Skip to main content

Advertisement

SpringerLink
Log in

AI for dynamic packet size optimization of batteryless IoT nodes: a case study for wireless body area sensor networks

  • S.I. : Applying Artificial Intelligence to the Internet of Things
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Packet size optimization, with the purpose of minimizing the wireless packet transmission energy consumption, is crucial for the energy efficiency of the Internet of Things nodes. Meanwhile, energy scavenging from ambient energy sources has gained a significant attraction to avoid battery issues as the number of nodes increasingly grows. Packet size optimization algorithms have so far been proposed for battery-powered networks that have limited total energy with continuous power availability to prolong their lifetime. On the other hand, batteryless networks based on energy harvesting offer unlimited total energy with the interruption in availability. This is due to changing ambient conditions or the required time for harvesting and storing in small capacitors. Packet size optimization of batteryless networks has not been addressed so far. In this paper, an AI-based packet size optimization algorithm is proposed for batteryless networks that consider the amount of harvested energy at each node. Therefore, packet size is optimized dynamically for each round of data transmission. The proposed method is then evaluated via numerical simulations for a heterogenous wireless body area sensor network as a case study, considering 1-hop, cooperative, and 2-hop communication networks. Cooperative topology yields optimum energy efficiency for highly dynamic sensors, such as ECG, while 2-hop has shown to be optimum for the same type of sensors in battery-powered networks. Also, for sensors with slower dynamics such as body temperature, 1-hop turns out to be optimum in networks solely dependent on energy scavenging while cooperative topology is optimum for battery-powered networks. The algorithm applies to any heterogeneous fully batteryless networks to dynamically optimize packet size at each transmission instance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

ACK:

Acknowledgment

ARQ:

Error control scheme

AWGN:

Additive white Gaussian noise

BER:

Bit error rate

BPSK:

Binary phase shift keying

DN:

Destination node

LOS:

Line of sight

NLOS:

Non line of sight

RN:

Relay node

SN:

Source node

SNR:

Signal-to-noise ratio

WBASN:

Wireless body area sensor network

References

  1. Wu F, Redoute JM, Yuce MR (2018) WE-safe: a self-powered wearable IoT sensor network for safety applications based on lora. IEEE Access 6:40846–40853

    Article  Google Scholar 

  2. Buxi D et al (2018) Systolic time interval estimation using continuous wave radar with on-body antennas. IEEE J Biomed Heal Informatics 22(1):129–139

    Article  Google Scholar 

  3. Wu T, Redouté JM, Yuce MR (2018) A wireless implantable sensor design with subcutaneous energy harvesting for long-term IoT healthcare applications. IEEE Access 6:35801–35808

    Article  Google Scholar 

  4. Wu T, Wu F, Redoute JM, Yuce MR (2017) An autonomous wireless body area network implementation towards IoT connected healthcare applications. IEEE Access 5:11413–11422

    Article  Google Scholar 

  5. Demir SM, Al-Turjman F, Muhtaroglu A (2018) Energy scavenging methods for WBAN applications: a review. IEEE Sens J 18(16):6477–6488

    Article  Google Scholar 

  6. Zhang R, Nayak A, Yu J (2019) Sleep scheduling in energy harvesting wireless body area networks. IEEE Commun Mag 57(2):95–101

    Article  Google Scholar 

  7. Akhtar F, Rehmani MH (2017) Energy harvesting for self-sustainable wireless body area networks. IT Prof 19(2):32–40

    Article  Google Scholar 

  8. Hamid R, Yuce MR (2017) A wearable energy harvester unit using piezoelectric–electromagnetic hybrid technique. Sensors Actuators A Phys. 257:198–207

    Article  Google Scholar 

  9. Pandey B, Jain A, Azeem MF (2017) Self-sustaining WBAN implants for biomedical applications. In: Proceedings of 2016 2nd international conference on applied and theoretical computing and communication technology iCATccT 2016, pp 494–503

  10. Kappel R, Pachler W, Auer M, Pribyl W, Hofer G, Holweg G (2013) Using thermoelectric energy harvesting to power a self-sustaining temperature sensor in body area networks. In: Proceedings of the IEEE international conference on industrial technology, pp 787–792

  11. Promwongsa N, Sanguankotchakorn T (2016) “Packet size optimization for energy-efficient 2-hop in multipath fading for WBAN. In: Proceedings of Asia-Pacific conference on communications APCC 2016, pp 445–450

  12. Deepak KS, Babu AV (2012) Packet size optimization for energy efficient cooperative wireless body area networks. In: 2012 annual IEEE India conference. INDICON 2012, pp 736–741

  13. Domingo MC (2011) Packet size optimization for improving the energy efficiency in body sensor networks. ETRI J 33(3):299–309

    Article  Google Scholar 

  14. Jacob L, Member S (2018) Energy efficient and reliable communication in IEEE 802.15.6 Ir-Uwb. In: 2018 international conference on advances in computing, communications and informatics (ICACCI), pp 2352–2358

  15. Zang W, Miao F, Gravina R, Sun F, Fortino G, Li Y (2019) CMDP-based intelligent transmission for wireless body area network in remote health monitoring. Neural Comput Appl 32:829–837

    Article  Google Scholar 

  16. Bai T et al (2019) An optimized protocol for QoS and energy efficiency on wireless body area networks. Peer-to-Peer Netw Appl 12(2):326–336

    Article  Google Scholar 

  17. Takizawa K et al (2008) Channel models for wireless body area networks. In: Proceedings of the 30th annual international conference of the IEEE engineering in medicine and biology society, EMBS’08—personalized healthcare through technology, pp 1549–1552

  18. Ju M, Kim IM (2010) Error performance analysis of BPSK modulation in physical-layer network-coded bidirectional relay networks. IEEE Trans Commun 58(10):2770–2775

    Article  Google Scholar 

  19. IEEE (2018) IEEE draft standard for safety levels with respect to human exposure to electric, magnetic and electromagnetic fields, 0 Hz to 300 GHz. In: IEEE PC95.1/D3.4, pp 1–315

  20. Krieger UR, Kumar BK (2018) Modeling the performance of ARQ error control in an LTE transmission system. In: German R, Hielscher KS, Krieger U (eds) Measurement, modelling and evaluation of computing systems, MMB 2018. Lecture Notes in Computer Science, vol 10740. Springer, Cham

    Google Scholar 

  21. Osouli Tabrizi H, Jayaweera HMPC, Muhtaroglu A (2019) Fully integrated autonomous interface with maximum power point tracking for energy harvesting TEGs with high power capacity. IEEE Trans Power Electron 8993(c):1

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sustainable Environment and Energy Systems, Middle East Technical University, Northern Cyprus Campus, Kalkanli, Guzelyurt, Mersin 10, Turkey

    Hamed Osouli Tabrizi

  2. Artificial Intelligence Department, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

Authors
  1. Hamed Osouli Tabrizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fadi Al-Turjman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamed Osouli Tabrizi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Osouli Tabrizi, H., Al-Turjman, F. AI for dynamic packet size optimization of batteryless IoT nodes: a case study for wireless body area sensor networks. Neural Comput & Applic 32, 16167–16178 (2020). https://doi.org/10.1007/s00521-020-04813-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04813-x

Keywords

Search

Navigation