Multi-Sensor Information Fusion in Ocean of Things Based on Improved Adaptive Dempster-Shafer Evidence Theory

The Internet of Things has been used in a variety of industries, including the marine information industry. The Ocean of Things includes measurements of marine environmental information. Due to the variety of sensors and the duplication of measurement tasks, the marine environment data is numerous and redundant. Achieving information fusion computing for multiple measurement devices is an important task for IoT devices. In the absence of human participation, IoT devices need to intelligently calculate the credibility of each sensor information to enable multi-sensor information fusion. This paper presents an improved adaptive DS evidence fusion algorithm. The method calculates the reliability of the sensor data by using multiple sets of sensor measurement data. It enables data level fusion of multisensor information. Ocean temperature measurements are used as experimental examples. The fusion results and the reliability of each temperature sensor are obtained through simulation calculation. By analyzing the experimental results, the reliability and accuracy of the proposed method are proved.