Application of Machine Learning Algorithms and Intelligent Sensor Networks in Crowdsourcing Pricing Models for Biotechnology Innovation

Abstract

The emergence of the sharing economy has transformed traditional business models, introducing innovative approaches such as crowdsourcing and crowdfunding that leverage network-based collaboration to enhance resource sharing and operational efficiency. In biotechnology, these models hold significant potential for driving innovation and reducing costs, particularly through the application of intelligent technologies. Crowdsourcing extends traditional outsourcing by enabling collaborative innovation at scale, utilizing dynamic and participatory networks to engage users in solving complex problems and generating value. This study explores the application of machine learning algorithms integrated with intelligent sensor networks in constructing crowdsourcing pricing models tailored for biotechnology-driven innovation. The research investigates dynamic group construction algorithms within participatory sensing systems, proposing three distinct crowdsourcing models based on the characteristics of participatory sensing applications. It examines the impact of mobility, social attributes, and opportunity contexts on swarm construction and designs methods for building credible swarms using user reputation evaluation models. Dynamic pricing algorithms are developed, incorporating factors such as data types, user profiles, and reputation metrics to construct effective and trustworthy user swarms. User incentive strategies are introduced to optimize engagement and participation in these crowdsourcing ecosystems. To validate the proposed methods, an experimental participatory sensing system comprising 100 smartphones and 100 sensor nodes was developed. The effectiveness of the designed algorithms was verified, and their performance was evaluated under real-world conditions. The results demonstrate that machine learning algorithms combined with intelligent sensor networks can significantly improve the efficiency, accuracy, and scalability of crowdsourcing pricing models in biotechnology. This approach provides a robust framework for fostering innovation, optimizing resource allocation, and enhancing collaborative problem-solving in biotechnology commercialization efforts.