Abstract
The quantity of Telemetry Data (TD) is rapidly augmenting because of a large extent of involved parameters in addition to the use of higher sampling frequencies. Therefore, to enhance the transmission efficiency as well as lessen the load on spacecraft resources, effectual Data Compression approaches in space TD are required. Further, the TD is suffered as of some faults. Therefore, to identify that fault data and render secure Data Transmission, the fault-analysis and data security techniques are needed onboard. It is challenging to attain higher Compression Ratios together with higher accuracy in lossless compression together with fault analysis. This paper designs an effective lossless TD compression as well as fault analysis scheme to trounce all these challenges. This scheme comprises:— (i) compression, (ii) encryption and (iii) classification. In the compression stage, the 2SMLZ compress the redundancy removed TD. In the encryption stage, the EECC algorithm encrypts the compressed data, so that the encrypted data can well be transmitted securely to the Ground Station (GS). In the GS, for recovering the original data, the decryption, as well as the decompression process, are carried out. Conversely, the CMOW-DLNN classifier identifies the fault data on the fault analysis stage. The proposed techniques effectively compress the data, as well as securely transmit the data and accurately recognize the fault data, which are experimentally proved by contrasting it with existing techniques.
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References
Wan, P., Zhan, Y., & Jiang, W. (2019). Study on the satellite telemetry data classification based on self-learning. IEEE Access, 8, 2656–2669.
Anandan, V. K., Kumar, C. P., Satyanarayana, S. N. V., Sarkar, M. (2018). Multiple Satellite Telemetry and Tracking System (MuST). In IEEE Indian Conference on Antennas and Propogation (InCAP), 16–19 Dec. 2018 (pp. 1–4). Hyderabad, India. https://doi.org/10.1109/INCAP.2018.8770889
Avramenko, S., SonzaReorda, M., Violante, M., & Fey, G. (2017). A high-level approach to analyze the effects of soft errors on lossless compression algorithms. Journal of Electronic Testing, 33(1), 53–64.
Sharma, N., Hussain, M. O., & Alam, M. (2020). Enriched J-Bit encrypting technique using data compression algorithms in data warehouse. Journal of Information and Optimization Sciences, 41(3), 813–822.
Baga, Y., Ghaffari, F., Declercq, D., Zante, E., Nahmiyace, M. (2017). Reduction of frames storage size in AFDX reception end-system using a lossless compression algorithm. In IEEE/AIAA 36th Digital Avionics Systems Conference (DASC) (pp. 1–8). https://doi.org/10.1109/DASC.2017.8102086.
Fan, C., Hu, Z., Jia, L., Min, H. (2020). A novel lossless compression encoding framework for SAR remote sensing images. Signal, Image and Video Processing (pp.1–8). https://doi.org/10.1007/s11760-020-01763-8
Kumar, P., & Parmar, A. (2020). Versatile approaches for medical image compression: A review. Procedia Computer Science, 167, 1380–1389.
Sharma, U., Sood, M., & Puthooran, E. (2019). A novel resolution independent gradient edge predictor for lossless compression of medical image sequences. International Journal of Computers and Applications, 41, 1–11. https://doi.org/10.1080/1206212X.2019.1610994
Gopinath, A., Ravisankar, M. (2020). Comparison of Lossless Data Compression Techniques. In IEEE International Conference on Inventive Computation Technologies (ICICT) (pp. 628–633).
Hussain, A. J., Al-Fayadh, A., & Radi, N. (2018). Image compression techniques: A survey in lossless and lossy algorithms. Neurocomputing, 300, 44–69.
Wen, L., Zhou, K., Yang, S., & Li, L. (2018). Compression of smart meter big data: A survey. Renewable and Sustainable Energy Reviews, 91, 59–69.
Liu, D., Pang, J., Song, G., **e, W., Song, G., Peng, Y., & Peng, X. (2017). Fragment anomaly detection with prediction and statistical analysis for satellite telemetry. IEEE Access, 5, 19269–19281.
Sasi, S., & Swarna Jyothi, L. (2016). Robustic public key cryptosystem for space data communication. In IEEE International Conference on Communication and Electronics Systems (ICCES), 21–22 Oct. 2016 (pp. 1–5). Coimbatore, India. https://doi.org/10.1109/CESYS.2016.7889818
Liu, D., Pang, J., Xu, B., Liu, Z., Zhou, J., Zhang, G. (2017). Satellite telemetry data anomaly detection with hybrid similarity measures. In IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) (pp. 591–596).
Junfeng, Wu., Yao, Li., Liu, B., Ding, Z., & Zhang, L. (2020). Combining OC-SVMs With LSTM for detecting anomalies in telemetry data with irregular intervals. IEEE Access, 8, 106648–106659.
Pan, D., Song, Z., Nie, L., Wang, B. (2020). Satellite telemetry data anomaly detection using Bi-LSTM prediction based model. In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1–6). https://doi.org/10.1109/I2MTC43012.2020.9129010
Lv, Z., Zhang, W., Li, N., Chen, C., Cai, J. (2019). A highly reliable lightweight distribution network communication encryption scheme. In IEEE International Conference on Power Data Science (ICPDS) (pp. 11–14).
Shi, X., Shen, Y., Wang, Y., & Bai, L. (2018). Differential-clustering compression algorithm for real-time aerospace telemetry data. IEEE Access, 6, 57425–57433.
Pan, X., Tang, S., Liu, S., Kong, J., Zhang, X., Hu, D., Qi, J., & Zhu, Z. (2020). Privacy-preserving multilayer in-band network telemetry and data analytics: For safety, please do not report plaintext data. Journal of Lightwave Technology, 38(21), 5855–5866.
Chen, J., Pi, D., Zhiyuan, Wu., Zhao, X., Pan, Y., & Zhang, Q. (2020). Imbalanced satellite telemetry data anomaly detection model based on Bayesian LSTM. Acta Astronautica. https://doi.org/10.1016/j.actaastro.2020.12.012
Pilastre, B., Boussouf, L., Escrivan, S., & Tourneret, J. Y. (2020). Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning. Signal Processing, 168, 107320.
Iqbal, W., Berral, J. L., Erradi, A., & Carrera, D. (2019). Real-time data center’s telemetry reduction and reconstruction using Markov chain models. IEEE Systems Journal, 13(4), 4039–4050.
Zhang, H., Wang, X.-Q., Sun, Y.-J., & Wang, X.-Y. (2020). A novel method for lossless image compression and encryption based on LWT, SPIHT and cellular automata. Signal Processing: Image Communication, 84, 115829. https://doi.org/10.1016/j.image.2020.115829
Portell, J., Iudica, R., García-Berro, E., Villafranca, A. G., & Artigues, G. (2018). FAPEC, a versatile and efficient data compressor for space missions. International journal of remote sensing, 39(7), 2022–2042.
Avramenko, S., Sonza Reorda, M., Violante, M., et al. (2017). A high-level approach to analyze the effects of soft errors on lossless compression algorithms. Journal of Electronic Test, 33, 53–64. https://doi.org/10.1007/s10836-016-5637-6
Hamed, H., & Mahdi, A. (2019). Analysis of lossless compression techniques time-frequency based in ECG signal compression. Asian Journal of Biomedical and Pharmaceutical Sciences. https://doi.org/10.35841/2249-622X.66.18-867
Nair, M. S., & Rajaram, A. (2014). Low power receiver using envelope Detector converters. International Journal of Advanced Information Science and Technology, 3(3), 50–57.
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Ramalingam, P., Thanuja, R., Bhavani, R. et al. An Efficient Lossless Telemetry Data Compression and Fault Analysis System Using 2SMLZ and CMOW-DLNN. Wireless Pers Commun 127, 2325–2345 (2022). https://doi.org/10.1007/s11277-021-08799-0
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DOI: https://doi.org/10.1007/s11277-021-08799-0