Paper Info
Title
A Partition-Based Mobile-Crowdsensing-Enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance
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, that is: 1) scheduling the cleaning tasks of SILs periodically and 2) minimizing the insecticidal efficiency reduction over time. As smartphones are widely available among farmers across the globe, mobile crowdsensing (MCS) for agricultural data collection becomes a cost-effective and efficient solution by integrating participatory sensing based on a large group of individuals. This article 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 subproblems, 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 tradeoff between task cost and insecticidal efficiency and outperform the three selected baseline algorithms.
Year
DOI
Venue
2022
10.1109/JIOT.2022.3175732
IEEE Internet of Things Journal
Keywords
DocType
Volume
Combinatorial optimization,mobile crowdsensing (MCS),path planning,smart agriculture,solar insecticidal lamps maintenance problem,task allocation
Journal
9
Issue
ISSN
Citations 
20
2327-4662
0
PageRank 
References 
Authors
0.34
19
5
Name
Order
Citations
PageRank
Yuanhao Sun111.69
Edmond Nurellari2233.88
Weimin Ding3103.87
Shu Lei42927216.78
Zhiqiang Huo500.34