New IoT Technology for Food Shelf Life Management

Cho, Hyun-Woo; Jo, Guk-Han; Song, Young Joon

doi:10.1166/asl.2017.10449

Cited by 4 publications

(1 citation statement)

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“…In 2015, Boulos et al 14 presented a comprehensive survey of food ontology models presented by numerous researchers for identification of automated food and drinks recommendations for better, and healthy food and drink choices based on specific health conditions like age, body weight, and lifestyle. In addition to these, works presented in Zulaika et al 22 (2018), Subramaniyaswamy et al 8 (2019), Chihana et al 6 (2018), Gupta et al 4 (2018), and Cho et al 23 (2017) shows an effective IoT environments for food monitoring and tracking of supply chains in food industry. A detailed comparative analysis with the proposed framework has been shown in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Game theory based framework of smart food quality assessment

Bhatia

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) technology backed by fog-cloud computing has conceptualized the notion of Industry 4.0. Motivated from this, smart restaurants presents a novel vision of provisioning effective food quality assessment based on game-theoretic decision-modeling techniques. This research presents an IoT-inspired framework to analyze restaurants and food-hubs for food quality perspective. Specifically, IoT technology is used to acquire ubiquitous data inside restaurant environment that can impact food quality directly and indirectly. This data is analyzed over the fog-cloud computing platform to formulate a quantification measure in terms of probability of food quality using Bayesian modeling technique. In addition to this, food quality assurance index is estimated to present a unifying factor for food quality assessment over temporal scale. Finally, a three-player game-model is presented for food quality-based decision making by the customers, restaurant managers, and monitoring officials. For validation purposes, the proposed model is deployed in four restaurants of Amritsar, India. The comparative analysis was performed with state-of-the-art decision-making modeling techniques, which showed the superiority of the presented technique in terms of temporal efficiency, statistical performance enhancement, correlation analysis, stability, and reliability. Moreover, a mathematical evaluation has been performed to depict the overall performance enhancement for the proposed decision-making.

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