Energy-Efficient Dual Prediction-Based Data Gathering for Environmental Monitoring Applications

2007 ◽  
Author(s):  
Guojun Wang ◽  
Huan Wang ◽  
Jiannong Cao ◽  
Minyi Guo
Keyword(s):  
Environmental Monitoring ◽  
Energy Efficient ◽  
Data Gathering ◽  
Monitoring Applications

scholarly journals Adaptive WSN Scheduling for Lifetime Extension in Environmental Monitoring Applications

2011 ◽  
Vol 8 (1) ◽  
pp. 286981 ◽  
Author(s):  
Jong Chern Lim ◽  
Chris Bleakley
Keyword(s):  
Environmental Monitoring ◽  
Network Lifetime ◽  
Data Gathering ◽  
Temporal Correlation ◽  
Research Problem ◽  
Monitoring Applications ◽  
Efficient Sampling ◽  
Real World Datasets ◽  
Application Specific ◽  
Sampling Schedule

Wireless sensor networks (WSNs) are often used for environmental monitoring applications in which nodes periodically measure environmental conditions and immediately send the measurements back to the sink for processing. Since WSN nodes are typically battery powered, network lifetime is a major concern. A key research problem is how to determine the data gathering schedule that will maximize network lifetime while meeting the user's application-specific accuracy requirements. In this work, a novel algorithm for determining efficient sampling schedules for data gathering WSNs is proposed. The algorithm differs from previous work in that it dynamically adapts the sampling schedule based on the observed internode data correlation as well as the temporal correlation. The performance of the algorithm has been assessed using real-world datasets. For two-tier networks, the proposed algorithm outperforms a highly cited previously published algorithm by up to 512% in terms of lifetime and by up to 30% in terms of prediction accuracy. For multihop networks, the proposed algorithm improves on the previously published algorithm by up to 553% and 38% in terms of lifetime and accuracy, respectively.

Sign in / Sign up

Export Citation Format

Share Document