Survey of Energy-Efficient Techniques for the Cloud-Integrated Sensor Network

Table 2

Energy-efficient scheduling techniques for the sensor cloud.

References	Algorithm/methods	Problem discussed	Benefit/achievement	Drawback/limitation
[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