Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/1/14 |
| _version_ | 1797344270467727360 |
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| author | Shengqi Kang Xiuwen Fu |
| author_facet | Shengqi Kang Xiuwen Fu |
| author_sort | Shengqi Kang |
| collection | DOAJ |
| description | The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels. |
| first_indexed | 2024-03-08T10:59:52Z |
| format | Article |
| id | doaj.art-2a1d42b2a04d402b9faf1dc38efcebdf |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-08T10:59:52Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-2a1d42b2a04d402b9faf1dc38efcebdf2024-01-26T16:05:52ZengMDPI AGDrones2504-446X2024-01-01811410.3390/drones8010014Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted MechanismShengqi Kang0Xiuwen Fu1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaThe collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.https://www.mdpi.com/2504-446X/8/1/14blockchaindrone-assisted sample collectionroutingadaptive large neighborhood searchsynchronization |
| spellingShingle | Shengqi Kang Xiuwen Fu Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism Drones blockchain drone-assisted sample collection routing adaptive large neighborhood search synchronization |
| title | Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism |
| title_full | Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism |
| title_fullStr | Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism |
| title_full_unstemmed | Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism |
| title_short | Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism |
| title_sort | blockchain enabled infection sample collection system using two echelon drone assisted mechanism |
| topic | blockchain drone-assisted sample collection routing adaptive large neighborhood search synchronization |
| url | https://www.mdpi.com/2504-446X/8/1/14 |
| work_keys_str_mv | AT shengqikang blockchainenabledinfectionsamplecollectionsystemusingtwoechelondroneassistedmechanism AT xiuwenfu blockchainenabledinfectionsamplecollectionsystemusingtwoechelondroneassistedmechanism |