Snapshot/Continuous Data Collection capacity for large-scale probabilistic Wireless Sensor Networks

2012 ◽  
Author(s):  
Shouling Ji ◽  
Raheem Beyah ◽  
Zhipeng Cai
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Wireless Sensor ◽  
Continuous Data

scholarly journals A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1432 ◽  
Author(s):  
Chuan Zhu ◽  
Sai Zhang ◽  
Guangjie Han ◽  
Jinfang Jiang ◽  
Joel Rodrigues
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Mobile Sink ◽  
Wireless Sensor ◽  
Data Collection Strategy

scholarly journals Designing Constrained Trajectory Based on Maximizing Energy Reduction in Large-Scale Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Xu ◽  
Chuan Ping Wang ◽  
Hua Dai ◽  
Da Qiang Zhang ◽  
Jing Jie Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Mobile Sink ◽  
Energy Reduction ◽  
Wireless Sensor ◽  
Movement Trajectory ◽  
Computation Efficiency

TheMobile Sinkbased data collection in wireless sensor network can reduce energy consumption efficiently and has been a new data collection paradigm. In this paper, we focus on exploring polynomial algorithm to compute the constrained trajectory of theMobile Sinkfor data collection. We first present a universal system model for designing constrained trajectory in large-scale wireless sensor networks and formulate the problem as theMaximizing Energy Reduction for Constrained Trajectory(MERC) problem. We show that the MERC problem is NP-hard and design an approximation algorithm (CTMER), which follows the greedy approach to design the movement trajectory of theMobile Sinkby maximizing theeffective average energy reduction. Through both rigid theoretical analysis and extensive simulations, we demonstrate that our algorithm achieves high computation efficiency and is superior to otherMobile Sinkbased data collection methods in aspects of energy consumption and network lifetime.

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