AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring

Akey Sungheetha; John Blake; R. Rajesh Sharma; Sheila Mahapatra; Jeba Singh O

doi:10.1007/978-3-032-18910-3_39

AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring

Akey Sungheetha
, John Blake^*
, R. Rajesh Sharma
, Sheila Mahapatra
, Jeba Singh O

^*Corresponding author for this work

School of Computer Science and Digital Technologies

Alliance University

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system’s adaptability to various environmental conditions, with an average response time of 3.2ms and 92% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.

Original language	English
Title of host publication	Proceedings of 5th International Conference on Computing and Communication Networks
Subtitle of host publication	ICCCN 2025
Editors	Gia-Nhu Nguyen, Abhishek Swaroop, Pancham Shukla
Publisher	Springer, Cham
Pages	431-441
Number of pages	11
Volume	3
ISBN (Electronic)	9783032189103
ISBN (Print)	9783032189097
DOIs	https://doi.org/10.1007/978-3-032-18910-3_39
Publication status	Published - 1 May 2026
Event	5th International Conference on Computing and Communication Networks, ICCCN 2025 - Hybrid, Manchester, United Kingdom Duration: 1 Aug 2025 → 3 Aug 2025

Publication series

Name	Lecture Notes in Networks and Systems (LNNS)
Volume	1855
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	5th International Conference on Computing and Communication Networks, ICCCN 2025
Country/Territory	United Kingdom
City	Hybrid, Manchester
Period	1/08/25 → 3/08/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Artificial Intelligence
Environmental Monitoring
Neuromorphic Computing
Stream Processing Analytics
Sustainable Energy
Triboelectric Nanogenerator

Access to Document

10.1007/978-3-032-18910-3_39

Cite this

Sungheetha, A., Blake, J., Rajesh Sharma, R., Mahapatra, S., & Singh O, J. (2026). AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring. In G.-N. Nguyen, A. Swaroop, & P. Shukla (Eds.), Proceedings of 5th International Conference on Computing and Communication Networks: ICCCN 2025 (Vol. 3, pp. 431-441). (Lecture Notes in Networks and Systems (LNNS); Vol. 1855). Springer, Cham. https://doi.org/10.1007/978-3-032-18910-3_39

Sungheetha, Akey ; Blake, John ; Rajesh Sharma, R. et al. / AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring. Proceedings of 5th International Conference on Computing and Communication Networks: ICCCN 2025. editor / Gia-Nhu Nguyen ; Abhishek Swaroop ; Pancham Shukla. Vol. 3 Springer, Cham, 2026. pp. 431-441 (Lecture Notes in Networks and Systems (LNNS)).

@inproceedings{e09eb1424e5543e7829ff4f4cbcf08b0,

title = "AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring",

abstract = "This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47\% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system{\textquoteright}s adaptability to various environmental conditions, with an average response time of 3.2ms and 92\% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.",

keywords = "Artificial Intelligence, Environmental Monitoring, Neuromorphic Computing, Stream Processing Analytics, Sustainable Energy, Triboelectric Nanogenerator",

author = "Akey Sungheetha and John Blake and \{Rajesh Sharma\}, R. and Sheila Mahapatra and \{Singh O\}, Jeba",

year = "2026",

month = may,

day = "1",

doi = "10.1007/978-3-032-18910-3\_39",

language = "English",

isbn = "9783032189097",

volume = "3",

series = "Lecture Notes in Networks and Systems (LNNS)",

publisher = "Springer, Cham",

pages = "431--441",

editor = "Gia-Nhu Nguyen and Abhishek Swaroop and Pancham Shukla",

booktitle = "Proceedings of 5th International Conference on Computing and Communication Networks",

note = "5th International Conference on Computing and Communication Networks, ICCCN 2025 ; Conference date: 01-08-2025 Through 03-08-2025",

}

Sungheetha, A, Blake, J, Rajesh Sharma, R, Mahapatra, S & Singh O, J 2026, AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring. in G-N Nguyen, A Swaroop & P Shukla (eds), Proceedings of 5th International Conference on Computing and Communication Networks: ICCCN 2025. vol. 3, Lecture Notes in Networks and Systems (LNNS), vol. 1855, Springer, Cham, pp. 431-441, 5th International Conference on Computing and Communication Networks, ICCCN 2025, Hybrid, Manchester, United Kingdom, 1/08/25. https://doi.org/10.1007/978-3-032-18910-3_39

AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring. / Sungheetha, Akey; Blake, John; Rajesh Sharma, R. et al.
Proceedings of 5th International Conference on Computing and Communication Networks: ICCCN 2025. ed. / Gia-Nhu Nguyen; Abhishek Swaroop; Pancham Shukla. Vol. 3 Springer, Cham, 2026. p. 431-441 (Lecture Notes in Networks and Systems (LNNS); Vol. 1855).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring

AU - Sungheetha, Akey

AU - Blake, John

AU - Rajesh Sharma, R.

AU - Mahapatra, Sheila

AU - Singh O, Jeba

PY - 2026/5/1

Y1 - 2026/5/1

N2 - This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system’s adaptability to various environmental conditions, with an average response time of 3.2ms and 92% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.

AB - This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system’s adaptability to various environmental conditions, with an average response time of 3.2ms and 92% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.

KW - Artificial Intelligence

KW - Environmental Monitoring

KW - Neuromorphic Computing

KW - Stream Processing Analytics

KW - Sustainable Energy

KW - Triboelectric Nanogenerator

UR - https://link.springer.com/chapter/10.1007/978-3-032-18910-3_39

UR - https://www.scopus.com/pages/publications/105040505458

U2 - 10.1007/978-3-032-18910-3_39

DO - 10.1007/978-3-032-18910-3_39

M3 - Conference publication

AN - SCOPUS:105040505458

SN - 9783032189097

VL - 3

T3 - Lecture Notes in Networks and Systems (LNNS)

SP - 431

EP - 441

BT - Proceedings of 5th International Conference on Computing and Communication Networks

A2 - Nguyen, Gia-Nhu

A2 - Swaroop, Abhishek

A2 - Shukla, Pancham

PB - Springer, Cham

T2 - 5th International Conference on Computing and Communication Networks, ICCCN 2025

Y2 - 1 August 2025 through 3 August 2025

ER -

Sungheetha A, Blake J, Rajesh Sharma R, Mahapatra S, Singh O J. AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring. In Nguyen GN, Swaroop A, Shukla P, editors, Proceedings of 5th International Conference on Computing and Communication Networks: ICCCN 2025. Vol. 3. Springer, Cham. 2026. p. 431-441. (Lecture Notes in Networks and Systems (LNNS)). doi: 10.1007/978-3-032-18910-3_39