scholarly journals Spatial-Temporal Data Collection with Compressive Sensing in Mobile Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2575 ◽  
Author(s):  
Haifeng Zheng ◽  
Jiayin Li ◽  
Xinxin Feng ◽  
Wenzhong Guo ◽  
Zhonghui Chen ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Compressive Sensing ◽  
Mobile Sensor Networks ◽  
Temporal Data ◽  
Mobile Sensor

scholarly journals A Clue Based Data Collection Routing Protocol for Mobile Sensor Networks

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 8476-8486 ◽  
Author(s):  
Guisong Yang ◽  
Huifen Xu ◽  
Xingyu He ◽  
Liping Gao ◽  
Yishuang Geng ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Routing Protocol ◽  
Mobile Sensor Networks ◽  
Mobile Sensor

A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks

2017 ◽  
Vol 8 (3) ◽  
pp. 1-14
Author(s):  
Dongfeng Fang ◽  
Feng Ye ◽  
Yi Qian ◽  
Hamid Sharif
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Data Quality ◽  
Incentive Mechanism ◽  
Mobile Sensor Networks ◽  
Recruitment Strategy ◽  
Easy Access ◽  
Mobile Sensor ◽  
Incentive Mechanisms ◽  
Maximum Utility

Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary tasks for many MSN users. For example, drivers are not required to report traffic condition although their vehicles with advanced sensors have easy access to critical information. Therefore, incentive mechanisms are needed to recruit sensing users (SUs). Incentive mechanisms proposed for traditional MSNs cannot be applied directly due to limited information of SU used for recruitment. In this article, the authors propose a novel cloud-based MSN model that consists of three parties, including data request party, cloud-based platform and SUs. To better utilize information of SUs, a data quality model is proposed to measure the credit level of SUs. The proposed SU recruitment strategy takes into consideration social connections of users. According to the strategy, SUs are divided into two separate levels. Moreover, the authors propose an incentive mechanism using a Stackelberg game theoretical approach to achieve the maximum utility of each recruited SU. The simulation results demonstrate that the proposed incentive mechanism can recruit SUs more efficiently while providing data quality guarantee.

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