Sensors are used for providing a system with needed data considering some features of interest in the environment of system. Multi-sensor fusion would provide more accurate and reliable information. Multi-sensor fusion would be beneficial in numerous ways such as timeliness, redundancy, complementarily and so on. The main purpose of the research is to examine the biological sensor performance validation using data fusion technique. The fusion or integration of simulated sensor would minimize overall uncertainty and thus helps to maximize the accuracy. It would provide redundant data and also serve to maximize reliability in terms of sensor failure or error. The implementation would be performed in two phases such as data fusion approach and neural network approach. The code would be executed in the MATLAB. Glucose sensor and sucrose sensor were used as the biological sensor. Fusion method used is the state-vector fusion method and a Kalman filter and H-infinity based filter are implemented for enhancing the performance of data fusion algorithm. Simulate the sensor network and deploy the algorithm of data fusion and use neural network for validating the faulty of the sensor network. From the analysis, it was noticed that when compared to simulated stated sensor output, the simulated fused sensor output and target performs well. It was also observed that error rate also minimal in the simulated fused sensor. Further, Future work would be based on validating the biological and cognitive sensor performance through other fusion models.Key words: multi-sensor fusion, biological sensor, data fusion approach, fused sensor, neural network approach, simulated fused sensor and simulated stated sensor

The biological sensor has played a significant and contributory role in the area of medical science and healthcare industry. Owing to critical healthcare usage, it is essential that such type of sensors should be highly robust, sustainable under the adverse condition and highly fault tolerant against any forms of possible system failure in future. A massive amount of research work has been done in the area of the sensor network. However, works done in biological sensors are quite less in number. Hence, this manuscript highlights all the significant research work towards the line of discussion for evaluating the effective in the techniques for performance evaluation of biological sensor. The study finally explores the problems and discusses it under research gap. Finally, the manuscript gives highlights of the future direction of the work to solve the research gap explored from the proposed review of the existing system.

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Abstract-The energy dissipation in Wireless Body Area Network (WBAN) systems is the biggest concern as it proportionally affects the system longevity. This energy dissipation in the WBAN system mainly takes place due to the signal interference from other networks causing reduction on the dimensionality. The data prediction in WBAN is also a considerable concern corresponding to misinterpretations and faults in the signals. In this paper a novel combination of Principle Component Analysis (PCA) pre-processing along with optimization using the conjugate gradient descent algorithm is proposed. Experimental observations show an improvement in the mean square error and the regression based correlation coefficient when compared to other standard techniques.

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