GEP-Based Temporal Data Aggregation in Wireless Sensor Networks

Author(s):  
Honglei Gao ◽  
Wenzhong Guo ◽  
Guolong Chen ◽  
Jiawen Lin ◽  
Yanhua Liu
2011 ◽  
Vol 268-270 ◽  
pp. 517-522 ◽  
Author(s):  
Guo Rui Li ◽  
Ying Wang

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.


2010 ◽  
Vol 56 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Wenzhong Guo ◽  
Naixue Xiong ◽  
Athanasios V. Vasilakos ◽  
Guolong Chen ◽  
Hongju Cheng

2021 ◽  
Vol 40 (5) ◽  
pp. 8727-8740
Author(s):  
Rajvir Singh ◽  
C. Rama Krishna ◽  
Rajnish Sharma ◽  
Renu Vig

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.


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