物聯網（IoT）因為具有非常有用的設備，因此非常受歡迎。越來越多國家與公司開始部署多種類型的傳感器來監控環境。此外，智能電網系統為了數據監控和傳輸，也配備越來越多物聯網設備或傳感器。智能電網基於先進的測量基礎設施，包括電錶數據管理系統，數據集中器和智慧電錶，然而，這些物聯網設備與傳感器會經常發出請求或響應消息或數據。這給網路頻寬帶來了巨大的負擔，可能導致網路崩潰或阻塞。為了解決這個問題，本研究提出了一種數據壓縮算法，能夠減輕智慧電網中網路有限頻寬的負擔。除此之外，智慧電網也受到網路安全問題的損害，特別是隨著越來越多的電力或公共事業組織使用此類設備來監控其電力系統的狀態，並向其客戶提供自動化計費功能。因此，確保智慧電網設備的安全勢在必行。智慧電網必須提供安全措施以確保所有信息都是從授權的設備或客戶端所發送。為了解決這些安全問題，本研究所提出的算法整合了數位簽章，從中防止中間人攻擊和重送攻擊。這項研究所進行的實驗，也證明了此算法的效能，並且與現有算法進行了比較，本論文所提出的演算法在壓縮率（壓縮率是同類算法的兩倍）和性能上均優於現有的演算法。

The Internet of Things (IoT) features highly useful devices and is thus highly popular. Countries and companies have increasingly started deploying numerous types of sensors for monitoring the environment. Similarly, smart grid systems have been increasingly equipped with IoT devices or sensors for data monitoring and transmission. A smart grid is based on an advanced metering infrastructure system and comprises meter data management systems, data concentrators, and smart meters. However, these IoT devices or sensors must request for or respond to messages or data frequently. This imposes a substantial burden on the network bandwidth, causing the network to crash or become blocked. To resolve this problem, this study proposes a data compression algorithm for reducing the burden imposed on network bandwidth in smart grids. Furthermore, smart grid devices are marred by cybersecurity problems. In particular, as more power or utility organizations use such devices to monitor the status of their power systems and provide automatic billing to their customers, ensuring the security of smart grid devices is imperative. Security measures should be implemented to ensure that all of the information is sent from authorized devices or clients. To address such security concerns, the proposed algorithm is equipped with a highly efficient elliptic curve digital signature algorithm to prevent man-in-the-middle and replay attacks. This study conducted an experiment to demonstrate the performance of the proposed algorithm; the algorithm was compared with existing algorithms. The experimental results revealed that the proposed algorithm outperformed these counterpart algorithms in terms of compression ratio (which was twice those of the counterpart algorithms) and computing speed.

[ 1 ] X. Fang, S. Misra, G. Xue and D. Yang, "Smart Grid — The New and Improved Power Grid: A Survey," in IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 944-980, Fourth Quarter 2012, doi: 10.1109/SURV.2011.101911.00087.
[ 2 ] R. Ma, H. Chen, Y. Huang and W. Meng, "Smart Grid Communication: Its Challenges and Opportunities," in IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 36-46, March 2013, doi: 10.1109/TSG.2012.2225851.
[ 3 ] V. C. Gungor, D. Sahin, T. Kocak, S. Ergut, C. Buccella, C. Cecati and G. P. Hancke, "A Survey on Smart Grid Potential Applications and Communication Requirements," in IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 28-42, Feb. 2013, doi: 10.1109/TII.2012.2218253.
[ 4 ] Z. Fan, P. Kulkarni, S. Gormus, C. Efthymiou, G. Kalogridis, M. Sooriyabandara, Z. Zhu, S. Lambotharan and W. H. Chin, "Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities," in IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 21-38, First Quarter 2013, doi: 10.1109/SURV.2011.122211.00021.
[ 5 ] M. Ghofrani, M. Hassanzadeh, M. Etezadi-Amoli and M. S. Fadali, "Smart meter based short-term load forecasting for residential customers," 2011 North American Power Symposium, Boston, pp. 1-5, MA, USA, 2011, doi: 10.1109/NAPS.2011.6025124.
[ 6 ] S. Wang, L. Cui, J. Que, D. Choi, X. Jiang, S. Cheng, and L. Xie, "A Randomized Response Model for Privacy Preserving Smart Metering," in IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1317-1324, Sept. 2012, doi: 10.1109/TSG.2012.2192487.
[ 7 ] D. Alahakoon and X. Yu, "Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey," in IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 425-436, Feb. 2016, doi: 10.1109/TII.2015.2414355.
[ 8 ] P. Parkash, "Data concentrators: The core of energy and data management", Texas Instruments, Sep. 2013.
[ 9 ] Y. Yan, R. Q. Hu, S. K. Das, H. Sharif and Y. Qian, "An efficient security protocol for advanced metering infrastructure in smart grid," in IEEE Network, vol. 27, no. 4, pp. 64-71, July-August 2013, doi: 10.1109/MNET.2013.6574667.
[ 10 ] Y. Yang and S. Roy, "Grouping-Based MAC Protocols for EV Charging Data Transmission in Smart Metering Network," in IEEE Journal on Selected Areas in Communications, vol. 32, no. 7, pp. 1328-1343, July 2014, doi: 10.1109/JSAC.2014.2332047.
[ 11 ] K. Yu, M. Arifuzzaman, Z. Wen, D. Zhang and T. Sato, "A Key Management Scheme for Secure Communications of Information Centric Advanced Metering Infrastructure in Smart Grid," in IEEE Transactions on Instrumentation and Measurement, vol. 64, no. 8, pp. 2072-2085, Aug. 2015, doi: 10.1109/TIM.2015.2444238.
[ 12 ] C. W. Hsu and S. Y. Hsieh, "Data Compression Strategies for Use in Advanced Metering Infrastructure Networks," IntechOpen, Jun. 2020, doi: 10.5772/intechopen.94974.
[ 13 ] J. Zhou, R. Qingyang Hu and Y. Qian, "Scalable Distributed Communication Architectures to Support Advanced Metering Infrastructure in Smart Grid," in IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1632-1642, Sept. 2012, doi: 10.1109/TPDS.2012.53.
[ 14 ] O. A. Osanaiye and M. Dlodlo, "TCP/IP header classification for detecting spoofed DDoS attack in Cloud environment," IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON), pp. 1-6, Salamanca, Spain, 2015, doi: 10.1109/EUROCON.2015.7313736.
[ 15 ] K. Choi and K. Chae, "Data aggregation using temporal and spatial correlations in Advanced Metering Infrastructure," The International Conference on Information Networking 2014 (ICOIN2014), pp. 541-544, Phuket, Thailand, 2014, doi: 10.1109/ICOIN.2014.6799740.
[ 16 ] K. C. Barr and K. Asanović, "Energy-aware lossless data compression," ACM Transactions on Computer Systems, vol. 24, no. 3, pp. 250–291, Aug. 2006, doi:10.1145/1151690.1151692.
[ 17 ] M. Ringwelski, C. Renner, A. Reinhardt, A. Weigel and V. Turau, "The Hitchhiker's guide to choosing the compression algorithm for your smart meter data," 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), pp. 935-940, Florence, Italy, 2012, doi: 10.1109/EnergyCon.2012.6348285.
[ 18 ] Manzini, Giovanni, "The Burrows-Wheeler Transform: Theory and Practice," Mathematical Foundations of Computer Science, 1999, pp. 34-47.
[ 19 ] Farooq, S. Mullapathi, Hussain, S. M. Suhail, Kiran, Siddavaram, Ustun and T. Selim, "Certificate Based Authentication Mechanism for PMU Communication Networks Based on IEC 61850-90-5," Electronics, vol. 7, no. 12, 2018, doi: 10.3390/electronics7120370.
[ 20 ] S. M. Farooq, S. Nabirasool, S. Kiran, S. M. Suhail Hussain and T. S. Ustun, "MPTCP based mitigation of Denial of Service (DoS) Attack in PMU Communication Networks," 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1-5, Chennai, India, 2018, doi: 10.1109/PEDES.2018.8707542.
[ 21 ] L. K. Galla, V. S. Koganti and N. Nuthalapati, "Implementation of RSA," 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 81-87, Kumaracoil, 2016, doi: 10.1109/ICCICCT.2016.7987922.
[ 22 ] S. Lamba and M. Sharma, "An Efficient Elliptic Curve Digital Signature Algorithm (ECDSA)," 2013 International Conference on Machine Intelligence and Research Advancement, pp. 179-183, Katra, India, 2013, doi: 10.1109/ICMIRA.2013.41.
[ 23 ] W. R. Stone, "Some Useful Utilities [From the Screen of Stone]," in IEEE Antennas and Propagation Magazine, vol. 58, no. 4, pp. 104-107, Aug. 2016, doi: 10.1109/MAP.2016.2569473.
[ 24 ] X. Zhao and B. Li, "Implementation of the LZMA compression algorithm on FPGA," 2017 International Conference on Electron Devices and Solid-State Circuits (EDSSC), pp. 1-2, Hsinchu, 2017, doi: 10.1109/EDSSC.2017.8126506.
[ 25 ] P. M. Szecowka and T. Mandrysz, "Towards hardware implementation of bzip2 data compression algorithm," 2009 MIXDES-16th International Conference Mixed Design of Integrated Circuits & Systems, pp. 337-340, Lodz, Poland, 2009.
[ 26 ] S. Lee and K. Shin, "An efficient implementation of SHA processor including three hash algorithms (SHA-512, SHA-512/224, SHA-512/256)," 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA, 2018, pp. 1-4, doi: 10.23919/ELINFOCOM.2018.8330578.
[ 27 ] J. Z. Kolter and M. J. Johnson, "REDD: A public data set for energy disaggregation research," Proceedings of the SustKDD workshop on Data Mining Applications in Sustainability, 2011.
[ 28 ] J. F. Huang, G. G. Zhang and S. Y. Hsieh, "A Strategy for Real-Time Energy Data Compression to Reduce Data Traffic based on Smart Grid AMI Networks," The Journal of Supercomputing, Feb. 2021, doi: 10.1007/s11227-020-03557-8.