Introduction to Energy Management in Wireless Sensor Networks Using Energy-Hungry Sensors Seminar:
This paper discussed about the energy management schemes which addresses the energy saving issues at the sensor level. There are some strategies which help to reduce the power consumption at the radio level by considering data aggregation, data compression, topology management and predictive monitoring.
Duty cycling and adaptive sensing are the two main approaches which can reduce the energy consumed by a sensor. The process of waking up the sensorial system only, when a new set of samples are needed and powering it off immediately after the usage is called Duty cycling. By this strategy, power saving is possible but periodic sensing is typically considered only in few applications, where the process dynamics are stationary. There is a need of adaptive sensing strategy to adapt the sensor activities dynamically to the real dynamics of the process. Both adaptive sensing and Duty cycling are complementary approaches.
Operating system provides a set of primitives for powering on and off the sensors in order to support duty cycle mechanisms. Later, the application uses such primitives to get data as per the (adaptive) sensing strategy it implements. The general framework of Wireless sensor Networks allows the designers to focus on selecting best adaptive sensing strategy by leaving low-level duty cycling aspects to the operating system. Adaptive sensing approach has been further classified into three approaches namely hierarchical sensing, adaptive sensing and model-based active sensing.
Hierarchical sensing: The main assumption of this technique is multiple sensors are installed on sensor nodes and observe different resolutions and power consumption.
Adaptive sensing: Adaptive sampling techniques include the adapting of sensor sampling rate dynamically based on the correlation among acquired data.
Model-based active sensing: Model based active sensing technique works by building a sensed phenomenon abstraction by using a forecasting model.
Power saving is possible with an efficient sensing strategy, by reducing the number of samples and also need to reduce the data to be processed.
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