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Battery Service-Life Enhancement Using Temporal Data Partitioning Mechanism for Sustainable IoT Applications

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Abstract

Majority of event-driven IoT applications in wireless multimedia sensor networks (WMSN) nodes often acquire redundant data of the same target or event, which show a very high degree of temporal correlation, and causes high consumption of energy due to transmission of redundant information; resulting a massive depletion of battery sources of the sensor node and reduces its service-life. To mitigate this issue, this paper presents a novel twofold encoding scheme to tackle the problem of highly temporally correlated data acquisition by sensor devices and redundant data transmission in smart IoT applications. The scheme first employs the Kruskal–Wallis Hypothesis test to partition the data, followed by encoding the data using the Redundant Binary Number System (RBNS) produces high proportion of 0 s in the encoded string. This in turn increases Silent Symbol period during transmission. To further conserve energy, a hybrid FSK-ASK modulation and demodulation technique is employed during communication with a non-coherent receiver, leading to a significant reduction in transmitter energy. We simulate the proposed raw sensor data encoding technique exploiting runs of individual encoded symbols of real-life sensor data source with commercially available transceiver Atmel ATR 2406. The simulation result shows about 0.041 mW h energy consumption per day employing Atmel ATR 2406 transceiver utilizing our proposed scheme which is about 58.0–83.6% less consumption compared to popular communication schemes like CNS, PCA, NCS, and ALDC schemes, respectively. The proposed idea also extended the CR2032 lithium coin cell battery life of the sensor module to 2.41 years which is 58–83% more compared to existing schemes, and resulted in a reduced CO\(_2\) emission rate of 0.036 mg/day, making the scheme environmentally sustainable.

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References

  1. Barnett T, Jain S, Andra U, Khurana T. Cisco visual networking index: forecast and trends, 2017–2022. In: Americas/EMEAR Cisco knowledge network (CKN) presentation. 2022. pp. 1–38.

  2. Wu T, Redouté J-M, Yuce M. A wearable, low-power, real-time ECG monitor for smart T-shirt and IoT healthcare applications. In: Advances in body area networks I. Springer; 2019. pp. 165–73.

  3. Chen X, Zhang J, Lin B, Chen Z, Wolter K, Min G. Energy-efficient offloading for DNN-based smart IoT systems in cloud-edge environments. IEEE Trans Parallel Distrib Syst. 2021;33(3):683–97.

    Article  Google Scholar 

  4. Albahri AS, Alwan JK, Taha ZK, Ismail SF, Hamid RA, Zaidan AA, Albahri OS, Zaidan BB, Alamoodi AH, Alsalem MA. IoT-based telemedicine for disease prevention and health promotion: state-of-the-art. J Netw Comput Appl. 2021;173(1–37): 102873.

    Article  Google Scholar 

  5. Adapa S, Fazal-e-Hasan SM, Makam SB, Azeem MM, Mortimer G. Examining the antecedents and consequences of perceived shopping value through smart retail technology. J Retail Consum Serv. 2020;52:101901 (1–11).

  6. Zhu Q, Loke SW, Trujillo-Rasua R, Jiang F, Xiang Y. Applications of distributed ledger technologies to the internet of things: a survey. ACM Comput Surv. 2019;52(6):1–34.

    Google Scholar 

  7. Sarkar S, Chatterjee S, Misra S. Assessment of the suitability of fog computing in the context of internet of things. IEEE Trans Cloud Comput. 2018;6(1):46–59.

    Article  Google Scholar 

  8. Misra S, Saha N. Detour: dynamic task offloading in software-defined fog for IoT applications. IEEE J Sel Areas Commun. 2019;37(5):1159–66.

    Article  Google Scholar 

  9. Misra S, Singh A, Chatterjee S, Obaidat MS. Mils-cloud: a sensor-cloud-based architecture for the integration of military tri-services operations and decision making. IEEE Syst J. 2016;10(2):628–36.

    Article  Google Scholar 

  10. Wang T, Li Y, Wang G, Cao J, Bhuiyan MZA, Jia W. Sustainable and efficient data collection from WSNs to cloud. IEEE Trans Sustain Comput. 2019;4(2):252–62.

    Article  Google Scholar 

  11. Woody M, Arbabzadeh M, Lewis GM, Keoleian GA, Stefanopoulou A. Strategies to limit degradation and maximize Li-ion battery service lifetime-critical review and guidance for stakeholders. J Energy Storage. 2020. https://doi.org/10.1016/j.est.2020.101231.

  12. Gulati K, Boddu RSK, Kapila D, Bangare SL, Chandnani N, Saravanan G. A review paper on wireless sensor network techniques in internet of things (IoT). Mater Today Proc. 2022;51:161–5.

    Article  Google Scholar 

  13. Wen F, Wang H, He T, Shi Q, Sun Z, Zhu M, Lee C. Battery-free short-range self-powered wireless sensor network (SS-WSN) using TENG based direct sensory transmission (TDST) mechanism. Nano Energy. 67:1–14.

  14. Mukherjee A, Misra S, Chandra VSP, Raghuwanshi NS. ECoR: energy-aware collaborative routing for task offload in sustainable UAV swarms. IEEE Trans Sustain Comput. 2020;5(4):514–25.

    Article  Google Scholar 

  15. Boukerche A, Guan S, De Grande RE. A task-centric mobile cloud-based system to enable energy-aware efficient offloading. IEEE Trans Sustain Comput. 2018;3(4):248–61.

    Article  Google Scholar 

  16. Xie G, Chen Y, Xiao X, Xu C, Li R, Li K. Energy-efficient fault-tolerant scheduling of reliable parallel applications on heterogeneous distributed embedded systems. IEEE Trans Sustain Comput. 2018;3(3):167–81.

    Article  Google Scholar 

  17. Gupta V, De S. Energy-efficient temporal sensing: an age-of-sample-based approach. IEEE Internet Things J. 2022;9(3):1806–17.

    Article  Google Scholar 

  18. Chen YP, Wang D, Zhang J. Variable-base tacit communication: a new energy efficient communication scheme for sensor networks. In: Proceedings of first international conference on integrated internet adhoc and sensor networks, ACM, 2006.

  19. Wang Z, Bulut E, Szymanski BK. Energy efficient collision aware multipath routing for wireless sensor networks. In: Proceedings of IEEE international conference on communications, 2009. pp. 1–5.

  20. Čagalj M, Hubaux J-P, Enz CC. Energy-efficient broadcasting in all-wireless networks. Wirel Netw. 2005;11:177–88.

    Article  Google Scholar 

  21. Zhou Y, Yang L, Yang L, Ni M. Novel energy-efficient data gathering scheme exploiting spatial-temporal correlation for wireless sensor networks. Wirel Commun Mob Comput. 2019. https://doi.org/10.1155/2019/4182563.

  22. Polastre J, Szewczyk R, Culler D. Telos: enabling ultra-low power wireless research. In: Proceedings of fourth IEEE international symposium on information processing in sensor networks, 2005. pp. 364–9.

  23. Hoang M-T, Sugii N, Ishibashi K. A 1.36 μW 312–315 MHz synchronized-OOK receiver for wireless sensor networks using 65 nm SOTB CMOS technology. Solid State Electron. 2016;117:161–9.

    Article  Google Scholar 

  24. Sharma A, Banerjee A, Sircar P. Performance analysis of energy-efficient modulation techniques for wireless sensor networks. In: Proceedings of annual IEEE India conference, Dec. 11–13, 2008, vol. 2. pp. 327–32.

  25. Zhan Y, Dai H. Energy-based transmission strategy selection for wireless sensor networks. In: Proceedings of IEEE global telecommunications conference, GLOBECOM, USA, Nov. 28–Dec. 2, 2005, vol. 6. pp. 1–5.

  26. Sinha K, Sinha BP, Datta D. An energy-efficient communication scheme for wireless networks: a redundant radix-based approach. IEEE Trans Wirel Commun. 2011;10(2):550–9.

    Article  Google Scholar 

  27. Sinha K, Sinha BP, Datta D. CNS: a new energy efficient transmission scheme for wireless sensor networks. Wirel Netw. 2010;16(8):2087–104.

    Article  Google Scholar 

  28. Bhattacharya A, Majumder P, Sinha K, Sinha BP, Kavitha K. An energy-efficient wireless communication scheme using quint Fibonacci number system. Int J Commun Netw Distrib Syst. 2016;16(2):140–61.

    Google Scholar 

  29. Vargha A, Delaney HD. The Kruskal-Wallis test and stochastic homogeneity. J Educ Behav Stat. 1998;23(2):170–92.

    Article  Google Scholar 

  30. Zhang X, Wu H, Li Q, Pan B. An event-based data aggregation scheme using PCA and SVR for WSNs. In: Proceedings of IEEE 85th vehicular technology conference, Sydney, Australia, June 4–7, 2017. pp. 1–5.

  31. Uthayakumar J, Elhoseny M, Shankar K. Highly reliable and low-complexity image compression scheme using neighborhood correlation sequence algorithm in WSN. IEEE Trans Reliab. 2020;69(4):1398–423.

    Article  Google Scholar 

  32. Ketshabetswe KL, Zungeru AM, Mtengi B, Lebekwe CK, Prabaharan SRS. Data compression algorithms for wireless sensor networks: a review and comparison. IEEE Access. 2021;9:136872–136891.

  33. Majumder P, Das SR, Sinha K, Sinha BP. Sustainable operations in IoT by combining spatiotemporal data correlation with silent symbol based communication strategy. IEEE Trans Sustain Comput. 2022.

  34. Gasch C, Brown D, Campbell C, Cobos D, Brooks E, Chahal M, Poggio M. A field-scale sensor network data set for monitoring and modeling the spatial and temporal variation of soil water content in a dryland agricultural field. Water Resour Res. 2017;53(12):10 878-10 887.

    Article  Google Scholar 

  35. Atmel ATR 2406 datasheet. https://www.mouser.com/datasheet/2/268/atmel_atmel-4779-smart-rf-atr2406_datasheet-1180337.pdf.

  36. Carbon emission calculator. https://www.rensmart.com/Calculators/KWH-to-CO2.

  37. Lithium coin CR2023 battery specification. https://data.energizer.com/pdfs/cr2032.pdf.

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Authors and Affiliations

  1. Department of Information Science and Engineering, JAIN (Deemed-to-be University), Jain Global Campus, Kanakapura, 562112, Karnataka, India

    Pratham Majumder

  2. School of Mobile Computing & Communication, Jadavpur University, Kolkata, West Bengal, India

    Punyasha Chatterjee

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  1. Pratham Majumder
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  2. Punyasha Chatterjee
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This article is part of the topical collection “Research Trends in Communication and Network Technologies” guest edited by Anshul Verma, Pradeepika Verma, and Kiran Kumar Pattanaik.

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Majumder, P., Chatterjee, P. Battery Service-Life Enhancement Using Temporal Data Partitioning Mechanism for Sustainable IoT Applications. SN COMPUT. SCI. 5, 14 (2024). https://doi.org/10.1007/s42979-023-02334-7

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