Compression-based Data Reduction Technique for IoT Sensor Networks
Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energ...
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| Language: | English |
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University of Baghdad, College of Science for Women
2021-03-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5069 |
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| author | Suha Abdulhussein Abdulzahra Ali Kadhum M. Al-Qurabat Ali Kadhum Idrees |
| author_facet | Suha Abdulhussein Abdulzahra Ali Kadhum M. Al-Qurabat Ali Kadhum Idrees |
| author_sort | Suha Abdulhussein Abdulzahra |
| collection | DOAJ |
| description | Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature. |
| format | Article |
| id | doaj-art-a3aff13b3ae8489b991c80b3c4eb4b68 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-a3aff13b3ae8489b991c80b3c4eb4b682025-08-20T03:56:58ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-03-0118110.21123/bsj.2021.18.1.0184Compression-based Data Reduction Technique for IoT Sensor NetworksSuha Abdulhussein Abdulzahra0Ali Kadhum M. Al-Qurabat1Ali Kadhum Idrees2Al-Mustaqbal University CollegeUniversity of BabylonUniversity of BabylonEnergy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5069IoT, Sensor Networks, Data Compression, SAX Quantization, LZW. |
| spellingShingle | Suha Abdulhussein Abdulzahra Ali Kadhum M. Al-Qurabat Ali Kadhum Idrees Compression-based Data Reduction Technique for IoT Sensor Networks مجلة بغداد للعلوم IoT, Sensor Networks, Data Compression, SAX Quantization, LZW. |
| title | Compression-based Data Reduction Technique for IoT Sensor Networks |
| title_full | Compression-based Data Reduction Technique for IoT Sensor Networks |
| title_fullStr | Compression-based Data Reduction Technique for IoT Sensor Networks |
| title_full_unstemmed | Compression-based Data Reduction Technique for IoT Sensor Networks |
| title_short | Compression-based Data Reduction Technique for IoT Sensor Networks |
| title_sort | compression based data reduction technique for iot sensor networks |
| topic | IoT, Sensor Networks, Data Compression, SAX Quantization, LZW. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5069 |
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