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WSN has been used for various applications recently. Many applications like monitoring environments such as measuring temperature, humidity, speed of the wind, and rainfall need the help of WSN. Many physical sensors combine and transmit data wirelessly in the WSN. The cloud system provides storage, infrastructure, and other resources on rent to the users. Many organization uses cloud services to minimize the cost of buying sever and other resources like platform, software, and other services. The Sensor-cloud provides sensing service to the end-users using the cloud systems. The end users can use the sensor, which is attached in the cloud by the sensor owner. The sensor owner gets paid once the users utilize the sensor. By using the virtualization technique, one sensor can be accessed by multiple end-users. Sensor-cloud must utilize energy efficiency due to the limited lifetime of the battery in the sensor node. The cloud system consumes more energy for running the servers in the datacenter.
A sensor node Skˊ uses Pk amount of energy to transmit data to another sensor Skˊˊ with a rate of data transmission Rk. Here 1 ≤ kˊ, kˊˊ ≤ n, and kˊ≠ kˊˊ. Here Pk can be calculated (Guha et al., 2007) as follows:
(1) where:
P1 = the ideal power expenditure of sensor node
SkˊPˊ = constant
Rk = the data rate of sensor
SkˊPrec = minimum energy required for successfully decoding at
Skˊˊd = distance between the sensor nodes
Skˊ and
Skˊˊβ lies between 2 and 6, depending upon the environment.
The consumption of energy in the WSN depends on the data rate and the distance between the nodes. More transmission of data can make the battery dry soon. Generally, user queries are generated fifteen minutes, so the sensor responds to the queries by sending data to the cloud every fifteen minutes. So forecasting schemes that forecast future sensor data for two hours in advance within the cloud system can save energy for the sensor network as sensors send data every two hours to the cloud system.