scholarly journals Data Collection Method for Mobile Sensor Networks Based on the Theory of Thermal Fields

Sensors ◽  
2011 ◽  
Vol 11 (7) ◽  
pp. 7188-7203 ◽  
Author(s):  
Martin Macuha ◽  
Muhammad Tariq ◽  
Takuro Sato
Keyword(s):  
Sensor Networks ◽  
Data Collection ◽  
Mobile Sensor Networks ◽  
Mobile Sensor ◽  
Collection Method ◽  
Data Collection Method ◽  
Thermal Fields

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

scholarly journals Node-Gosper Island-Based Data Collection Method with Recharge Plan for Rechargeable Wireless Sensor Networks

2018 ◽  
Vol 201 ◽  
pp. 03001
Author(s):  
Chi-Chang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Power Consumption ◽  
Wireless Sensor ◽  
Transmission Method ◽  
Collection Method ◽  
Data Collection Methods ◽  
Data Collection Method ◽  
Collection Methods

Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node-Gosper island-based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used a mobile wireless charger to visit and recharge the batteries of sensors located on different levels of node-Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well-known data collection methods in terms of energy consumption.

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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