scholarly journals Cooperation-Driven Virtual Terminal Coalition Formation Games for Task Assignment in Mobile Crowdsensing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haifei Yu ◽  
Shiyong Chen ◽  
Xiang Liu ◽  
Yucheng Wu
Keyword(s):  
Coalition Formation ◽  
Large Scale ◽  
Task Assignment ◽  
Mobile Terminal ◽  
Sensor Data ◽  
Mobile Crowdsensing ◽  
Cooperative Relationships ◽  
Resource Requirements ◽  
Smart Mobile ◽  
Formation Game

Mobile crowdsensing (MCS) is a popular way of data collection, which forms the large-scale sensing system by smart mobile terminal users and provides multimodal sensor data. In the sensing scenario, there are various sense resource requirements of tasks released by the platform. One of the most urgent issues in MCS is how to choose corresponding users with appropriate sense resources to accomplish assigned tasks. In this article, cooperating among a host of users to perform sense tasks is considered. Firstly, the cooperation among users to accomplish the sense tasks is described as an overlapping coalition formation game (OCF game). In addition, an initial coalition method of using social networks (SN) is proposed to accelerate the formation of coalition. Finally, the cooperation degree is used to describe the cooperative relationships among users, and virtual terminal coalition formation (VTCF) algorithm is proposed to simplify the process of coalition formation. The simulated results show that the proposed approach effectively improves the system’s utility under the constraints of task cost and sense quality.

scholarly journals Differentially Private and Skew-Aware Spatial Decompositions for Mobile Crowdsensing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3696
Author(s):  
Jong Kim ◽  
Yon Chung ◽  
Jong Kim
Keyword(s):  
Large Scale ◽  
Location Privacy ◽  
Task Assignment ◽  
Domain Size ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Uniform Grid ◽  
Mobile Crowdsensing ◽  
Real World Datasets ◽  
Spatial Histogram

Mobile Crowdsensing (MCS) is a paradigm for collecting large-scale sensor data by leveraging mobile devices equipped with small and low-powered sensors. MCS has recently received considerable attention from diverse fields, because it can reduce the cost incurred in the process of collecting a large amount of sensor data. However, in the task assignment process in MCS, to allocate the requested tasks efficiently, the workers need to send their specific location to the requester, which can raise serious location privacy issues. In this paper, we focus on the methods for publishing differentially a private spatial histogram to guarantee the location privacy of the workers. The private spatial histogram is a sanitized spatial index where each node represents the sub-regions and contains the noisy counts of the objects in each sub-region. With the sanitized spatial histograms, it is possible to estimate approximately the number of workers in the arbitrary area, while preserving their location privacy. However, the existing methods have given little concern to the domain size of the input dataset, leading to the low estimation accuracy. This paper proposes a partitioning technique SAGA (Skew-Aware Grid pArtitioning) based on the hotspots, which is more appropriate to adjust the domain size of the dataset. Further, to optimize the overall errors, we lay a uniform grid in each hotspot. Experimental results on four real-world datasets show that our method provides an enhanced query accuracy compared to the existing methods.

scholarly journals Min- k -Cut Coalition Structure Generation on Trust-Utility Relationship Graph

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
XiangLong Kong ◽  
XiangRong Tong ◽  
YingJie Wang
Keyword(s):  
Social Networks ◽  
Coalition Formation ◽  
Large Scale ◽  
Task Assignment ◽  
Coalition Structure ◽  
Structure Generation ◽  
Trust Value ◽  
Data Intensive ◽  
Edge Contraction ◽  
The Impact

Trust relationships have an important effect on coalition formation. In many real scenarios, agents usually cooperate with others in their trusted social networks to form coalitions. Therefore, the trust value between agents should constrain the utility of forming coalitions when cooperating. At the same time, most studies ignore the impact of the number of coalitions in coalition structure. In this paper, the coalition formation of trust-utility relationship in social networks is researched. Each node represents an agent, and the trust-utility networks that connect the agents constrain coalition formation. To solve the task assignment problem, this paper proposes a greedy algorithm which is based on the edge contraction. Under the premise of ensuring the agent’s individually rationality, this algorithm simulates the formation process of coalitions between agents through continuous edge contraction and constrains the number of forming coalitions to k to solve the problem of coalition structure. Finally, the simulation results show that our algorithm has great scalability because of the ability of solving the coalition structure on a large-scale agent set. It can meet the growing demand for data intensive applications in the Internet of things and artificial intelligence era. The quality of the solution is much higher than other algorithms, and the running time is negligible.

scholarly journals Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110131
Author(s):  
Junfeng Wu ◽  
Li Yao ◽  
Bin Liu ◽  
Zheyuan Ding ◽  
Lei Zhang
Keyword(s):  
Anomaly Detection ◽  
Event Detection ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Unified Framework ◽  
Diagnosis System ◽  
Learning Framework ◽  
Task Learning ◽  
Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

Task coalition formation for Mobile CrowdSensing based on workers' routes preferences

2021 ◽  
pp. 100376
Author(s):  
Rebeca Estrada ◽  
Rabeb Mizouni ◽  
Hadi Otrok ◽  
Azzam Mourad
Keyword(s):  
Coalition Formation ◽  
Mobile Crowdsensing

scholarly journals Core in a simple coalition formation game

2001 ◽  
Vol 18 (1) ◽  
pp. 135-153 ◽  
Author(s):  
Suryapratim Banerjee ◽  
Hideo Konishi ◽  
Tayfun Sönmez
Keyword(s):  
Coalition Formation ◽  
Coalition Formation Game ◽  
Formation Game

scholarly journals Using autonomous video to estimate the bottom-contact area of longline trap gear and presence–absence of sensitive benthic habitat

2018 ◽  
Vol 75 (5) ◽  
pp. 797-812 ◽  
Author(s):  
Beau Doherty ◽  
Samuel D.N. Johnson ◽  
Sean P. Cox
Keyword(s):  
Contact Area ◽  
Large Scale ◽  
Marine Protected Area ◽  
Cold Water ◽  
Sensor Data ◽  
Benthic Habitat ◽  
Commercial Fishing ◽  
Depth Sensor ◽  
Quality Of Data ◽  
Bottom Contact

Bottom longline hook and trap fishing gear can potentially damage sensitive benthic areas (SBAs) in the ocean; however, the large-scale risks to these habitats are poorly understood because of the difficulties in mapping SBAs and in measuring the bottom-contact area of longline gear. In this paper, we describe a collaborative academic–industry–government approach to obtaining direct presence–absence data for SBAs and to measuring gear interactions with seafloor habitats via a novel deepwater trap camera and motion-sensing systems on commercial longline traps for sablefish (Anoplopoma fimbria) within SGaan Kinghlas – Bowie Seamount Marine Protected Area. We obtained direct presence–absence observations of cold-water corals (Alcyonacea, Antipatharia, Pennatulacea, Stylasteridae) and sponges (Hexactinellida, Demospongiae) at 92 locations over three commercial fishing trips. Video, accelerometer, and depth sensor data were used to estimate a mean bottom footprint of 53 m2 for a standard sablefish trap, which translates to 3200 m2 (95% CI = 2400–3900 m2) for a 60-trap commercial sablefish longline set. Our successful collaboration demonstrates how research partnerships with commercial fisheries have potential for massive improvements in the quantity and quality of data needed for conducting SBA risk assessments over large spatial and temporal scales.

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