Research Article
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Year 2019, Volume: 19 Issue: 1, 1 - 11, 01.01.2019

Abstract

References

  • J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, Vol. 29 (7), pp. 1645-1660, 2013.
  • O. Vermesan, P. Friess, Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, River Publishers series in Communications 2013.
  • P. Visconti, A. Lay-Ekuakille, P. Primiceri, G. Cavalera, Wireless Energy Monitoring System of Photovoltaic Plants with Smart Anti-Theft solution integrated with Household Electrical Consumption’s Control Unit Remotely Controlled by Internet, International Journal on Smart Sensing and Intelligent Systems, Vol. 9 (2), pp. 681-708, 2016.
  • R. Hemalatha, R. Ramaprabha, S. Radha: A comprehensive analysis on sizing of solar energy harvester elements for wireless sensor motes. International Journal on Smart Sensing and Intelligent Systems, Vol. 8 (1), pp. 291-315, 2015.
  • G. Zheng, Z. Ho, E.A. Jorswieck, B. Ottersten, Information and energy cooperation in cognitive radio networks, IEEE Transactions on Signal Processing, vol. 62, no. 9, pp. 2290-2303, 2014.
  • Z. Ding, S. M. Perlaza, I. Esnaola, and H. V. Poor, Power allocation strategies in energy harvesting wireless cooperative networks, IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 846-860, 2014.
  • H. Chen, Y. Li, J. L. Rebelatto, B. F. U. Filhoand, and B. Vucetic, Harvest-then-cooperate: wireless-powered cooperative communications, IEEE Transactions on Signal Processing, Vol. 63, Iss. 7, 2015.
  • O. Ozel, K. Tutuncuoglu, J. Yang, S. Ulukus, A. Yener, Transmission with energy harvesting nodes in fading wireless channels: Optimal policies, IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1732-1743, 2011.
  • Farahani, S., 2008, ZigBee Wireless Networks and Transceivers, Newnes, Elsevier, Oxford, UK, ISBN: 978-0-7506-8393-7.
  • Gislason, D., 2004, ZigBee wireless networking, Elsevier, Oxford, UK, ISBN: 978-0-7506-8597-9.
  • Wang, C., Jiang, T., Zhang, Q., 2014, ZigBee network protocols and applications, CRC Press, Taylor & Francis Group, FL, USA, ISBN 978-1-4398-1601-1.
  • Chen, D., Nixon, M., Mok, A., 2010, WirelessHART: real-time mesh network for industrial automation, Springer, NY, USA, ISBN 978-1-4419-6046-7.
  • Güngör, V. Ç., Hancke, G. P., 2013, Industrial wireless sensor networks: applications, protocols, and standards, CRC Press, Taylor & Francis Group, FL, USA, ISBN 978-1-4665-0051-8.
  • Wang, A., Chandrakasan, A., 2002, Energy-efficient DSPs for wireless sensor networks, IEEE Signal Processing magazine, 19 (4), 68-78.
  • Sinha, A., Chandrakasan, A., 2001, Dynamic power management in wireless sensor networks, IEEE Design & Test of Computers, 18 (2), 62-74.
  • Min, R., Furrer, T., Chandrakasan, A., 2000, Dynamic voltage scaling techniques for distributed microsensor networks, IEEE Computer Society Workshop on VLSI, 27-28 April 2000, Orlando, FL, USA, pp. 43-46.
  • Pering, T., Burd, T., Brodersen, R., 1998, The simulation and evaluation of dynamic voltage scaling algorithms, International Symposium on Low Power Electronics and Design, 10-12 August 1998, Monterey, CA, USA, 76-81.
  • Burd, T., Pering, T., Stratakos, A., Brodersen, R., 2000, A dynamic voltage scaled microprocessor system, IEEE Journal of Solid-State Circuits, 35 (11), 1571-1580.
  • Pillai, P., Shin, K. G., 2001, Real-time dynamic voltage scaling for low-power embedded operating systems, The 18th ACM symposium on Operating systems principles, 21-24 October 2001, Chateau Lake Louise, Banff, Canada, 89-102.
  • Qadi, A., Goddard, S., Farritor, S., 2003, A dynamic voltage scaling algorithm for sporadic tasks, The 24th IEEE Real-Time Systems Symposium, 3-5 December 2003, Cancun, Mexico, 52-62.
  • Yu,Y.,Prasanna,V.K.,2005,Energy balanced task allocation for collaborative processing in wireless sensor networks, Mobile Networks and Applications, 10 (1-2), 115-131.
  • Zhuo, J., Chakrabarti, C., 2008, Energy-efficient dynamic task scheduling algorithms for DVS systems, ACM Transactions on Embedded Computing Systems, 7 (2), 1-22.

The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications

Year 2019, Volume: 19 Issue: 1, 1 - 11, 01.01.2019

Abstract

DOI: 10.26650/electrica.2018.28092

With the increasing popularity of embedded
systems that consist of identifying, sensing, processing, and communication
capabilities, the Internet of Things (IoT) has enabled many new application
scenarios in diverse scientific fields and provided an important opportunity to
build efficient industrial systems and applications. Most embedded systems
consume power provided by fixed batteries with limited capacity. However, the
main disadvantage of batteries is that they must be periodically replaced with
new ones or recharged when they are depleted. This kind of maintenance process
increases the cost and restricts the use in inaccessible places. Considering
the limitations of battery power, alternative energy sources are required for
interconnected devices to operate efficiently and effectively. Harvesting or
scavenging energy from the environment is an important strategy to design
self-powered systems employed in the IoT domain. Traditional energy harvesting
applications use large energy storage devices to supply the power to the system
whenever needed. Batteryless energy harvesting techniques have advantages to
operate long periods of time without maintenance. In this study, we designed
and implemented a supercapacitor-based, solar-powered wireless embedded system
that can operate autonomously without the need of maintenance and battery
replacement. Our goal is to explore and analyze the applicability and
usefulness of batteryless wireless data communication and self-powered smart
devices using energy harvesting technique in the IoT environment. In our
experiments, we observed that our prototype boards operate well with low-power
consumption without any performance degradation.

Cite this article as: Yüksel ME. Design and
Implementation of A Batteryless Wireless Embedded System for IoT Applications.
Electrica, 2019; 19(1): 1-11.

References

  • J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, Vol. 29 (7), pp. 1645-1660, 2013.
  • O. Vermesan, P. Friess, Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems, River Publishers series in Communications 2013.
  • P. Visconti, A. Lay-Ekuakille, P. Primiceri, G. Cavalera, Wireless Energy Monitoring System of Photovoltaic Plants with Smart Anti-Theft solution integrated with Household Electrical Consumption’s Control Unit Remotely Controlled by Internet, International Journal on Smart Sensing and Intelligent Systems, Vol. 9 (2), pp. 681-708, 2016.
  • R. Hemalatha, R. Ramaprabha, S. Radha: A comprehensive analysis on sizing of solar energy harvester elements for wireless sensor motes. International Journal on Smart Sensing and Intelligent Systems, Vol. 8 (1), pp. 291-315, 2015.
  • G. Zheng, Z. Ho, E.A. Jorswieck, B. Ottersten, Information and energy cooperation in cognitive radio networks, IEEE Transactions on Signal Processing, vol. 62, no. 9, pp. 2290-2303, 2014.
  • Z. Ding, S. M. Perlaza, I. Esnaola, and H. V. Poor, Power allocation strategies in energy harvesting wireless cooperative networks, IEEE Transactions on Wireless Communications, vol. 13, no. 2, pp. 846-860, 2014.
  • H. Chen, Y. Li, J. L. Rebelatto, B. F. U. Filhoand, and B. Vucetic, Harvest-then-cooperate: wireless-powered cooperative communications, IEEE Transactions on Signal Processing, Vol. 63, Iss. 7, 2015.
  • O. Ozel, K. Tutuncuoglu, J. Yang, S. Ulukus, A. Yener, Transmission with energy harvesting nodes in fading wireless channels: Optimal policies, IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1732-1743, 2011.
  • Farahani, S., 2008, ZigBee Wireless Networks and Transceivers, Newnes, Elsevier, Oxford, UK, ISBN: 978-0-7506-8393-7.
  • Gislason, D., 2004, ZigBee wireless networking, Elsevier, Oxford, UK, ISBN: 978-0-7506-8597-9.
  • Wang, C., Jiang, T., Zhang, Q., 2014, ZigBee network protocols and applications, CRC Press, Taylor & Francis Group, FL, USA, ISBN 978-1-4398-1601-1.
  • Chen, D., Nixon, M., Mok, A., 2010, WirelessHART: real-time mesh network for industrial automation, Springer, NY, USA, ISBN 978-1-4419-6046-7.
  • Güngör, V. Ç., Hancke, G. P., 2013, Industrial wireless sensor networks: applications, protocols, and standards, CRC Press, Taylor & Francis Group, FL, USA, ISBN 978-1-4665-0051-8.
  • Wang, A., Chandrakasan, A., 2002, Energy-efficient DSPs for wireless sensor networks, IEEE Signal Processing magazine, 19 (4), 68-78.
  • Sinha, A., Chandrakasan, A., 2001, Dynamic power management in wireless sensor networks, IEEE Design & Test of Computers, 18 (2), 62-74.
  • Min, R., Furrer, T., Chandrakasan, A., 2000, Dynamic voltage scaling techniques for distributed microsensor networks, IEEE Computer Society Workshop on VLSI, 27-28 April 2000, Orlando, FL, USA, pp. 43-46.
  • Pering, T., Burd, T., Brodersen, R., 1998, The simulation and evaluation of dynamic voltage scaling algorithms, International Symposium on Low Power Electronics and Design, 10-12 August 1998, Monterey, CA, USA, 76-81.
  • Burd, T., Pering, T., Stratakos, A., Brodersen, R., 2000, A dynamic voltage scaled microprocessor system, IEEE Journal of Solid-State Circuits, 35 (11), 1571-1580.
  • Pillai, P., Shin, K. G., 2001, Real-time dynamic voltage scaling for low-power embedded operating systems, The 18th ACM symposium on Operating systems principles, 21-24 October 2001, Chateau Lake Louise, Banff, Canada, 89-102.
  • Qadi, A., Goddard, S., Farritor, S., 2003, A dynamic voltage scaling algorithm for sporadic tasks, The 24th IEEE Real-Time Systems Symposium, 3-5 December 2003, Cancun, Mexico, 52-62.
  • Yu,Y.,Prasanna,V.K.,2005,Energy balanced task allocation for collaborative processing in wireless sensor networks, Mobile Networks and Applications, 10 (1-2), 115-131.
  • Zhuo, J., Chakrabarti, C., 2008, Energy-efficient dynamic task scheduling algorithms for DVS systems, ACM Transactions on Embedded Computing Systems, 7 (2), 1-22.
There are 22 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mehmet Erkan Yüksel 0000-0001-8976-9964

Publication Date January 1, 2019
Published in Issue Year 2019 Volume: 19 Issue: 1

Cite

APA Yüksel, M. E. (2019). The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications. Electrica, 19(1), 1-11.
AMA Yüksel ME. The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications. Electrica. January 2019;19(1):1-11.
Chicago Yüksel, Mehmet Erkan. “The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications”. Electrica 19, no. 1 (January 2019): 1-11.
EndNote Yüksel ME (January 1, 2019) The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications. Electrica 19 1 1–11.
IEEE M. E. Yüksel, “The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications”, Electrica, vol. 19, no. 1, pp. 1–11, 2019.
ISNAD Yüksel, Mehmet Erkan. “The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications”. Electrica 19/1 (January 2019), 1-11.
JAMA Yüksel ME. The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications. Electrica. 2019;19:1–11.
MLA Yüksel, Mehmet Erkan. “The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications”. Electrica, vol. 19, no. 1, 2019, pp. 1-11.
Vancouver Yüksel ME. The Design and Implementation of a Batteryless Wireless Embedded System for IoT Applications. Electrica. 2019;19(1):1-11.