A Partition-based Mobile Crowd Sensing-enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance

Sun, Yuanhao, Nurellari, Edmond, Ding, Weimin , Shu, Lei and Huo, Zhiqiang (2022) A Partition-based Mobile Crowd Sensing-enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance. IEEE Internet of Things Journal . ISSN 2327-4662

Full content URL: https://doi.org/10.1109/JIOT.2022.3175732

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A Partition-based Mobile Crowd Sensing-enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance
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Abstract

Solar Insecticidal Lamps Internet of Things (SIL-IoT) is a new green prevention and control technology for pest management. In the implementation of SIL-IoT to large-scale regions, two practical issues remain to be solved, i.e., i) scheduling the cleaning tasks of SILs periodically; and ii) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, Mobile Crowd Sensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This paper proposes an MCS-enabled framework to address the SIL Maintenance Problem (SILMP) and perform system analysis by considering both the partition structure of farmland and the insecticidal efficiency of SILs. In addition, considering the farmland’s practical natural geographical features, we propose dividing the regions of interest into numerous subareas, where each subarea can be considered a separate partition. Finally, we formulate the SILMP framework as two sub-problems, i.e., path planning and task selection, and propose two different methods to tackle each problem based on the concept of greedy algorithm. Simulation results show that our proposed methods have improved performance in the trade-off between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.

Keywords:Task Allocation, Mobile crowd sensing, Smart agriculture, Path planning, Combinatorial optimization, Solar Insecticidal Lamps Internet of Things
Subjects:H Engineering > H620 Electrical Engineering
H Engineering > H611 Microelectronic Engineering
H Engineering > H661 Instrumentation Control
D Veterinary Sciences, Agriculture and related subjects > D411 Agricultural Pests and Diseases
H Engineering > H600 Electronic and Electrical Engineering
D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
D Veterinary Sciences, Agriculture and related subjects > D470 Agricultural Technology
Divisions:College of Science > School of Engineering
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ID Code:49514
Deposited On:08 Jun 2022 13:11

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