Improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption

The research looks at the problem of receiving a precise estimate of the sensor node attribute from the Wireless Sensor Network (WSN) in a certain time frame while using as little energy as possible from the sensors. An estimate of an attribute may be obtained by taking readings from wake-up sensors connected to a sink node that has been placed at random. The sink must take relevant measurements within a certain time constraint. As an added bonus, the sink reduces the amount of power needed for sensors to transmit their readings. The predicted energy consumption of WSN sensor nodes is presented in a closed-form formulation in this work. The research establishes a maximum allowable sensor transmission distance. The Markov Decision Process may be used to determine the timing of sensor transmissions over a given time frame (MDP). A strategy for transmitting-sensor programming is also developed in this work. The simulation is conducted in MATLAB environment, where the MDP schedule offers reduced energy consumption, reduced delay, increase packet delivery ratio, reduced false alarm rate, which finds the attribute of the data sent via a sensor node.