Research / Research News / A robust approach for evaluating scalable ranking vectors of IoT farming methods via multi-sensor estimation
Dr. Nahia Mourad, Assistant Professor, Faculty of Engineering and IT, published a paper titled, ‘A robust approach for evaluating scalable ranking vectors of IoT farming methods via multi-sensor estimation’.
This study examines how the Internet of Things (IoT) is transforming smart agriculture by enabling real-time monitoring, data-driven decision-making, and improved crop productivity. However, evaluating different IoT-based farming methods remains challenging due to the variety of sensors, expert opinions, and decision-making criteria involved.
This study introduces a new three-phase evaluation framework to assess 23 IoT farming methods across nine sensor-based criteria, using input from 15 domain experts. The framework combines advanced expert-weighting techniques, multiple decision-making methods, and machine learning-based clustering to generate more reliable and interpretable rankings.
The results demonstrate that sensor weighting can significantly influence which farming methods perform best. While different weighting scenarios identified different top-performing methods, both produced a high level of consensus and consistently identified the same three methods as the weakest performers. The study also found CODAS to be the most stable decision-making technique under one of the evaluated scenarios.
Overall, the research demonstrates how expert-based weighting, large-scale evaluation, and intelligent clustering can support more trustworthy assessments of IoT-enabled smart farming technologies. The proposed approach also has potential applications in other complex, sensor-intensive decision-making fields.
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