Dynamic Data Processing Middleware for Sensor Networks

2012 ◽  
pp. 141-147 ◽  
Author(s):  
Teemu Leppänen ◽  
Jukka Riekki
Keyword(s):  
Sensor Networks ◽  
Data Processing ◽  
Dynamic Data

scholarly journals A Wireless Sensor Network with Enhanced Power Efficiency and Embedded Strain Cycle Identification for Fatigue Monitoring of Railway Bridges

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Glauco Feltrin ◽  
Nemanja Popovic ◽  
Kallirroi Flouri ◽  
Piotr Pietrzak
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Processing ◽  
Monitoring System ◽  
Life Assessment ◽  
Wireless Sensor ◽  
Quality Data ◽  
Railway Bridges ◽  
Strain Cycle

Wireless sensor networks have been shown to be a cost-effective monitoring tool for many applications on civil structures. Strain cycle monitoring for fatigue life assessment of railway bridges, however, is still a challenge since it is data intensive and requires a reliable operation for several weeks or months. In addition, sensing with electrical resistance strain gauges is expensive in terms of energy consumption. The induced reduction of battery lifetime of sensor nodes increases the maintenance costs and reduces the competitiveness of wireless sensor networks. To overcome this drawback, a signal conditioning hardware was designed that is able to significantly reduce the energy consumption. Furthermore, the communication overhead is reduced to a sustainable level by using an embedded data processing algorithm that extracts the strain cycles from the raw data. Finally, a simple software triggering mechanism that identifies events enabled the discrimination of useful measurements from idle data, thus increasing the efficiency of data processing. The wireless monitoring system was tested on a railway bridge for two weeks. The monitoring system demonstrated a good reliability and provided high quality data.

scholarly journals DynDL: Scheduling Data-Locality-Aware Tasks with Dynamic Data Transfer Cost for Multicore-Server-Based Big Data Clusters

2018 ◽  
Vol 8 (11) ◽  
pp. 2216
Author(s):  
Jiahui Jin ◽  
Qi An ◽  
Wei Zhou ◽  
Jiakai Tang ◽  
Runqun Xiong
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Time ◽  
Data Transfer ◽  
Data Locality ◽  
Free Time ◽  
Time Data ◽  
Dynamic Data ◽  
Network Bandwidth ◽  
Transfer Cost

Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server’s network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server’s free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server’s data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL’s specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server’s free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds.

Comparison of Data Processing Techniques in Sensor Networks

2004 ◽  
Author(s):  
Enrique Sanchis-Peris ◽  
Vicente González-Millán
Keyword(s):  
Sensor Networks ◽  
Data Processing ◽  
Processing Techniques

Data Processing and Exchange Challenges in Video-Based Wireless Sensor Networks

2014 ◽  
pp. 196-215
Author(s):  
Dan Pescaru ◽  
Daniel-Ioan Curiac
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Processing ◽  
Data Exchange ◽  
Video Data ◽  
Wireless Sensor ◽  
Localization Algorithms ◽  
Innovative Solutions ◽  
Communication Efficiency ◽  
Specific Localization

This chapter presents the main challenges in developing complex systems built around the core concept of Video-Based Wireless Sensor Networks. It summarizes some innovative solutions proposed in scientific literature on this field. Besides discussion on various issues related to such systems, the authors focus on two crucial aspects: video data processing and data exchange. A special attention is paid to localization algorithms in case of random deployment of nodes having no specific localization hardware installed. Solutions for data exchange are presented by highlighting the data compression and communication efficiency in terms of energy saving. In the end, some open research topics related with Video-Based Wireless Sensor Networks are identified and explained.

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