Abstract
The rapid growth of population in metropolitan areas has put incremental pressure on urban cities. The centric strategy towards smart cities are expected to cover solution for metropolitan life and ecological environment. One of the significant application areas of IoT in smart cities is the food industry. IoT systems help to monitor, analyze, and manage the real-time food industry in smart cities. In this research, we proposed an IoT based Dynamic Food Supply Chain for Smart Cities which not only ensures the food quality but also provides intelligent vehicle routing as well as tracing sources of contamination in FCM. Furthermore, a smart sensor data collection strategy based on IoT is utilized which would improve the efficiency and accuracy of the supply chain network with the minimized size of dataset and vehicle routing algorithm is introduced and tracing the contamination sources of infected food in the markets. Our proposed model is evaluated with the comprehensive evaluation and used various performance metrics such as tracing accuracy, delay, execution time, and traveling time. The results show that the proposed system outperforms when compared with existing approach.
5. Conclusion
In this paper, we present IoT based Food Supply chain network that efficient trace and track the contaminate food product within supply chain network and also determine the source of contaminated food product. Moreover, we also present dynamic vehicle routing using Bee Colony algorithm to minimize the traveling and execution time during transportation. In the proposed work first, we proposed DPSTT system that trace and track the contaminated food product and root source using Bayesian estimation based dynamic sampling and portioning approach and later trace and track the food product DAG graph and DFS traversal scheme. From result analysis, it is indicated that our DPSTT tracing and tracking algorithm achieve tracing accuracy of 95.3% under minimum sampling rate of 7.6% in comparison to traditional global sampling approach. In addition to outperforming tracing accuracy, we demonstrate comparison analysis of proposed dynamic routing models with existing algorithms including greedy and random allocation based on comparison parameters including execution time, traveling time, and delay. The performance evaluation analysis illustrates that the proposed model suggests the best optimal path that provides minimum traveling time, minimum delay and minimum execution time. In this work, we supposed that all source information of foodstuff is compared by a central repository and these metadata sources are systematized in a uniform way. In future, this work can be extended by (1) performing cloud computation tasks at the network edge itself for more delay optimization and less energy consumption. (2) Real-world application of the provenance of the food supply chain in a community as our testing bed for megacity management.
