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Mobile crowd sensing (MCS) is a popular sensing paradigm that recruits participants carrying sensing devices to collect data for specific tasks in a sensing area. In an MCS system, when a participant executes limited multiple location-based tasks according to different task accessing sequences, its executing time which depends on its travelling path is different. Therefore, how to perform an efficient path planning to find the optimal executing time for each participant is of vital importance. In this paper, we revisit the path planning problem in MCS and propose a novel path planning method based on the beetle swarm optimization (BSO) algorithm. First, a participant executing time is defined as the sum of the moving time along its travelling path and the sensing time for executing all tasks on this path. Then, to minimize it, an improved BSO algorithm is utilized to find the optimal path for each participant. In particular, a largest-order-value (LOV) rule is introduced to convert each position of beetles from a multi-dimensional vector into a travelling path for the participant. The simulation results verify that the proposed method is superior to other baselines in terms of the participant executing time and shows a faster convergence speed. IEEE

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost. IEEE

Mobile Crowd Sensing (MCS) is an emerging sensing paradigm which employs mobile devices, carried by participants, to sense real-time information. Most previous studies on task allocation in practical applications lack a way of calculating real participant density and making a detailed analysis of it. In this work, first, a fuzzy logic control method is employed to obtain the participant density in different time and space based on participants’ travel time and space. Further, according to the participant density, we can calculate the effective quantity of samples a task requires in a specific time and space. Then, the utility of all tasks can be obtained by considering both the attributes of tasks and the participant-side factors. Last, a hybrid greedy algorithm is proposed to allocate both urgent and non-urgent tasks to ensure the urgent tasks can be allocated as soon as possible and to maximize the utility of all tasks. The simulation results show that the proposed hybrid greedy algorithm is superior to other baselines in terms of the utility of all tasks. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost. IEEE

Self-powered devices and systems that operate by harnessing environmental mechanical energies including raindrops and body motions have been extensively explored owing to their promising applications. In practical applications, these devices are often exposed to humid conditions or directly contact aqueous solutions. Here, we report the development of chemically inert and superhydrophobic electrode based on fluoropolymer (FP)/carbon-nanotube (CNT) that circumvents undesired metal electrode corrosion, deformation, and damage in harsh environments. The electrode surface can be patterned on flexible surfaces via direct ink writing (DIW), and no damage or corrosion is detected even being bent 10,000 times or immersed into salt/acid/alkaline solutions for 20 h. The integration of such robust electrodes with hydrophobic tribo-materials enables the construction of droplet-based electricity generators (DEGs) that exhibit an instantaneous current and power outputs of 2 mA and 0.12 W, respectively, and that light up 50 LEDs by one water droplet. Self-powered touch sensing function is also demonstrated on FP/CNT-based electronic skin, offering the broad applicability of the proposed electrode preparation strategy for self-powered devices.

As a key research issue in mobile crowdsensing (MCS), recent studies on task recommendation have begun to focus on recommending tasks to participants according to the learned participant preferences. The common drawbacks of these studies are that, on the one hand, the factors affecting participant preferences are predefined, which is not practical as the influential factors are quite complex and a full map of participant profiles needs to be preexisted. On the other hand, they do not consider how to update the recommendation dynamically. To overcome these drawbacks, a profile-free and real-time task recommendation method is proposed in this work. First, we apply the recommendation systems to MCS to realize profile-free task recommendations. Second, a participant-task-location tensor is constructed, based on which an improved tensor factorization method is presented to provide task recommendations for participants at a given location. Finally, we design a real-time update algorithm based on the idea of one update at a time to update task recommendation lists for participants in real time. Based on real-world trace data sets, extensive evaluations show that the proposed method has obvious advantages over other baselines in terms of accuracy and time cost.

Mobile Crowd Sensing (MCS) is an emerging sensing paradigm which employs mobile devices, carried by participants, to sense real-time information. Most previous studies on task allocation in practical applications lack a way of calculating real participant density and making a detailed analysis of it. In this work, first, a fuzzy logic control method is employed to obtain the participant density in different time and space based on participants’ travel time and space. Further, according to the participant density, we can calculate the effective quantity of samples a task requires in a specific time and space. Then, the utility of all tasks can be obtained by considering both the attributes of tasks and the participant-side factors. Last, a hybrid greedy algorithm is proposed to allocate both urgent and non-urgent tasks to ensure the urgent tasks can be allocated as soon as possible and to maximize the utility of all tasks. The simulation results show that the proposed hybrid greedy algorithm is superior to other baselines in terms of the utility of all tasks. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

Vegetation is the natural linkage connecting soil, atmosphere and water. It can represent the change of land cover to a certain extent and serve as an indicator for global change research. Methods for measuring coverage can be divided into two types: surface measurement and remote sensing. Because vegetation cover has significant spatial and temporal differentiation characteristics, remote sensing has become an important technical means to estimate vegetation coverage. This paper firstly uses U-net to perform remote sensing image semantic segmentation training, then uses the result of semantic segmentation, and then uses the integral progressive method to calculate the forestland change rate, and finally realizes automated valuation of woodland change rate.