LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Zhou, Ning; Zhang, Jianhui; Wang, Binqiang; Xiao, Jia

doi:10.1186/s13638-019-1610-2

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Additionally, it can reduce the number of sensors to lower the cost of task publishers. Zhou et al [30] proposed a low-cost and balanced task assignment algorithm (LCBPA). The algorithm introduces a trade-off factor and uses a heuristic to avoid selecting nodes where sensing quality cannot be guaranteed.…”

Section: Related Workmentioning

confidence: 99%

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Zhang

2023

Electronics

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In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method demonstrated a 20% improvement in spatial coverage compared with the existing RBR algorithm and outperformed the LCBPA, SMA, and MOTA algorithms in 41, 42, and 48 tasks, respectively. This research contributes to the optimization of task allocation in MCS and advances the integration of edge computing in MCS systems.

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Section: Related Workmentioning

confidence: 99%

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Zhang

2023

Electronics

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“…Ordinary users use their smart devices to obtain data, which are then submitted to the sink node for fusion; the sink node submits the final data to the sensing platform for publication. is method of obtaining information data by ordinary users using their own smart terminal devices is called MCS [5][6][7][8][9][10][11][12][13][14]. Currently, MCS is widely used in many fields, including environmental monitoring [15], traffic conditions [16], and medical health [17].…”

Section: Research Backgroundmentioning

confidence: 99%

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

Chen

Liu

et al. 2022

Journal of Electrical and Computer Engineering

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With the increased awareness of environmental protection, people have higher requirements for the accuracy of environmental information of surrounding life. The current monitoring of urban environmental information mainly comes from local environmental weather stations. Although the monitoring equipment of environmental weather stations is better than personal monitoring equipment, the monitoring equipment of weather monitoring stations is too expensive and only suitable for large-scale coarse-grained monitoring. Because the environmental information of a city is affected by factors such as landforms, buildings, rivers, factories, population density, and traffic flow, there are great differences in the environmental information of different areas in a city. Therefore, this study proposes a method that can be used for small-scale and fine-grained environmental information monitoring: the task grid-based urban environmental information release mechanism for mobile crowd sensing (MCS). Through this mechanism, the monitoring area is divided into different task grids according to the characteristics of the area, and the environmental information is sensed by mobile crowd sensing. For the sensing data, through an efficient data fusion algorithm designed in this study, the sensing information is fused to obtain the fine-grained environmental information of different task grids in the area. Through the use of this mechanism, differentiated environmental information can be provided to users in different areas of the city. In a simulation, this mechanism showed higher information accuracy than traditional information release methods. Thus, the mechanism is scientific and has good application value.

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“…After collecting data using the smart devices of many common users and then submitting the sensing data to a sensing platform for processing, we can obtain the final data from the sensing platform. This method in which data by common participants using their own smart terminal devices is called a mobile crowd-sensing system (MCS) [5][6][7][8]. In applying an MCS to urban inland river water quality monitoring, the cost is low, the coverage of detection is wider, and the water quality of the entire river can be more accurately obtained in comparison to the detection device method.…”

Section: Introductionmentioning

confidence: 99%

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

Chen

Mao

2023

Wireless Communications and Mobile Computing

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The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.

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LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Cited by 3 publications

References 21 publications

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Task Grid-Based Urban Environmental Information Release Mechanism for Mobile Crowd Sensing

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

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