Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - PSAllocator
T2 - Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints
AU - Wang, Jiangtao
AU - Wang, Yasha
AU - Zhang, Daqing
AU - Wang, Feng
AU - He, Yuanduo
AU - Ma, Liantao
PY - 2017/2/25
Y1 - 2017/2/25
N2 - This paper proposes a novel multi-task allocation framework, named PSAllocator, for participatory sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.
AB - This paper proposes a novel multi-task allocation framework, named PSAllocator, for participatory sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.
KW - Participatory sensing
KW - mobile crowd sensing
KW - multi-task allocation
KW - sensing capability constraints
U2 - 10.1145/2998181.2998193
DO - 10.1145/2998181.2998193
M3 - Conference contribution/Paper
SN - 9781450343350
SP - 1139
EP - 1151
BT - CSCW '17 Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PB - ACM
CY - New York
ER -