nach oben

The Journal of Supercomputing

Erschienen in:

04.06.2021

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

verfasst von: Seyedeh Nafiseh Sajedi, Mohsen Maadani, Meisam Nesari Moghadam

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other and central nodes over a wireless network and internet. Recently, due to the crucial importance of human well-being, IoT-enabled healthcare systems have gained significant attention. On the other hand, as IoT networks are large-scaled and battery-powered, developing proper energy and resource management mechanisms for them is inevitable. On account of the large amount of data generated in IoT environments, data aggregation is vital to lower energy consumption and extend network lifespan, and many researchers have endeavored to enhance its efficiency. However, there is no optimized method for the dynamic, complex, and nonlinear nature of healthcare applications. Fuzzy logic could be effective in these scenarios because it can convert qualitative data to quantitative, implement complex nonlinear functions, and present approximate solutions for cases where there is no single optimal answer, and it changes with a slight variation in conditions. This paper proposes the F-LEACH, a Fuzzy-based data aggregation scheme for IoT-enabled healthcare applications aiming to maximize the network lifetime. According to the simulation results, the proposed method outperformed similar works by 5–20%.

Vorheriger Artikel Complex and flexible data access policy in attribute-based encryption

Nächster Artikel A new approach for physical human activity recognition based on co-occurrence matrices

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Baranwal G, Singh M, Vidyarthi DP (2019) A framework for IoT service selection. J Supercomput 76:1–38

Yue H, Jiang Q, Yin C, Wilson J (2020) Research on data aggregation and transmission planning with the Internet of Things technology in WSN multi-channel aware network. J Supercomput 76(5):3298–3307CrossRef

Khan MA, Salah K (2018) IoT security: review, blockchain solutions, and open challenges. Futur Gener Comput Syst 82:395–411CrossRef

Shafqat S, Kishwer S, Rasool RU, Qadir J, Amjad T, Ahmad HF (2020) Big data analytics enhanced healthcare systems: a review. J Supercomput 76(3):1754–1799CrossRef

Farahani B, Firouzi F, Chakrabarty K (2020) Healthcare IoT. In: Intelligent Internet of Things. Springer, pp 515–545

Luo X, Zhang D, Yang LT, Liu J, Chang X, Ning H (2016) A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems. Futur Gener Comput Syst 61:85–96CrossRef

Sohn I, Lee J-H, Lee SH (2016) Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks. IEEE Commun Lett 20(3):558–561CrossRef

Maadani M (2019) Reanalyzing a simplified Markov model for the low-density P2P wireless sensor and actuator networks. Telecommun Syst 70(2):159–169CrossRef

Maadani M, Motamedi SA (2016) A comprehensive DCF performance analysis in noisy industrial wireless networks. Int J Commun Syst 29(11):1720–1739CrossRef

10.

Baseri M, Motamedi SA, Maadani MA (2014) Load-adaptive beacon scheduling algorithm for IEEE 802.15. 4 mesh topology improving throughput and QoS in WMSNs. In: Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, pp 1–5

11.

Maadani M, Motamedi SA (2014) A simple and comprehensive saturation packet delay model for wireless industrial networks. Wirel Pers Commun 77(1):365–381CrossRef

12.

Maadani M, Motamedi SA (2014) A simple and closed-form access delay model for reliable IEEE 802.11-based wireless industrial networks. Wirel Pers Commun 75(4):2243–2268CrossRef

13.

Maadani M, Motamedi SA, Safdarkhani H (2011) Delay-reliability trade-off in MIMO-enabled IEEE 802.11-based wireless sensor and actuator networks. Procedia Comput Sci 5:945–950CrossRef

14.

Alimorad NM, Mohsen, Mahdavi, Mojdeh (2021) REO: a reliable and energy efficient optimization algorithm for beacon-enabled 802.15.4-based wireless body area networks. IEEE Sens J 1–8

15.

Nasrollahzadeh S, Maadani M, Pourmina MA (2021) Optimal motion sensor placement in smart homes and intelligent environments using a hybrid WOA-PSO algorithm. J Reliab Intell Environ 1–20

16.

Shad MN, Maadani M, Moghadam MN (2021) GAPSO-SVM: an IDSS-based Energy-Aware Clustering Routing Algorithm for IoT perception layer. Wirel Pers Commun 1–19

17.

Nabati M, Maadani M, Pourmina MA (2021) AGEN-AODV: an intelligent energy-aware routing protocol for heterogeneous mobile ad-hoc networks. Mob Netw Appl 1–15

18.

Pourghebleh B, Navimipour NJ (2017) Data aggregation mechanisms in the Internet of things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl 97:23–34CrossRef

19.

Rahman H, Ahmed N, Hussain I (2016) Comparison of data aggregation techniques in Internet of Things (IoT). In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, pp 1296–1300

20.

Abid B, Nguyen TT, Seba H (2015) New data aggregation approach for time-constrained wireless sensor networks. J Supercomput 71(5):1678–1693CrossRef

21.

Mehrjou SKF, Dehghanian S (2015) Data aggregating tree based on river dynamic formation algorithm in a wireless sensor network. Soft Process 54(6):54–67

22.

Ullah I, Youn HY (2019) A novel data aggregation scheme based on self-organized map for WSN. J Supercomput 75(7):3975–3996CrossRef

23.

Ullah I, Youn HY (2020) Efficient data aggregation with node clustering and extreme learning machine for WSN. J Supercomput 76:10009–10035CrossRef

24.

Habibi Masouleh H, Marvi M, Jahangir A (2008) An efficient algorithm in wireless sensor networks’ data aggregation using clustering and compression. In: 14th Annual Conference of Iran Computer Association, pp 1–5

25.

Rouhifar M, Rohhifar S, Mohamadian A (2016) A protocol to improve reliability in aggregating and transferring compressed data for wireless sensor networks with energy efficiency. In: National conference on applications of mechatronic and robotic systems, pp 1–13

26.

Rafiei F, Azad M (2016) Wireless sensor networks’ data aggregation based on clustering and compression. In: National Conference on New Approaches in Electrical and Computer Engineering, pp 1–11

27.

Ullah A, Said G, Sher M, Ning H (2020) Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN. Peer-to-Peer Netw Appl 13(1):163–174CrossRef

28.

Pourjavad E, Mayorga RV (2019) A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system. J Intell Manuf 30(3):1085–1097CrossRef

29.

Guillaume S, Charnomordic B (2012) Fuzzy inference systems: an integrated modeling environment for collaboration between expert knowledge and data using FisPro. Expert Syst Appl 39(10):8744–8755CrossRef

30.

Zhang Y, Liu M, Liu Q (2018) An energy-balanced clustering protocol based on an improved CFSFDP algorithm for wireless sensor networks. Sensors 18(3):881CrossRef

31.

Maadani M, Shabro M, Alavikia Z (2019) Analysis of demand-side business opportunities in Iran, as a digital transformation perspective. In: 2019 International Power System Conference (PSC). IEEE, pp 46–51

Titel: F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems
verfasst von: Seyedeh Nafiseh Sajedi
Mohsen Maadani
Meisam Nesari Moghadam
Publikationsdatum: 04.06.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 1/2022
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03890-6

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 1/2022

FlexSched: Efficient scheduling techniques for concurrent kernel execution on GPUs

Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches

Reversible multiplier with a column-wise structure and a reduced number of ancilla inputs and garbage outputs

Novel parallel hybrid genetic algorithms on the GPU for the generalized assignment problem

Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

A novel virtual machine placement algorithm using RF element in cloud infrastructure

Premium Partner