scholarly journals A traffic data collection and analysis method based on wireless sensor network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Huan Wang ◽  
Min Ouyang ◽  
Qingyuan Meng ◽  
Qian Kong
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Traffic Data ◽  
Flow Data ◽  
Analysis Method ◽  
Traffic Data Collection ◽  
Data Collection And Analysis

AbstractWith the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

scholarly journals An Enhanced Underwater Linear Wireless Sensor Network Deployment Strategy for Data Collection

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Zahoor Ahmed ◽  
Kamalrulnizam Abu Bakar
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Research Work ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Deployment Strategy ◽  
Underwater Pipeline

The deployment of Linear Wireless Sensor Network (LWSN) in underwater environment has attracted several research studies in the underwater data collection research domain. One of the major issues in underwater data collection is the lack of robust structure in the deployment of sensor nodes. The challenge is more obvious when considering a linear pipeline that covers hundreds of kilometers. In most of the previous work, nodes are deployed not considering heterogeneity and capacity of the various sensor nodes. This lead to the problem of inefficient data delivery from the sensor nodes on the underwater pipeline to the sink node at the water surface. Therefore, in this study, an Enhanced Underwater Linear Wireless Sensor Network Deployment (EULWSND) has been proposed in order to improve the robustness in linear sensor underwater data collection. To this end, this paper presents a review of related literature in an underwater linear wireless sensor network. Further, a deployment strategy is discussed considering linearity of the underwater pipeline and heterogeneity of sensor nodes. Some research challenges and directions are identified for future research work. Furthermore, the proposed deployment strategy is implemented using AQUASIM and compared with an existing data collection scheme. The result demonstrates that the proposed EULWSND outperforms the existing Dynamic Address Routing Protocol for Pipeline Monitoring (DARP-PM) in terms of overhead and packet delivery ratio metrics. The scheme performs better in terms of lower overhead with 17.4% and higher packet delivery with 20.5%.

scholarly journals DBac: deadline based data collection using CSMA/CD and earliest deadline first (EDF) scheduling in wireless sensor network

2018 ◽  
Vol 7 (3) ◽  
pp. 1956
Author(s):  
A Felix Arokya Jose ◽  
C Anand Deva Durai ◽  
S John Livingston
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Sampling Rate ◽  
Data Communication ◽  
Base Station ◽  
Wireless Sensor ◽  
Earliest Deadline First ◽  
Delivery Ratio ◽  
Earliest Deadline

Wireless Sensor Network (WSN) has an enormous scope of utilizations in detecting different parameters such as temperature, pressure, sound, pollution, etc. The sensed data in each sensor node are a valuable one. To communicate the information to the base station for further processing, a lot of strategies are available. Each sensor senses the data in different sampling rate depending upon the sudden raise in the sensing parameters. Data communication to the base station is very critical due to the dynamicity of the environment during the stipulated time.The sensed data should reach the base station before the data becomes invalid due to the violation of the deadline. In order to avoid deadline violation so that the sensed data becomes useless, this paper proposing a novel data collection algorithm based on the popular Earliest Deadline First (EDF) scheduling algorithm. The various simulation parameters are taken into account to verify the performance of the proposed method and the result shows that it achieves high throughput, low delay, high Packet Delivery Ratio (PDR) and low energy consumption.  

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