Abstract
Data aggregation is essential in many modern wireless sensor network-based applications as its usage can guarantee a significant increase in the Quality of Service in this technology. In this paper, we consider a geographically deployed group of synchronous wireless sensor nodes employing the distributed flooding algorithm for data aggregation of sensor-measured values. As previously identified in many other papers, properly bounded algorithms are crucial for wireless sensor networks’ effective and long-lasting operation. Therefore, we apply four upper bounds on the graph diameter based on Laplacian eigenvalues to stop executing the mentioned algorithm in the synchronous mode. The purpose of this paper is to provide a comparative study of these four stopping criteria in random graphs of various connectivity and graph order in order to identify the best-performing approach in terms of the number of iterations required for the algorithm to be completed. Moreover, the performance of these approaches is compared to the optimal solution for stopping the distributed flooding algorithm in the synchronous mode, i.e., bounding the algorithm based on the exact value of the graph diameter. Thus, the main contribution of this paper is to analyze the potential applicability of the examined upper bounds to stopping the mentioned concerned algorithm and identify its optimal stopping criterion based on the Laplacian spectrum for real-world scenarios.
Keywords
- Data aggregation
- Distributed algorithms
- Flooding algorithm
- Graph diameter
- Laplacian eigenvalues
- Stopping criterion
- Synchronous mode
- Upper bounds
- Wireless sensor networks
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge (2005). https://doi.org/10.1017/CBO9780511841224
Yang, B., et al.: Edge intelligence for autonomous driving in 6G wireless system: design challenges and solutions. IEEE Wirel. Commun. 28, 40–47 (2021). https://doi.org/10.1109/MWC.001.2000292
Zhou, Y., Liu, Y., Zhao, Y., Huang, P.: Appointed-time average consensus over directed networks. IEEE Trans. Circuits Syst. II: Express Briefs 69, 2922–2926 (2022). https://doi.org/10.1109/TCSII.2022.3152521
Zhu, L., et al.: A wearable, high-resolution, and wireless system for multichannel surface electromyography detection. IEEE Sens. J. 21, 9937–9948 (2021). https://doi.org/10.1109/JSEN.2021.3058987
Moioli, R.C., et al.: Neurosciences and wireless networks: The potential of brain-type communications and their applications. IEEE Commun. Surv. Tutor. 23, 1599–1621 (2021). https://doi.org/10.1109/COMST.2021.3090778
Chowdhury, M.Z., Shahjalal, M., Ahmed, S., Jang, Y.M.: 6G wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open J. Commun. Soc. 1, 957–975 (2020). https://doi.org/10.1109/OJCOMS.2020.3010270
Melgarejo, D.C., et al.: Optimizing flying base station connectivity by RAN slicing and reinforcement learning. IEEE Access 10, 53746–53760 (2022). https://doi.org/10.1109/ACCESS.2022.3175487
Kuthadi, V.M., Selvaraj, R., Baskar, S., Shakeel, P.M., Ranjan, A.: Optimized energy management model on data distributing framework of wireless sensor network in IoT system. Wireless. Pers. Commun. XX, 1–27 (2021). https://doi.org/10.1007/s11277-021-08583-0
Lin, J.C.: Safety of wireless power transfer. IEEE Access 9, 125342–125347 (2021). https://doi.org/10.1109/ACCESS.2021.3108966
Fahmy, H.M.A.: Concepts, Applications, Experimentation and Analysis of Wireless Sensor Networks. SCT, Springer, Cham (2021). https://doi.org/10.1007/978-3-030-58015-5
Bhushan, B., Sahoo, G.: Recent advances in attacks, technical challenges, vulnerabilities and their countermeasures in wireless sensor networks. Wirel. Pers. Commun. 98(2), 2037–2077 (2017). https://doi.org/10.1007/s11277-017-4962-0
Phani Rama Krishna, K., Thirumuru, R.: Optimized energy-efficient multi-hop routing algorithm for better coverage in mobile wireless sensor networks. Int. J. Integr. Sci. Technol. 10, 100–109 (2022)
Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Comput. Netw. 52, 2292–2330 (2008). https://doi.org/10.1016/j.comnet.2008.04.002
Landaluce, H., Arjona, L., Perallos, A., Falcone, F., Angulo, I., Muralter, F.: A review of IoT sensing applications and challenges using RFID and wireless sensor networks. Sensors 20, 2495 (2020). https://doi.org/10.3390/s20092495
Vikram, R., Sinha, D., De, D., Das, A.K.: EEFFL: energy efficient data forwarding for forest fire detection using localization technique in wireless sensor network. Wirel. Netw. 26(7), 5177–5205 (2020). https://doi.org/10.1007/s11276-020-02393-1
Zhao, J., Li, G.: Study on real-time wearable sport health device based on body sensor networks. Comput. Commun. 154, 40–47 (2020). https://doi.org/10.1016/j.comcom.2020.02.045
Kenyeres, M., Kenyeres, J.: Average consensus over mobile wireless sensor networks: weight matrix guaranteeing convergence without reconfiguration of edge weights. Sensors 20, 3677 (2020). https://doi.org/10.3390/s20133677
Pragadeswaran, S., Madhumitha, S., Gopinath, S.: Certain investigation on military applications of wireless sensor network. Int. J. Adv. Res. Sci. Commun. Technol. 3, 14–19 (2021). https://doi.org/10.48175/IJARSCT-819
Kenyeres, M., Kenyeres, J.: Distributed mechanism for detecting average consensus with maximum-degree weights in bipartite regular graphs. Mathematics 9, 3020 (2021). https://doi.org/10.3390/math9233020
Abdulkarem, M., Samsudin, K., Rokhani, F.Z., A Rasid, M.F.: Wireless sensor network for structural health monitoring: a contemporary review of technologies, challenges, and future direction. Struct. Health Monit. 19, 693–735 (2020). https://doi.org/10.1177/1475921719854528
Izadi, D., Abawajy, J.H., Ghanavati, S., Herawan, T.: A data fusion method in wireless sensor networks. Sensors 15, 2964–2979 (2015). https://doi.org/10.3390/s150202964
Randhawa, S., Jain, S.: Data aggregation in wireless sensor networks: previous research, current status and future directions. Wirel. Pers. Commun. 97(3), 3355–3425 (2017). https://doi.org/10.1007/s11277-017-4674-5
Kenyeres, M., Kenyeres, J.: Comparative study of distributed consensus gossip algorithms for network size estimation in multi-agent systems. Future Internet 13, 134 (2021). https://doi.org/10.3390/fi13050134
Tran, D., Casbeer, D.W., Yucelen, T.: A distributed counting architecture for exploring the structure of anonymous active-passive networks. Automatica (Oxf) 146, 110550 (2022). https://doi.org/10.1016/j.automatica.2022.110550
Krammer, P., Habala, O., Mojžiš, J., Hluchý, L., Jurkovič, M.: Anomaly detection method for online discussion. Procedia Comput. Sci. 155, 311–318 (2019). https://doi.org/10.1016/j.procs.2019.08.045
Liu, X., Yu, J., Li, F., Lv, W., Wang, Y., Cheng, X.: Data aggregation in wireless sensor networks: from the perspective of security. IEEE Internet Things J. 7, 6495–6513 (2019). https://doi.org/10.1109/JIOT.2019.2957396
Mojžiš, J., Laclavík, M.: Relationship discovery and navigation in big graphs. Stud. Comput. Intell. 591, 109–123 (2015). https://doi.org/10.1007/978-3-319-14654-6_7
Ozdemir, S., Xiao, Y.: Secure data aggregation in wireless sensor networks: a comprehensive overview. Comput. Netw. 53, 2022–2037 (2009). https://doi.org/10.1016/j.comnet.2009.02.023
Maraiya, K., Kant, K., Gupta, N.: Wireless sensor network: a review on data aggregation. Int. J. Sci. Eng. 2, 1–6 (2011)
Kaur, M., Munjal, A.: Data aggregation algorithms for wireless sensor network: a review. Ad Hoc Netw. 100, 102083 (2020). https://doi.org/10.1016/j.adhoc.2020.102083
Chib, S., Greenberg, E.: Understanding the metropolis-hastings algorithm. Am. Stat. 49, 327–335 (1995). https://doi.org/10.1080/00031305.1995.10476177
Tidke, B., Mehta, R., Dhanani, J.: Consensus-based aggregation for identification and ranking of top-k influential nodes. Neural Comput. Appl. 32(14), 10275–10301 (2019). https://doi.org/10.1007/s00521-019-04568-0
Xiao, L., Boyd, S.: Fast linear iterations for distributed averaging. Syst. Control. Lett. 53, 65–78 (2004). https://doi.org/10.1016/j.sysconle.2004.02.022
Merris, R.: Laplacian matrices of graphs: a survey. Linear Algebra Appl. 197, 143–176 (1994). https://doi.org/10.1016/0024-3795(94)90486-3
Tang, M., Priebe, C.E.: Limit theorems for eigenvectors of the normalized Laplacian for random graphs. Ann. Stat. 46, 2360–2415 (2018). https://doi.org/10.1214/17-AOS1623
Chung, F.: The diameter and Laplacian eigenvalues of directed graphs. Electron. J. Comb. 13, 1–6 (2006). https://doi.org/10.37236/1142
Pirzada, S., Ganie, H.A., Alghamdi, A.M.: On the sum of signless Laplacian spectra of graphs. Carpathian Math. Publ. 11, 407–417 (2019). https://doi.org/10.15330/cmp.11.2.407-417
Hogben, L.: Handbook of Linear Algebra. Second Edn. CRC Press, Boca Raton (2005). https://doi.org/10.1201/b16113
Mohar, B.: Eigenvalues, diameter, and mean distance in graphs. Graphs Combin. 7, 53–64 (1991). https://doi.org/10.1007/BF01789463
Chwila, A., Zadlo, T.: On the choice of the number of Monte Carlo iterations and bootstrap replicates in Empirical Best Prediction. Stat. Transit. 21, 35–60 (2020). https://doi.org/10.21307/stattrans-2020-013
Acknowledgment
This work was supported by the Slovak Scientific Grand Agency VEGA under the contract 2/0135/23 “Intelligent sensor systems and data processing” and by the Slovak Research and Development Agency under the contract No. APVV-19-0220.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kenyeres, M., Kenyeres, J. (2023). Upper Bounds on Graph Diameter Based on Laplacian Eigenvalues for Stopping Distributed Flooding Algorithm. In: Silhavy, R., Silhavy, P. (eds) Software Engineering Research in System Science. CSOC 2023. Lecture Notes in Networks and Systems, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-031-35311-6_67
Download citation
DOI: https://doi.org/10.1007/978-3-031-35311-6_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35310-9
Online ISBN: 978-3-031-35311-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)