Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios
With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in mee...
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| Vydáno v: | Scientific reports Ročník 15; číslo 1; s. 16170 - 24 |
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Nature Publishing Group UK
09.05.2025
Nature Publishing Group Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations. |
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| AbstractList | Abstract With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations. With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations. With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations.With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations. |
| ArticleNumber | 16170 |
| Author | Peng, Qinglan Zheng, Wanbo Li, Xu Ren, Jiaxin Li, Weidong Xia, Yunni Li, Siqi Guo, Kunyin |
| Author_xml | – sequence: 1 givenname: Siqi surname: Li fullname: Li, Siqi organization: Faculty of Science, Kunming University of Science and Technology – sequence: 2 givenname: Weidong surname: Li fullname: Li, Weidong organization: School of Mathematics and Statistics, Yunnan University – sequence: 3 givenname: Wanbo surname: Zheng fullname: Zheng, Wanbo email: zwanbo2001@163.com organization: Faculty of Science, Kunming University of Science and Technology – sequence: 4 givenname: Yunni surname: Xia fullname: Xia, Yunni organization: School of Computer Science, Chongqing University – sequence: 5 givenname: Kunyin surname: Guo fullname: Guo, Kunyin organization: School of Computer Science, Chongqing University – sequence: 6 givenname: Qinglan surname: Peng fullname: Peng, Qinglan organization: School of Artificial Intelligence, Henan University – sequence: 7 givenname: Xu surname: Li fullname: Li, Xu organization: Faculty of Science, Kunming University of Science and Technology – sequence: 8 givenname: Jiaxin surname: Ren fullname: Ren, Jiaxin organization: Faculty of Science, Kunming University of Science and Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40346170$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/JSAC.2016.2545382 10.1109/TIV.2022.3190308 10.1016/j.comnet.2021.108352 10.1109/TNET.2021.3110052 10.1109/MCOM.2018.1700873 10.1016/j.comcom.2022.04.017 10.1109/ACCESS.2017.2665971 10.1109/TWC.2012.113012.120500 10.1016/j.cor.2004.04.016 10.1109/TITS.2022.3178759 10.1109/JIOT.2021.3053283 10.1109/JSAC.2016.2611964 10.1109/GLOCOM.2017.8254550 10.1109/TVT.2022.3227197 10.1109/JIOT.2018.2875246 10.1109/JSAC.2016.2525398 10.1109/JIOT.2023.3240395 10.1109/TVT.2018.2868013 10.1109/ICCC52777.2021.9580313 10.1007/s11227-024-06557-0 10.1109/TWC.2017.2785305 10.1109/TVT.2022.3182378 10.1109/TPDS.2014.2316834 10.1109/TMC.2019.2928811 10.3390/s20113064 10.1109/TNSM.2023.3343290 10.1109/JIOT.2021.3137984 10.23919/cje.2022.00.031 10.1109/MWC.2017.1600321 10.1109/TITS.2021.3099368 10.1109/JSAC.2017.2760160 10.1109/WOCC.2019.8770605 10.1186/s13677-021-00240-y 10.1109/TVT.2020.3022766 10.1109/COMST.2014.2375934 10.1109/MNET.001.1900652 10.1145/3318265.3318276 10.1109/TC.2015.2435781 10.1109/JIOT.2018.2875218 10.1109/JIOT.2022.3188434 10.1109/COMST.2018.2828120 10.1016/j.asoc.2017.12.031 10.1109/TVT.2014.2372852 10.1109/TWC.2018.2820077 10.1038/s41598-024-79464-2 10.1017/9781009489843 10.1109/JIOT.2021.3064186 |
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| Keywords | Mining edge computing Multi-objective optimization Task offloading Resource allocation |
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| References | B Picano (730_CR40) 2023; 21 M Simsek (730_CR1) 2016; 34 L Tang (730_CR25) 2021; 10 Q Zhang (730_CR38) 2022; 9 G Li (730_CR28) 2023; 10 C Su (730_CR35) 2020; 69 F Sun (730_CR4) 2018; 67 730_CR30 L Zhao (730_CR32) 2018; 67 F Wang (730_CR24) 2017; 17 H Zhang (730_CR44) 2023; 6 Y Mao (730_CR20) 2016; 34 X Yang (730_CR26) 2021; 8 Y Wang (730_CR49) 2016; 64 H Zhou (730_CR18) 2018; 64 X Chen (730_CR42) 2017; 24 M Sun (730_CR50) 2022; 72 C You (730_CR21) 2016; 34 JM Valente (730_CR51) 2005; 32 D Chatzopoulos (730_CR8) 2018; 57 S Liu (730_CR13) 2021; 23 L Liu (730_CR47) 2012; 12 730_CR46 X Pang (730_CR16) 2021; 30 X Gu (730_CR6) 2020; 20 C You (730_CR15) 2018; 17 R Roostaei (730_CR27) 2021; 198 S Wang (730_CR2) 2017; 5 X-Q Pham (730_CR43) 2022; 71 L Yang (730_CR33) 2015; 65 R Xie (730_CR5) 2018; 39 Y Sun (730_CR3) 2017; 35 730_CR53 M Qin (730_CR19) 2018; 6 730_CR14 X Cao (730_CR31) 2018; 6 A Mahmood (730_CR29) 2022; 190 SK Panda (730_CR39) 2025; 81 S Zhang (730_CR11) 2020; 34 Z Wang (730_CR17) 2023; 32 L Huang (730_CR52) 2019; 19 L Yao (730_CR36) 2022; 24 X Chen (730_CR45) 2014; 26 D Wang (730_CR37) 2021; 9 O Munoz (730_CR41) 2014; 64 J Liu (730_CR9) 2014; 17 F Jameel (730_CR10) 2018; 20 X Jin (730_CR7) 2024; 14 730_CR23 730_CR22 Q Wang (730_CR48) 2019; 21 P Lang (730_CR12) 2022; 7 D Chen (730_CR34) 2021; 8 |
| References_xml | – volume: 34 start-page: 1757 year: 2016 ident: 730_CR21 publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2016.2545382 – volume: 7 start-page: 783 year: 2022 ident: 730_CR12 publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2022.3190308 – volume: 198 year: 2021 ident: 730_CR27 publication-title: Comput. Netw. doi: 10.1016/j.comnet.2021.108352 – volume: 30 start-page: 327 year: 2021 ident: 730_CR16 publication-title: IEEE/ACM Trans. Netw. doi: 10.1109/TNET.2021.3110052 – volume: 57 start-page: 102 year: 2018 ident: 730_CR8 publication-title: IEEE Commun. Mag. doi: 10.1109/MCOM.2018.1700873 – volume: 190 start-page: 178 year: 2022 ident: 730_CR29 publication-title: Comput. Commun. doi: 10.1016/j.comcom.2022.04.017 – volume: 5 start-page: 2514 year: 2017 ident: 730_CR2 publication-title: IEEE access doi: 10.1109/ACCESS.2017.2665971 – volume: 12 start-page: 288 year: 2012 ident: 730_CR47 publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2012.113012.120500 – volume: 32 start-page: 2905 year: 2005 ident: 730_CR51 publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2004.04.016 – volume: 24 start-page: 12991 year: 2022 ident: 730_CR36 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3178759 – volume: 8 start-page: 11415 year: 2021 ident: 730_CR34 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3053283 – volume: 34 start-page: 3590 year: 2016 ident: 730_CR20 publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2016.2611964 – ident: 730_CR22 doi: 10.1109/GLOCOM.2017.8254550 – volume: 72 start-page: 4887 year: 2022 ident: 730_CR50 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2022.3227197 – volume: 6 start-page: 4188 year: 2018 ident: 730_CR31 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2875246 – volume: 21 start-page: 154 year: 2019 ident: 730_CR48 publication-title: Sustain. Comput. Inform. Syst. – volume: 34 start-page: 460 year: 2016 ident: 730_CR1 publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2016.2525398 – volume: 10 start-page: 12156 year: 2023 ident: 730_CR28 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2023.3240395 – volume: 67 start-page: 11049 year: 2018 ident: 730_CR4 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2018.2868013 – ident: 730_CR14 doi: 10.1109/ICCC52777.2021.9580313 – volume: 81 start-page: 1 year: 2025 ident: 730_CR39 publication-title: J. Supercomput. doi: 10.1007/s11227-024-06557-0 – volume: 17 start-page: 1784 year: 2017 ident: 730_CR24 publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2017.2785305 – volume: 6 start-page: 66 year: 2023 ident: 730_CR44 publication-title: IEEE Trans. Veh. Technol. – volume: 67 start-page: 6533 year: 2018 ident: 730_CR32 publication-title: IEEE Trans. Veh. Technol. – volume: 71 start-page: 10220 year: 2022 ident: 730_CR43 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2022.3182378 – volume: 26 start-page: 974 year: 2014 ident: 730_CR45 publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/TPDS.2014.2316834 – volume: 19 start-page: 2581 year: 2019 ident: 730_CR52 publication-title: IEEE Trans. Mob. Comput. doi: 10.1109/TMC.2019.2928811 – volume: 20 start-page: 3064 year: 2020 ident: 730_CR6 publication-title: Sensors doi: 10.3390/s20113064 – volume: 21 start-page: 1958 year: 2023 ident: 730_CR40 publication-title: IEEE Trans. Netw. Serv. Manag. doi: 10.1109/TNSM.2023.3343290 – volume: 9 start-page: 12588 year: 2021 ident: 730_CR37 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3137984 – volume: 32 start-page: 1 year: 2023 ident: 730_CR17 publication-title: Chin. J. Electron. doi: 10.23919/cje.2022.00.031 – volume: 24 start-page: 64 year: 2017 ident: 730_CR42 publication-title: IEEE Wirel. Commun. doi: 10.1109/MWC.2017.1600321 – volume: 23 start-page: 1616 year: 2021 ident: 730_CR13 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2021.3099368 – volume: 35 start-page: 2637 year: 2017 ident: 730_CR3 publication-title: IEEE J. Sel. Areas Commun. doi: 10.1109/JSAC.2017.2760160 – ident: 730_CR23 doi: 10.1109/WOCC.2019.8770605 – volume: 10 start-page: 23 year: 2021 ident: 730_CR25 publication-title: J. Cloud Comput. doi: 10.1186/s13677-021-00240-y – volume: 69 start-page: 13686 year: 2020 ident: 730_CR35 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.3022766 – volume: 17 start-page: 1923 year: 2014 ident: 730_CR9 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2014.2375934 – volume: 34 start-page: 86 year: 2020 ident: 730_CR11 publication-title: IEEE Netw. doi: 10.1109/MNET.001.1900652 – ident: 730_CR30 doi: 10.1145/3318265.3318276 – volume: 65 start-page: 1440 year: 2015 ident: 730_CR33 publication-title: IEEE Trans. Comput. doi: 10.1109/TC.2015.2435781 – ident: 730_CR46 – volume: 6 start-page: 4330 year: 2018 ident: 730_CR19 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2875218 – volume: 9 start-page: 23224 year: 2022 ident: 730_CR38 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2022.3188434 – volume: 20 start-page: 2133 year: 2018 ident: 730_CR10 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2018.2828120 – volume: 39 start-page: 138 year: 2018 ident: 730_CR5 publication-title: J. Commun. – volume: 64 start-page: 4268 year: 2016 ident: 730_CR49 publication-title: IEEE Trans. Commun. – volume: 64 start-page: 564 year: 2018 ident: 730_CR18 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.12.031 – volume: 64 start-page: 4738 year: 2014 ident: 730_CR41 publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2014.2372852 – volume: 17 start-page: 4104 year: 2018 ident: 730_CR15 publication-title: IEEE Trans. Wirel. Commun. doi: 10.1109/TWC.2018.2820077 – volume: 14 start-page: 27813 year: 2024 ident: 730_CR7 publication-title: Sci. Rep. doi: 10.1038/s41598-024-79464-2 – ident: 730_CR53 doi: 10.1017/9781009489843 – volume: 8 start-page: 12968 year: 2021 ident: 730_CR26 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2021.3064186 |
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| SubjectTerms | 639/705/1042 639/705/258 Algorithms Collaboration Computer applications Decision making Edge computing Energy consumption Genetic algorithms Humanities and Social Sciences Immunoglobulin A Internet of Things Latency Mining edge computing Multi-objective optimization multidisciplinary Resource allocation Resource utilization Science Science (multidisciplinary) Task offloading |
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| Title | Multi-user joint task offloading and resource allocation based on mobile edge computing in mining scenarios |
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