GMSVM-Based Prediction for Temporal Data Aggregation in Sensor Networks

2009 ◽  
Author(s):  
Jian Kang ◽  
Liwei Tang ◽  
Xianzhang Zuo ◽  
Xihong Zhang ◽  
Hao Li
Keyword(s):  
Sensor Networks ◽  
Data Aggregation ◽  
Temporal Data

GEP-Based Temporal Data Aggregation in Wireless Sensor Networks

2009 ◽  
Author(s):  
Honglei Gao ◽  
Wenzhong Guo ◽  
Guolong Chen ◽  
Jiawen Lin ◽  
Yanhua Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Temporal Data

Energy-Efficient Data-Aggregation Technique for Correlated Spatial and Temporal Data in Cluster-Based Sensor Networks

2020 ◽  
Vol 16 (2) ◽  
pp. 53-68
Author(s):  
Khushboo Jain ◽  
Anoop Kumar
Keyword(s):  
Sensor Networks ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Data Gathering ◽  
Base Station ◽  
Sensor Nodes ◽  
Temporal Data ◽  
Network Applications ◽  
Efficient Data ◽  
Aggregation Technique

Continuous-monitoring applications in sensor network applications require periodic data transmissions to the base-station (BS), which may lead to unnecessary energy depletion. The energy-efficient data aggregation solutions in sensor networks have evolved as one of the favorable fields for such applications. Former research works have recommended many spatial-temporal designs and prototypes for successfully minimizing the data-gathering overheads, but these are constrained to their relevance. This work has proposed a data aggregation technique for homogeneous application set-ups in sensor networks. For this, the authors have employed two ways of model generation for reducing correlated spatial-temporal data in cluster-based sensor networks: one at the Sensor nodes (SNs) and the other at the Cluster heads (CHs). Building on this idea, the authors propose two types of data filtration, first at the SNs for determining temporal redundancies (TRs) in data readings by both relative deviation (RD) and adaptive frame method (AFM) and second at the CHs for determining spatial redundancies (SRs) by both RD and AFM.

A Prediction Based Data Aggregation Scheme in Wireless Sensor Networks

2011 ◽  
Vol 268-270 ◽  
pp. 517-522 ◽  
Author(s):  
Guo Rui Li ◽  
Ying Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Historical Data ◽  
Wireless Sensor ◽  
Temporal Data ◽  
Bandwidth Utilization ◽  
Seasonal Time Series ◽  
Aggregation Scheme ◽  
Aggregation Ratio

It is very important to minimize the amount of data transmission in wireless sensor networks so that the average sensor lifetime and the overall bandwidth utilization can be improved. In this paper, we propose a prediction based data aggregation scheme which takes advantage of temporal data correlation among sensors to monitor continuously changed environmental conditions. A seasonal time series model is built up to predict the sensed values of ordinary sensors according to the collected historical data. The experiment results show that our proposed scheme can provide considerable aggregation ratio while maintaining a low prediction error rate.

Multi-Source Temporal Data Aggregation in Wireless Sensor Networks

2010 ◽  
Vol 56 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Wenzhong Guo ◽  
Naixue Xiong ◽  
Athanasios V. Vasilakos ◽  
Guolong Chen ◽  
Hongju Cheng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Wireless Sensor ◽  
Temporal Data

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

2006 ◽  
Author(s):  
Tian He ◽  
Lin Gu ◽  
Liqian Luo ◽  
Ting Yan ◽  
John A. Stankovic ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Aggregation ◽  
Real Time Tracking
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