A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation
Navroop Kaur, Sandeep K. Sood · IEEE Systems Journal · 2015
In the present scenario, performance evaluation of employees in industries is done manually, in which there are ample chances of biases. It is observed that manual employee evaluation systems can be efficiently eliminated by using ubiquitous sensing capabilities of Internet of things (IoT) devices to monitor industrial employees. However, none of the authors have used IoT data for automating performance evaluation systems of employees. Hence, this paper proposes a game theoretic approach for an IoT-based employee performance evaluation in industry. The system infers useful results about the performance of employees by mining data collected by the sensory nodes using the MapReduce model. The information hence obtained is then used to draw automated decisions for employees using game theory. The system is analyzed both experimentally and mathematically. The experimental evaluation compares the proposed system with other techniques of data mining and decision making. The results depict that the proposed system evaluates the performance of employees efficiently and shows a performance improvement over other techniques. The mathematical evaluation shows that correct evaluation of employees by the system effectively motivates employees in favor of the industry. Thus, the proposed system effectively and efficiently automates the employee evaluation system and decision-making process in the industry.