Abstract
Owing to the inexpensive, adaptable, and scalable features of WSNs (wireless sensor networks), the network is viewed as a vital technology to support distinct smart applications. The primary issue is to strengthen the lifespan of the network, as the node devices have bounded lifecycles owing to notable power constraints. Thus, to sustain the lifespan of WSNs, it is crucial to reinforce the energy administration at the node devices, as they are routinely deployed in secluded areas. Several energy management strategies have recently been used in this context. Among these alternatives, cluster-chain hybrid networks have demonstrated the ability to significantly reduce the node energy usage. However, the chain formation techniques often result in increased latency in large-scale scenarios. Similarly, it has been proven that coding techniques preserves the network reliability and energy efficiency. While utilising the benefits of these coding schemes it is necessary to carefully secure the topology, link quality, and coding vectors. In this paper, an improved energy-saving distributed cluster–chain K-communication mechanism for smart sensor networks is proposed. In the proposed solution named IESDCC-KM, a dual K-means technique is adapted to form unequal clusters. IESDCC-KM implements a competing and ideal weight function to select the cluster heads and establishes perpendicular chain trees among heads based on their distances and a threshold value. It establishes gradient-based dis-joint multiple routes from the source to the destination and implements discrete wavelet transform to compress the accumulated inter-cluster data. At the intermediate nodes on the path along the source and destination, the packets from the different link nodes are encoded utilizing linear network coding. MATLAB 2018b experimental analysis demonstrates the proposed IESDCC-KM improves the reception ratio by 0.20 to 0.03 at 0.99 to 0.96 precision rate. Furthermore, it showed a 28.57% throughput increase with a 1% reduction in delay and a 2.86% boost in energy-saving for 4000 rounds.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not Applicable.
References
Ahlswede R, Cai N, Li SYR, et al (2000) Network information flow. IEEE Trans Inf Theory 46(3):1204–1216
Amanowicz M, Krygier J (2018) On applicability of network coding technique for 6LoWPAN-based sensor networks. Sensors 18(6):1718
Bidaki M, Ghaemi R, Tabbakh S (2016a) Towards energy efficient kMEANS based clustering scheme for wireless sensor networks. Int J Grid Distrib Comput 9(7):265–276
Bidaki M et al (2016b) Towards energy efficient k-MEANS based clustering scheme for wireless sensor networks. Int J Grid Distrib Comput 9(7):265–276
Bu B (2022) Network coding data distribution technology between streams of emergency system based-wireless multi-hop network. Comput Commun 186:22–32
Echoukairi H, Kada A, Bouragba K, Ouzzif M (2017) A novel centralized clustering approach based on K-means algorithm for a wireless sensor network. In: Computing conference
Fathima SKS, Witkowski U (2022) Enhanced energy-efficient fuzzy logic clustering and network coding strategy for wireless sensor networks (EEE-FL-NC). In: Bianchini M, Piuri V, Das S, Shaw RN (eds) Advanced computing and intelligent technologies. Lecture notes in networks and systems, 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_5
Harb H, Makhoul A, Laiymani D, Jaber A, Tawil R (2014) K-means based clustering approach for data aggregation in periodic sensor networks. In: IEEE 10th international conference on wireless and mobile computing, networking and communications (WiMob):434–441
Heinzelman WR, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless micro sensor networks. IEEE Trans Wirel Commun 1(4):660–670
Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energyefficient communication protocol for wireless micro-sensor networks. In EEEE computer society proceedings of the thirty third hawaii international conference on system sciences (HICSS '00), 1–10
Huynh T-T et al (2017) Delay constraint energy-efficient routing based on lagrange relaxation in wireless sensor networks. IET Wirel Sensor Syst 7(5):138–145
Kulaib R, Shubair RM, Al-Qutayri MA, JW PN (2015) Improved DVhop localization using node repositioning and clustering. In: International conference on communications, signal processing, and their applications (ICCSPA15)
Kumar GJR, Agbulu GP, Rahul TV et al (2022) A cloud-assisted mesh sensor network solution for public zone air pollution real-time data acquisition. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-022-03704-4
Kumar et al. (2014) A hybrid clustering algorithm for optimal clusters in Wireless sensor networks. In Electrical, electronics and computer science students conference (SCEECS):1–6
Lee HC, Ke KH, Fang YM, Lee BJ, Chan TC (2017) Open-source wireless sensor system for long-term monitoring of slope movement. IEEE Trans Instrum Meas 66(4):767–776
Lindsey S, Raghavendra C (2002) PEGASIS: Power-efficient gathering in sensor information systems. In: IEEE aerospace conference IEEE aerospace conference, BigSky, Montana
Mahboub A, Arioua M, En-Naimi E (2017) Energy-efficient hybrid k-means algorithm for clustered wireless sensor networks. Int J Electr Comput Eng (IJECE) 7(4):2054
Mechta S, Harous I, Alem, Khebbab D (2014) LEACH-CKM: low energy adaptive clustering hierarchy protocol with K-means and MTE. In 10th international conference on innovations in information technology (IIT)
Miao L, Djouani K, Kurien A, Noel G (2012) Network coding and competitive approach for gradient-based routing in wireless sensor networks. Ad Hoc Netw 10(6):990–1008
Migabo ME, Olwal TO, Djouani K, Kurien AM (2017) Cooperative and adaptive network coding for gradient-based routing in wireless sensor networks with multiple sinks. J Comp Netw Commun 2017:1–10
Periyasamy S, Khara S, Thangavelu S (2016) Balanced cluster head selection based on modified k-means in a distributed wireless sensor network. Int J Distrib Sens Netw 12(3):5040475
Pius GA, Kumar GJR, Juliet VA et al (2022) PECDF-CMRP: a power-efficient compressive data fusion and cluster-based multi-hop relay-assisted routing protocol for IoT sensor networks. Wirel Pers Commun. https://doi.org/10.1007/s11277-022-09905-6
Qiu Y et al (2017) Multi-gradient routing protocol for wireless sensor networks. China Commun 14(3):118–129
Raja AB (2022) A review on wireless sensor networks: routing. Wirel Pers Commun. https://doi.org/10.1007/s11277-022-09583-4
Randhawa S, Jain S (2016) Performance analysis of LEACH with machine learning algorithms in wireless sensor networks. Int J Comput Appl 147(2):7–12
RayDe AD (2016) Energy efficient clustering protocol based on Kmeans (EECPK-means)-midpoint algorithm for enhanced network lifetime in wireless sensor network. IET Wirel Sens Syst 6(6):181–191
Razzaq M,. Ningombam DD, Shin (2018) Energy efficient K-means clustering-based routing protocol for WSN using optimal packet size. In: International Conference on Information Networking (ICOIN)
SasirekhaSwamynathan SS (2017) Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. J Commun Netw 19(4):392–401
Sheikhpour R, Jabbehdari S, Khademzadeh A (2012) A cluster chain- based routing protocol for balancing energy consumption in wireless sensor networks. Int J Multimed Ubiquitous Eng 7(2):1–16
Tan ND, Vie ND (2015) SCBC: sector-chain based clustering routing protocol for energy efficiency in a heterogeneous wireless sensor network. In: international conference on advanced technologies for communications (ATC)
Tang F, You I, Guo S, Guo M, Ma Y (2012) A chain-cluster based routing algorithm for wireless sensor networks. J Intell Manuf 23(4):1305–1313
Thomas D et al (2021) QoS-aware energy management and node scheduling schemes for sensor network-based surveillance applications. IEEE Access 9:n3065-3096. https://doi.org/10.1109/ACCESS.2020.3046619
Wu FY, Yang K, Yang Z (2018) Compressed acquisition and denoising recovery of EMGdi signal in WSNs and IoT. IEEE Trans Ind Inform 14(5):2210–2219
Xing L, Vokkarane VM, Sun YL (2014) A time-dependent link failure model for wireless sensor networks. In: Reliability and maintainability symposium
Acknowledgements
Not Applicable.
Funding
Not Applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interest
The authors have no relevant financial or nonfinancial interests to disclose.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Pius Agbulu, G., Joselin Retna Kumar, G. & Gunasekar, S. IESDCC-KM: an improved energy-saving distributed cluster–chain K-communication scheme for smart sensor networks. Int J Syst Assur Eng Manag 15, 4443–4455 (2024). https://doi.org/10.1007/s13198-024-02456-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-024-02456-y