Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
Source abstract
Abstract
Emerging IoT (Internet of Things) technologies have enjoyed tremendous success in a variety of applications. Since sensors in IoT consume a lot of energy to transmit their data, data compression used to prolong system lifetime has become a hot research topic. In many real-world applications, such as IEI (IoT of environmental intelligence), the required sensing data usually have limited error tolerance according to the QoS2 (quality of sensor service) or QoD (quality of decision-making) required. However, the bounded-error-pruned sensor data can achieve higher data correlation for better compression without jeopardizing QoS2/QoD. Moreover, the sensing data in widely spread sensors usually have strong temporal and spatial correlations. We thus propose a BESDC (bounded-error-pruned sensor data compression) scheme to achieve better leverage between the bounded error and compression ratio for sensor data. In this paper, our experiments on a sensor network of two-tier tree architecture consider four different environmental datasets including PM 2.5, CO, temperature and seismic wave with different scales of bounded errors. With the bounded errors required by the given IEI applications, our BESDC can reduce the total size of data transmission to minimize both energy consumption in sensor-tier devices and storage of fog-tier servers. The experimental results demonstrate that our BESDC can reduce transmission data by over 55% and save 50% energy consumption when assigning 1% of error tolerance within QoS2/QoD requirement. To the best of our knowledge, the proposed BESDC scheme can help other energy-efficient IoT schemes applying network topologies and routing protocols to further enhance energy-efficient IoT services.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Efficient Wireless Sensor Networks · IoT and Edge/Fog Computing · Distributed Sensor Networks and Detection Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Engineering Science and Ocean Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan
Department of Computer Science and Information Engineering, Ming Chuan University, No. 5 Der-Ming Rd., Gwei Shan District, Taoyuan City 333, Taiwan
References (46)
Show source-provided references
1. Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing10.1109/MC.2019.2914018 ↗
2. On the Use of Stray Wireless Signals for Sensing: A Look Beyond 5G for the Next Generation of Industry10.1109/MC.2019.2913626 ↗
3. Augmenting Cognition Through Edge Computing10.1109/MC.2019.2911878 ↗
4. Predicting Personality Traits from Physical Activity Intensity10.1109/MC.2019.2913751 ↗
5. Tariq, N., Asim, M., Al-Obeidat, F., Farooqi, M.Z., Baker, T., Hammoudeh, M., and Ghafir, I. (2019). The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey. Sensors, 19.10.3390/s19081788 ↗
6. Gill, S.S., Tuli, S., Xu, M., Singh, I., Singh, K.V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., and Jain, U. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet Things, 8.10.1016/j.iot.2019.100118 ↗
7. Neutrosophic Multi-Criteria Decision Making Approach for IoT-Based Enterprises10.1109/ACCESS.2019.2908919 ↗
8. An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things10.1109/TII.2014.2302638 ↗
9. A new methodology to support group decision-making for IoT-based emergency response systems10.1007/s10796-013-9407-z ↗
10. Improving Quality-of-Service in Wireless Sensor Networks by Mitigating “Hidden-Node Collisions”10.1109/TII.2009.2026643 ↗
11. Wang, Y.J. (2019, October 31). Study on Physical Air Temperature Measurement, Available online: http://photino.cwb.gov.tw/rdcweb/lib/cd/cd07mb/MB/PDF/40/No.1/01.pdf.
12. Chang, R.-I., Li, M.-H., Chuang, P., and Lin, J.-W. (2017). Bounded-error Data Compression and Aggregation in Wireless Sensor Networks. Smart Sensors Networks, Academic Press.10.1016/B978-0-12-809859-2.00009-7 ↗
13. Compression of electrocardiogram signals using JPEG200010.1109/TCE.2003.1261162 ↗
14. Data compression for energy efficient communication on ubiquitous sensor networks
15. Lin, C.C., Chuang, C.C., Chiang, C.W., and Chang, R.I. (2010, January 7–10). A novel data compression method using improved JPEG-LS in wireless sensor networks. Proceedings of the 12th International Conference on Advanced Communication Technology (ICACT) IEEE, Phoenix Park, Korea.
16. Bendifallah, A., Benzid, R., and Boulemden, M. (2011). Improved ECG compression method using discrete cosine transform. Electron. Lett., 47.10.1049/el.2010.3191 ↗
17. Chen, J., Ma, J., Zhang, Y., and Shi, X. (2006, January 25–28). A Wavelet-Based ECG Compression Algorithm Using Golomb Codes. Proceedings of the 2006 International Conference on Communications, Circuits and Systems, Guilin, China.10.1109/ICCCAS.2006.284602 ↗
18. Hyperplane-Based Vector Quantization for Distributed Estimation in Wireless Sensor Networks10.1109/TIT.2009.2032856 ↗
19. Multiresolution Spatial and Temporal Coding in a Wireless Sensor Network for Long-Term Monitoring Applications10.1109/TC.2009.20 ↗
20. Kawahara, M., Chiu, Y.-J., and Berger, T. (April, January 30). High-speed software implementation of Huffman coding. Proceedings of the DCC’98 Data Compression Conference (Cat. No. 98TB100225), Snowbird, UT, USA.
21. A Simple Algorithm for Data Compression in Wireless Sensor Networks10.1109/LCOMM.2008.080300 ↗
22. Javed, M.Y., and Nadeem, A. (2000, January 24–27). Data compression through adaptive Huffman coding schemes. Proceedings of the 2000 TENCON Proceedings Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119), Kuala Lumpur, Malaysia.
23. Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network10.3844/jcssp.2009.466.470 ↗
24. Huang, N.Y., Lin, C.C., Chuang, C.C., and Chang, R.I. (2010, January 18–20). Lossless compression for low correlation data in wireless sensor networks. Proceedings of the Workshop on Wireless Ad Hoc and Sensor Network, Wisla, Poland.
25. Routing Correlated Data with Fusion Cost in Wireless Sensor Networks10.1109/TMC.2006.171 ↗
26. Adaptive Data Fusion for Energy Efficient Routing in Wireless Sensor Networks10.1109/TC.2006.157 ↗
27. Kumar, R., Wolenetz, M., and Agarwalla, B. (2003, January 5–7). DFuse: A framework for distributed data fusion. Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, Los Angeles, CA, USA.10.1145/958491.958505 ↗
28. Jin, G.-Y., and Park, M.-S. (2006). CAC: Context Adaptive Clustering for Efficient Data Aggregation in Wireless Sensor Networks, Springer Science and Business Media LLC.10.1007/11753810_98 ↗
29. Lee, S., Yoo, J., and Chung, T. (2004, January 16–18). Distance-based energy efficient clustering for wireless sensor networks. Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks LCN-04, Washington, DC, USA.
30. Yoon, S., and Shahabi, C. (2007). The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Trans. Sens. Netw., 3.10.1145/1210669.1210672 ↗
31. Prediction-based monitoring in sensor networks10.1145/1037107.1037117 ↗
32. An efficient data fusion and assurance mechanism using temporal and spatial correlations for home automation networks10.1109/TCE.2009.5277996 ↗
33. Makarenko, A., and Whyte, H.D. (July, January 28). Decentralized data fusion and control in active sensor networks. Proceedings of the International Conference on Information Fusion, Stockholm, Sweden.
34. Yuan, W., Krishnamurthy, S.V., and Tripathi, S.K. (December, January 29). Synchronization of multiple levels of data fusion in wireless sensor networks. Proceedings of the Global Telecommunications Conference, Dallas, TX, USA.
35. Sharaf, M.A., Beaver, J., Labrinidis, A., and Chrysanthis, P.K. (2003, January 19). TiNA: A scheme for temporal coherency-aware in-network aggregation. Proceedings of the 3rd ACM International Workshop on Data Engineering for Wireless and Mobile Access, San Diego, CA, USA.
36. Lin, C.-L., Tsai, J.-H., Chu, Y.-H., and Chang, R.-I. (2014, January 2–5). Concept of bounded error to improve wireless sensor network data compression. Proceedings of the IEEE SENSORS 2014, Valencia, Spain.10.1109/ICSENS.2014.6985234 ↗
37. Li, M.-H., Lin, C.-C., Chuang, C.-C., and Chang, R.-I. (2010, January 4–6). Error-Bounded Data Compression Using Data, Temporal and Spatial Correlations in Wireless Sensor Networks. Proceedings of the 2010 International Conference on Multimedia Information Networking and Security, Nanjing, China.10.1109/MINES.2010.31 ↗
38. Chen, Y.-H., Huang, N.-Y., Chu, Y.-H., Li, M.-H., Chang, R.-I., and Wang, C.-H. (2012, January 28–31). Dynamic bounded-error data compression and aggregation in wireless sensor network. Proceedings of the 2012 IEEE Sensors, Taipei, Taiwan.10.1109/ICSENS.2012.6411224 ↗
39. An energy efficient IoT data compression approach for edge machine learning10.1016/j.future.2019.02.005 ↗
40. Agarwal, S., Mozafari, B., Panda, A., Milner, H., Madden, S., and Stoica, I. (2003). BlinkDB: Queries with bounded-errors and bounded response times on very large data. EuroSys ’13: Proceedings of the 8th ACM European Conference on Computer Systems, Prague, Czech Republic, 14–17 April 2013, ACM.10.1145/2465351.2465355 ↗
41. Online aggregation10.1145/253262.253291 ↗
42. Di, S., and Cappello, F. (2016, January 23–27). Fast Error-Bounded Lossy HPC Data Compression with SZ. Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Chicago, IL, USA.10.1109/IPDPS.2016.11 ↗
43. (2019, February 01). Taiwan Air Quality Monitoring Network, Available online: https://taqm.epa.gov.tw/taqm/en/.
44. (2019, February 01). U.S. Geological Survey: Earthquake Hazards Program, Available online: http://earthquake.usgs.gov/.
45. Minimizing energy consumptions in wireless sensor networks via two-modal transmission10.1145/1672308.1672311 ↗
46. Habib, A., and Moh, S. (2019). Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey. Appl. Sci., 9.10.3390/app9142896 ↗