Skip to main content

Advertisement

SpringerLink
Log in

DCTP Architecture for Data-Centric Applications of Wireless Sensor Networks

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

Implementing reliable end-to-end transmission and reduced congestion in a single transport layer protocol is a critical issue for wireless sensor networks (WSNs). These issues degrade the performance of the WSN; since the devices used to configure the networks are resource-constrained ones. If the protocol manages without any one of the above-said problems which will not be a complete solution for transport layer issues in WSN. Some of the data-centric applications of WSNs need the reliable transmission of data packets with reduced congestion. To addresses the same, this paper proposed the data-centric transport layer protocol (DCTP). It effectively addresses problems like reliable delivery and congestion-free transmission. One of the major problems of WSNs is cluster head (CH) election and channel assignment to the members of CH. In this paper, the modified black widow optimization is used to select the CH which leads to reduced congestion and balanced energy utilization among the nodes. In a separate phase, the reliability of the event-to-sink transmission has also been addressed. Hence this proposed DCTP will be ultimately suitable for the data-centric applications of WSNs and ensures reliability and energy efficiency (i.e., 6.2315 mJ and 7.825 mJ) in the case of varying data rate and the number of nodes.

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

Similar content being viewed by others

References

  • Adwitiya S, Lobiyal DK (2015) Prediction models for energy efficient data aggregation in wireless sensor network. Wirel Pers Commun 84:1325–1343

    Article  Google Scholar 

  • Almomani I, Alenezi M (2018) Efficient denial of service attacks detection in wireless sensor networks. J Inf Sci Eng 34:977–1000

    Google Scholar 

  • Buddha S, Daya Krishnan L (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Hum Centric Comput Inf Sci 2:2–13

    Article  Google Scholar 

  • Chen L, Liu W, Gong D, Y, Chen Y (2020) Cluster-based routing algorithm for WSN based on subtractive clustering. In: 2020 International wireless communications and mobile computing (IWCMC), pp 403–406. https://doi.org/10.1109/IWCMC48107.2020.9148244

  • Ducrocq T, Mitton N, Hauspie M (2013) Energy-based clustering for wireless sensor network lifetime optimization. In: 2013 IEEE wireless communications and networking conference (WCNC), pp 968–973. https://doi.org/10.1109/WCNC.2013.6554695

  • Esmaeil R, Safieh G (2018) Energy-aware data aggregation in wireless sensor networks using particle swarm optimization algorithm. Am J Inf Sci Comput Eng 4:1–6

    Google Scholar 

  • Hayyolalam V, Kazem AAP (2020) Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intell 87:1–28

    Article  Google Scholar 

  • He B, Li G (2017) Intelligent self-adaptation data behavior control inspired by speech acts. ACM Trans Sens Netw (TOSN) 13:1–32

    Article  Google Scholar 

  • Illsoo S, Jong-Ho L, Sang Hyun L (2016) Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Commun Lett 20:558–561

    Article  Google Scholar 

  • Jie C, Lili S, Hong Z, Yan X, Lu L (2020) Energy-efficient privacy-preserving data aggregation protocols based on slicing. EURASIP J Wirel Commun Netw 2020(19):1–12

    Google Scholar 

  • Jitendra K, Ram SV, Sarita S (2017) An approach for data aggregation strategy in wireless sensor network using MAC authentication. Adv Comput Sci Technol 10:1037–1047

    Google Scholar 

  • Kalaivanan K, Bhanumathi V (2018) Unmanned aerial vehicle based reliable and energy efficient data collection from red alerted area using wireless sensor networks with IoT. J Inf Sci Eng 35:521–536

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks, 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  • Khediri SE, Thaljaoui A, Dallali A, Kachouri A (2018) Clustering algorithm in wireless sensor networks based on shortest path. 2018 30th International conference on microelectronics (ICM), pp. 335–338

  • Kumar P, Sunil Kumar V (2009) An adaptive data aggregation algorithm in wireless sensor network with Bursty source. Wirel Sens Netw 3:222–232

    Google Scholar 

  • Kumar B, Tiwari UK, Kumar S (2020) Energy efficient quad clustering based on K-means algorithm for wireless sensor network. In: 2020 Sixth international conference on parallel, distributed and grid computing (PDGC), pp 73–77. https://doi.org/10.1109/PDGC50313.2020.9315853

  • Li G, He B, Wang Z, Xu S, Huang H (2020) A swarm optimization-enhanced data aggregation tree based on a nonuniform clustering structure for long and linear wireless sensor networks. Wirel Pers Commun 112:1–11

    Article  Google Scholar 

  • Liu X, Mei K, Yu S (2020) Clustering algorithm in wireless sensor networks based on differential evolution algorithm. 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC), pp 478–482. https://doi.org/10.1109/ITNEC48623.2020.9085089

  • Mirzaie M, Mazinani SM (2020) AFLCH: Self-adaptive unequal fuzzy based clustering of heterogeneous sensors in wireless sensor networks. 2020 28th Iranian conference on electrical engineering (ICEE) pp 1–5. https://doi.org/10.1109/ICEE50131.2020.9260624

  • Qu S, Zhao L, Xiong Z (2020) Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control. Neural Comput Appl 32:13505–13520

    Article  Google Scholar 

  • Ran G, Zhang H, Gong S (2010) Improving on LEACH protocol of wireless sensor networks using fuzzy logic. J Inf Comput Sci 7:767–775

    Google Scholar 

  • Salim A, Osamy W, Khedr AM (2014) IBLEACH: intra-balanced LEACH protocol for wireless sensor networks. Wirel Netw 20:1515–1525

    Article  Google Scholar 

  • Sankardas R, Mauro C, Sanjeev S, Sushil J (2014) Secure data aggregation in wireless sensor networks: filtering out the attacker’s impact. IEEE Trans Inf Forensics Secur 9:681–694

    Article  Google Scholar 

  • Srivastava V, Tripathi S, Singh K, Son LH (2020) Energy efficient optimized rate based congestion control routing in wireless sensor network. J Ambient Intell Humaniz Comput 11:1325–1338

    Article  Google Scholar 

  • Sunil Kumar KN, Shiva Shankar B (2020) Improving the AODV route recovery mechanism using PSO in WSN. Int J Sens Wirel Commun Control 10(5):1–9

    Google Scholar 

  • Venkataramanan C, Senthil Kumar B, Subhas C (2017) Constructive relay based architecture for energy aware mobile ad-hoc networks. J Adv Res Dyn Control Syst 9:1213–1225

    Google Scholar 

  • Wimalajeewa T, Jayaweera SK (2008) Optimal power scheduling for correlated data fusion in wireless sensor networks via constrained PSO. IEEE Trans Wirel Commun 7:3608–3618

    Article  Google Scholar 

  • Yao L, Taihua Z, Erbaoe H, Ioan-Sorin C (2018) Self-learning-based data aggregation scheduling policy in wireless sensor networks. J Sens 2018:1–12

    Article  Google Scholar 

  • Zhang J, Fei L, Gao Q, Peng X-H (2011) Energy-efficient multi-hop cooperative MIMO transmission with optimal hop distance in wireless ad hoc networks. IEEE Trans Wirel Commun 10:3426–3435

    Article  Google Scholar 

  • Zhu N, Vasilakos AV (2015) A generic framework for energy evaluation on wireless sensor networks. Wirel Netw 22:1199–1220

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Vivekanandha College of Technology for Women, Tiruchengode, 637205, India

    Venkataramanan Chakrapani

  2. Centre for Communications and Signal Processing, Sree Vidyanikethan Engineering College, Tirupathi, 517 012, India

    Senthil Kumar Babu

Authors
  1. Venkataramanan Chakrapani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Senthil Kumar Babu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Venkataramanan Chakrapani.

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

Chakrapani, V., Babu, S.K. DCTP Architecture for Data-Centric Applications of Wireless Sensor Networks. Iran J Sci Technol Trans Electr Eng 45, 1203–1215 (2021). https://doi.org/10.1007/s40998-021-00448-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40998-021-00448-3

Keywords

Search

Navigation