ENERGY EFFICIENCY ACQUIRED USING STRESS DETECTION ALGORITHM WSN

Computer Science

With recent technological growth in the field of Wireless Sensor Network (WSN), its subset, Wireless Body Area Network (WBAN) has emerged tremendously and transformed the paradigm of medical sector by offering simple, more affordable healthcare services and swapping out pricey wired technology with sophisticated wireless alternatives. WBAN uses tiny sensor nodes that may be positioned inside, outside, or on a patient's body to monitor a variety of health metrics. These sensor nodes are powered by small sized batteries. Small size results in hardware constrains which further limits the lifetime of sensors. Usually, recharging or replacing battery is not feasible due to different deployment and execution scenarios. Therefore, energy efficiency which is capable of extending lifetime of sensor nodes and network becomes one of the major concerns for WBANs. Along with energy efficiency, reliability is also a challenging issue for WBANs as any nonreliable information about the health of the subject can mislead further diagnosis or treatment. For an effective WBAN, energy usage and dependability are therefore important considerations. The possibility for significant improvement of the administration and management of health care. WBANs comprise a variety of biological sensors that are heterogeneous. The sensors are positioned within the body in various places and are wearable, or placed beneath the skin of the person. Each has distinct specifications and are used to perform diverse purposes. The devices are employed to monitor the changes that occur in patients' vital indicators and also for determining emotions or states of human beings like stress, fear or happiness. They are connected to a specific coordinator node that tends to be less constrained in energy and also has greater processing capacities.

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