A location-constrained crowdsensing task allocation method for improving user satisfaction
Mobile crowdsensing is a special data collection manner which collects data by smart phones taken by people every day. It is essential to pick suitable workers for different outdoor tasks. Constrained by participants’ locations and their daily travel rules, they are likely to accomplish light outdoo...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719883987 |
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| _version_ | 1850228558546337792 |
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| author | Huihui Chen Bin Guo Zhiwen Yu Liming Chen |
| author_facet | Huihui Chen Bin Guo Zhiwen Yu Liming Chen |
| author_sort | Huihui Chen |
| collection | DOAJ |
| description | Mobile crowdsensing is a special data collection manner which collects data by smart phones taken by people every day. It is essential to pick suitable workers for different outdoor tasks. Constrained by participants’ locations and their daily travel rules, they are likely to accomplish light outdoor tasks by their way without extra detours. Previous researchers found that people prefer to accomplish a certain number of tasks at a time; thus, we focus on assigning light outdoor tasks to workers by considering two optimization objectives, including (1) maximizing the ratio of light tasks for different workers and (2) maximizing the worker’s satisfaction on assigned tasks. This task allocation problem is a non-deterministic polynomial-time-hard due to two reasons, that is, tasks and workers are many-to-many relationships and workers move from different places to different places. Considering both optimization objectives, we design the global-optimizing task allocation algorithm, which greedily selects the most appropriate participant until either no participant can be chosen or no tasks can be assigned. For the purpose of emulating real scenarios, different scales of maps, tasks, and workers are simulated to evaluate algorithms. Experimental results verify that the proposed global-optimizing method outperforms baselines on both maximization objectives. |
| format | Article |
| id | doaj-art-25330604f33c42d9be20709f867f8eb0 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-25330604f33c42d9be20709f867f8eb02025-08-20T02:04:29ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-10-011510.1177/1550147719883987A location-constrained crowdsensing task allocation method for improving user satisfactionHuihui Chen0Bin Guo1Zhiwen Yu2Liming Chen3Foshan University, Foshan, ChinaNorthwestern Polytechnical University, Xi’an, ChinaNorthwestern Polytechnical University, Xi’an, ChinaDe Montfort University, Leicester, UKMobile crowdsensing is a special data collection manner which collects data by smart phones taken by people every day. It is essential to pick suitable workers for different outdoor tasks. Constrained by participants’ locations and their daily travel rules, they are likely to accomplish light outdoor tasks by their way without extra detours. Previous researchers found that people prefer to accomplish a certain number of tasks at a time; thus, we focus on assigning light outdoor tasks to workers by considering two optimization objectives, including (1) maximizing the ratio of light tasks for different workers and (2) maximizing the worker’s satisfaction on assigned tasks. This task allocation problem is a non-deterministic polynomial-time-hard due to two reasons, that is, tasks and workers are many-to-many relationships and workers move from different places to different places. Considering both optimization objectives, we design the global-optimizing task allocation algorithm, which greedily selects the most appropriate participant until either no participant can be chosen or no tasks can be assigned. For the purpose of emulating real scenarios, different scales of maps, tasks, and workers are simulated to evaluate algorithms. Experimental results verify that the proposed global-optimizing method outperforms baselines on both maximization objectives.https://doi.org/10.1177/1550147719883987 |
| spellingShingle | Huihui Chen Bin Guo Zhiwen Yu Liming Chen A location-constrained crowdsensing task allocation method for improving user satisfaction International Journal of Distributed Sensor Networks |
| title | A location-constrained crowdsensing task allocation method for improving user satisfaction |
| title_full | A location-constrained crowdsensing task allocation method for improving user satisfaction |
| title_fullStr | A location-constrained crowdsensing task allocation method for improving user satisfaction |
| title_full_unstemmed | A location-constrained crowdsensing task allocation method for improving user satisfaction |
| title_short | A location-constrained crowdsensing task allocation method for improving user satisfaction |
| title_sort | location constrained crowdsensing task allocation method for improving user satisfaction |
| url | https://doi.org/10.1177/1550147719883987 |
| work_keys_str_mv | AT huihuichen alocationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT binguo alocationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT zhiwenyu alocationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT limingchen alocationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT huihuichen locationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT binguo locationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT zhiwenyu locationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction AT limingchen locationconstrainedcrowdsensingtaskallocationmethodforimprovingusersatisfaction |