scholarly journals Prioritized Degree Distribution in Wireless Sensor Networks with a Network Coded Data Collection Method

Sensors ◽  
2012 ◽  
Vol 12 (12) ◽  
pp. 17128-17154 ◽  
Author(s):  
Jan Wan ◽  
Naixue Xiong ◽  
Wei Zhang ◽  
Qinchao Zhang ◽  
Zheng Wan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Degree Distribution ◽  
Wireless Sensor ◽  
Collection Method ◽  
Data Collection Method

scholarly journals Node-Gosper Island-Based Data Collection Method with Recharge Plan for Rechargeable Wireless Sensor Networks

2018 ◽  
Vol 201 ◽  
pp. 03001
Author(s):  
Chi-Chang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Power Consumption ◽  
Wireless Sensor ◽  
Transmission Method ◽  
Collection Method ◽  
Data Collection Methods ◽  
Data Collection Method ◽  
Collection Methods

Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node-Gosper island-based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used a mobile wireless charger to visit and recharge the batteries of sensors located on different levels of node-Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well-known data collection methods in terms of energy consumption.

scholarly journals A Novel Data Collection Method with Recharge Plan for Rechargeable Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Chi-Chang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Power Consumption ◽  
Wireless Sensor ◽  
Transmission Method ◽  
Collection Method ◽  
Data Collection Methods ◽  
Data Collection Method ◽  
Collection Methods

Because of recent advances in wireless power transfer technologies, several key topics of wireless sensor networks (WSNs) need to be revisited. Traditional data collection methods for WSNs typically consider the balance of power consumption among sensors as a critical design criterion for avoiding uneven power depletion in the networks. I propose a solution for the uneven power consumption problem of data collection over WSNs. I designed a node-Gosper island-based scalable hierarchical cluster transmission method in conjunction with a wireless recharge plan for data collection over rechargeable WSNs. For the recharge plan, I used mobile wireless chargers to visit and recharge the batteries of sensors located on different levels of node-Gosper curves with various frequencies. The duration of each recharging process for each layer of sensors was calculated to verify the feasibility of the proposed recharge plan. The simulation results indicate that my proposed data collection method outperforms several well-known data collection methods in terms of energy consumption and that my proposed recharge plan is more efficient than previous approaches in terms of charge path length, number of alive nodes, and traveling efficiency.

scholarly journals Efficient Data Collection Method in Sensor Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Keyan Cao ◽  
Haoli Liu ◽  
Yefan Liu ◽  
Gongjie Meng ◽  
Si Ji ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Collection ◽  
Dynamic Environment ◽  
Wireless Sensor ◽  
Node Selection ◽  
Collection Method ◽  
Random Path ◽  
Efficient Data ◽  
Efficient Data Collection

Wireless sensor networks are widely used in many fields, such as medical and health care, military monitoring, target tracking, and people’s life, because of their advantages of convenient deployment, low cost, and good concealment. However, due to the low battery capacity of sensor nodes and environmental changes, the energy consumption of nodes is serious and the accuracy of data collection is low. In the data collection method of multiple random paths, due to the uneven geographical distribution between nodes and the influence of the environment, it is easy to cause the communication between nodes to be blocked and the construction of random paths to fail. This paper proposes an efficient data collection algorithm for this problem. The algorithm is improved on the basis of the random node selection algorithm. This method can effectively avoid the failure of random path node selection and improve the node selection of random path in wireless sensor networks. Then, the sensor network in the dynamic environment is analyzed based on the static environment. An efficient data collection algorithm based on the position prediction of extreme learning machines is proposed. This method uses extreme learning machine methods to perform trajectory prediction for nodes in a dynamic environment.

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