefit/achievement | Drawback/limitation |
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[7] | E2DAWCS | Network connectivity and sleep scheduling | Less energy consumption | Does not support scalability and QoS |
[8] | Scheduling, data aggregation, and low-power listening | Minimizing the sensed packets for transmission | Energy efficient, reliable, less latency, and scalable | On demand requests for applications is yet to be analyzed |
[9] | TDMA-based scheduling | Scheduling for fine granularity tasks | Provides less response time, high throughput, and energy efficient | Scalability and reliability are yet to be addressed |
[10] | Optimize scheduling of transmission | Dynamic adjustment of clock frequency | Minimizes the energy consumption | Does not support real-time application |
[11] | Task execution | Selecting the favorable sensors | Energy efficient | Does not support load balancing |
[12] | Clustered multichannel scheduling | Multichannel hierarchical scheduling | Provides high throughput, high delivery ratio, and energy efficient | Real-time implementation is yet to be done |
[13] | Real-time thing allocation heuristic | QoS aware selection of service | Less energy consumption | Sporadic service is yet to be supported |
[14] | Dynamic duty cycle scheduling | Scheduling to improve efficiency in WSN | Minimized cost and energy consumption | Does not support real-time cloud applications |
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