scholarly journals An Efficient Data Collection Protocol Based on Multihop Routing and Single-Node Cooperation in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqiang Zheng ◽  
Bing Li ◽  
Jishun Li ◽  
Huahong Ma ◽  
Baofeng Ji
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Data Transmission ◽  
Wireless Sensor ◽  
Single Node ◽  
Multihop Routing ◽  
Efficient Data ◽  
Efficient Data Collection

Considering the constrained resource and energy in wireless sensor networks, an efficient data collection protocol named ESCDD which adopts the multihop routing technology and the single-node selection cooperative communication is proposed to make the communication protocol more simple and easy to realize for the large-scale multihop wireless sensor networks. ESCDD uses the greedy strategy and the control information based on RTS/CTS to select forwarding nodes. Then, the hops in the multihop data transmission are reduced. Based on the power control in physical layer and the control frame called CoTS in MAC layer, ESCDD chooses a single cooperative node to perform cooperative transmission. The receiving node adopts maximal ratio combining (MRC) to recover original data. The energy consumption per hop is reduced. Furthermore, the total energy consumption in data collection process is shared by more nodes and the network lifetime is extended. Compared with GeRaF, EERNFS, and REEFG protocol, the simulation results show that ESCDD can effectively reduce the average delay of multihop data transmission, improve the successful delivery rate of data packets, significantly save the energy consumption of network nodes, and make the energy consumption more balanced.

scholarly journals A Hybrid Prediction Model for Energy-Efficient Data Collection in Wireless Sensor Networks

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2024
Author(s):  
Seyed Ahmad Soleymani ◽  
Shidrokh Goudarzi ◽  
Nazri Kama ◽  
Saiful Adli Ismail ◽  
Mazlan Ali ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Data Collection ◽  
Data Transmission ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Sampling ◽  
Efficient Data ◽  
Efficient Data Collection

Energy consumption because of unnecessary data transmission is a significant problem over wireless sensor networks (WSNs). Dealing with this problem leads to increasing the lifetime of any network and improved network feasibility for real time applications. Building on this, energy-efficient data collection is becoming a necessary requirement for WSN applications comprising of low powered sensing devices. In these applications, data clustering and prediction methods that utilize symmetry correlations in the sensor data can be used for reducing the energy consumption of sensor nodes for persistent data collection. In this work, a hybrid model based on decision tree (DT), autoregressive integrated moving average (ARIMA), and Kalman filtering (KF) methods is proposed to predict the data sampling requirement of sensor nodes to reduce unnecessary data transmission. To perform data sampling predictions in the WSNs efficiently, clustering and data aggregation to each cluster head are utilized, mainly to reduce the processing overheads generating the prediction model. Simulation experiments, comparisons, and performance evaluations conducted in various cases show that the forecasting accuracy of our approach can outperform existing Gaussian and probabilistic based models to provide better energy efficiency due to reducing the number of packet transmissions.

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