Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2022

26.08.2021

A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm

verfasst von: Behnaz Mohammadi, Nima Jafari Navimipour

Erschienen in: Wireless Personal Communications | Ausgabe 2/2022

Einloggen

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

search-config
loading …

Abstract

The Peer to Peer-Cloud (P2P-Cloud) is a suitable alternative to distributed cloud-based or peer-to-peer (P2P)-based content on a large scale. The P2P-Cloud is used in many applications such as IPTV, Video-On-Demand, and so on. In the P2P-Cloud network, overload is a common problem during overcrowds. If a node receives many requests simultaneously, the node may not be able to respond quickly to user requests, and this access latency in P2P-Cloud networks is a major problem for their users. The replication method in P2P-Cloud environments reduces the time to access and uses network bandwidth by making multiple data copies in diverse locations. The replication improves access to the information and increases the reliability of the system. The data replication's main problem is identifying the best possible placement of replica data nodes based on user requests for data access time and an NP-hard optimization problem. This paper proposes a new replica replacement to improve average access time and replica cost using fuzzy logic and Ant Colony Optimization algorithm. Ants can find the shortest path to discover the optimal node to place the duplicate file with the least access time latency. The fuzzy module evaluates the historical information of each node to analyze the pheromone value per iteration. The fuzzy membership function is also used to determine each node's degree based on the four characteristics. The simulation results showed that the access time and replica cost are improved compared to other replica replacement algorithms.
Vorheriger Artikel A Meta-Heuristic Based Approach with Modified Mutation Operation For Heterogeneous Networks
Nächster Artikel A Compact Rectangular Dual Patch Antenna for Multiple Satellite Communication Applications

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

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

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
Literatur
1.
Gharehpasha, S., Masdari, M., & Jafarian, A. (2020). Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm. Artificial Intelligence Review, 54, 2221–2257.CrossRef
2.
Goudarzi, P., Hosseinpour, M., & Ahmadi, M. R. (2020). Joint customer/provider evolutionary multi-objective utility maximization in cloud data center networks. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45, 479–492.CrossRef
3.
Naseri, A., & Navimipour, N. J. (2019). A new agent-based method for QoS-aware cloud service composition using particle swarm optimization algorithm. Journal of Ambient Intelligence and Humanized Computing, 10(5), 1851–1864.CrossRef
4.
Zanbouri, K., & Navimipour, N. J. (2020). A cloud service composition method using a trust-based clustering algorithm and honeybee mating optimization algorithm. International Journal of Communication Systems, 33(5), e4259.CrossRef
5.
Javadpour, A., Wang, G., & Rezaei, S. (2020). Resource management in a peer to peer cloud network for IoT. Wireless Personal Communications, 115(3), 2471–2488.CrossRef
6.
Mohammadian, V., Navimipour, N. J., Hosseinzadeh, M., & Darwesh, A. (2020). Comprehensive and systematic study on the fault tolerance architectures in cloud computing. Journal of Circuits, Systems Computers, 29(15), 2050240.CrossRef
7.
Aggarwal, A., Dimri, P., Agarwal, A., & Bhatt, A. (2020). Self adaptive fruit fly algorithm for multiple workflow scheduling in cloud computing environment. Kybernetes, 50, 1704–1730.CrossRef
8.
Sun, S., Yao, W., Qiao, B., Zong, M., He, X., & Li, X. (2019). RRSD: A file replication method for ensuring data reliability and reducing storage consumption in a dynamic Cloud-P2P environment. Future Generation Computer Systems, 100, 844–858.CrossRef
9.
Chakareski, J. (2015). Cost and profit driven cloud-P2P interaction. Peer-to-Peer Networking and Applications, 8(2), 244–259.CrossRef
10.
Sun, S., Yao, W., & Li, X. (2018). SORD: A new strategy of online replica deduplication in Cloud-P2P. Cluster Computing, 22, 1–23.CrossRef
11.
Li, J., Wu, J., & Chen, L. (2018). Block-secure: Blockchain based scheme for secure P2P cloud storage. Information Sciences, 465, 219–231.CrossRef
12.
Garmehi, M., Analoui, M., Pathan, M., & Buyya, R. (2014). An economic replica placement mechanism for streaming content distribution in Hybrid CDN-P2P networks. Computer Communications, 52, 60–70.CrossRef
13.
Nukarapu, D., Tang, B., Wang, L., & Lu, S. (2011). Data replication in data intensive scientific applications with performance guarantee. IEEE Transactions on Parallel and Distributed Systems, 22(8), 1299–1306.CrossRef
14.
Hassanzadeh-Nazarabadi, Y., Küpçü, A., & Ozkasap, O. (2019). Decentralized utility-and locality-aware replication for heterogeneous DHT-based P2P cloud storage systems. IEEE Transactions on Parallel and Distributed Systems, 31(5), 1183–1193.CrossRef
15.
Mokadem, R., & Hameurlain, A. (2020). A data replication strategy with tenant performance and provider economic profit guarantees in Cloud data centers. Journal of Systems and Software, 159, 110447.CrossRef
16.
Ali-Eldin, A., & El-Ansary, S. (2011). Replica placement in peer-assisted clouds: An economic approach. In IFIP international conference on distributed applications and interoperable systems (pp. 208–213). Springer.
17.
Gill, N. K., & Singh, S. (2016). A dynamic, cost-aware, optimized data replication strategy for heterogeneous cloud data centers. Future Generation Computer Systems, 65, 10–32.CrossRef
18.
Hamrouni, T., Slimani, S., & Charrada, F. B. (2016). A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids. Engineering Applications of Artificial Intelligence, 48, 140–158.CrossRef
19.
Guerrero, C., Lera, I., & Juiz, C. (2018). Migration-aware genetic optimization for mapreduce scheduling and replica placement in hadoop. Journal of Grid Computing, 16(2), 265–284.CrossRef
20.
Ebadi, Y., & Navimipour, N. J. (2019). An energy-aware method for data replication in the cloud environments using a Tabu search and particle swarm optimization algorithm. Concurrency and Computation: Practice and Experience, 31(1), e4757.CrossRef
21.
Moufida, R., & Benchaïba, M. (2017). An efficient replication scheme to increase file availability in mobile peer to peer systems. In 2017 international symposium on networks, computers and communications (ISNCC) (pp. 1–6). IEEE.
22.
Muqaddas, A. S., Sviridov, G., Giaccone, P., & Bianco, A. (2020). Optimal state replication in stateful data planes. IEEE Journal on Selected Areas in Communications, 38, 1388–1400.CrossRef
23.
Mansouri, N., & Javidi, M. M. (2020). A review of data replication based on meta-heuristics approach in cloud computing and data grid. Soft Computing, 24, 14503–14530.CrossRef
24.
Navimipour, N. J., & Milani, B. A. (2016). Replica selection in the cloud environments using an ant colony algorithm. In 2016 third international conference on digital information processing, data mining, and wireless communications (DIPDMWC) (pp. 105–110). IEEE.
25.
Wang, L., Luo, J., Shen, J., & Dong, F. (2013). Cost and time aware ant colony algorithm for data replica in alpha magnetic spectrometer experiment. In 2013 IEEE international congress on big data (pp. 247–254). IEEE.
26.
Muñoz, V. M., Vicente, G. A., Carballeira, F. G., & Cairols, J. S. (2010). Emergent algorithms for replica location and selection in data grid. Future Generation Computer Systems, 26(7), 934–946.CrossRef
27.
Ma, T., Yan, Q., Tian, W., Guan, D., & Lee, S. (2013). Replica creation strategy based on quantum evolutionary algorithm in data gird. Knowledge-Based Systems, 42, 85–96.CrossRef
28.
Huang, T., Lin, W., Li, Y., He, L., & Peng, S. (2019). A latency-aware multiple data replicas placement strategy for fog computing. Journal of Signal Processing Systems, 91(10), 1191–1204.CrossRef
29.
Wang, Y., Zhao, Y., & Hou, F. (2008). Ant colony optimization algorithm based P2P system replica optimal location strategy. In 2008 IEEE international conference on service operations and logistics, and informatics (Vol. 1, pp. 494–497). IEEE.
30.
Grace, R. K., & Manimegalai, R. (2014). Dynamic replica placement and selection strategies in data grids—A comprehensive survey. Journal of Parallel and Distributed Computing, 74(2), 2099–2108.CrossRef
31.
Wakil, K., Nazif, H., Panahi, S., Abnoosian, K., & Sheikhi, S. (2019). Method for replica selection in the Internet of Things using a hybrid optimisation algorithm. IET Communications, 13(17), 2820–2826.CrossRef
32.
Paul, V., & Vengattaraman, T. (2010). Ant colony optimization for replica management in distributed spanning tree modeled peer network. International Journal of Advanced Research in Computer Science, 1(2), 161–164.
33.
Long, S.-Q., Zhao, Y.-L., & Chen, W. (2014). MORM: A Multi-objective Optimized Replication Management strategy for cloud storage cluster. Journal of Systems Architecture, 60(2), 234–244.CrossRef
34.
Dai, W., Ibrahim, I., & Bassiouni, M. (2016). A new replica placement policy for hadoop distributed file system (pp. 262–267).
35.
Saranya, N., Geetha, K., & Rajan, C. (2020). Data replication in mobile edge computing systems to reduce latency in Internet of Things. Wireless Personal Communications, 112, 2643–2662.CrossRef
36.
Sun, S., Wang X., & Zuo, F. (2020). RPCC: A replica placement method to alleviate the replica consistency under dynamic cloud. In 2020 international conferences on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData) and IEEE congress on cybermatics (Cybermatics) (pp. 729–734). IEEE.
37.
Li, Y., Tian, M., Wang, Y., Zhang, Q., Saxena, D. K., & Jiao, L. (2020). A new replica placement strategy based on multi-objective optimisation for HDFS. International Journal of Bio-Inspired Computation, 16(1), 13–22.CrossRef
38.
Wei, Q., Veeravalli, B., Gong, B., Zeng, L., & Feng, D. (2010). CDRM: A cost-effective dynamic replication management scheme for cloud storage cluster (pp. 188–196).
39.
Bai, H. J. X., Liao, X., Shi, X., & Shao, Z. (2013). RTRM: A response time-based replica management strategy for cloud storage system.
40.
Rajalakshmi, A., Vijayakumar, D., & Srinivasagan, K. (2014). An improved dynamic data replica selection and placement in cloud. In 2014 international conference on recent trends in information technology (pp. 1–6). IEEE.
41.
Vijayakumar, D., Srinivasagan, K., & Sabarimuthukumar, R. (2015). FIR3: A fuzzy inference based reliable replica replacement strategy for cloud Data Centre. In 2015 international conference on computing and network communications (CoCoNet) (pp. 473–479). IEEE.
42.
Tsai, J., Liu, J.-S., & Chang, T.-Y. (2017). Optimality of a simple replica placement strategy for chord peer-to-peer networks. IEICE Transactions on Communications, E100.B(4), 557–565.CrossRef
43.
Sun, S., Yao, W., & Li, X. (2018). DARS: A dynamic adaptive replica strategy under high load Cloud-P2P. Future Generation Computer Systems, 78, 31–40.CrossRef
44.
Hassanzadeh-Nazarabadi, Y., Küpçü, A., & Özkasap, Ö. (2016). Awake: Decentralized and availability aware replication for p2p cloud storage. In 2016 IEEE international conference on smart cloud (SmartCloud) (pp. 289–294). IEEE.
45.
Xu, K., Song, M., Zhang, X., & Song, J. (2009). A cloud computing platform based on p2p. In 2009 IEEE international symposium on IT in medicine & education (Vol. 1, pp. 427–432). IEEE.
46.
Milani, B. A., & Navimipour, N. J. (2016). A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions. Journal of Network and Computer Applications, 64, 229–238.CrossRef
47.
Li, C., Wang, Y., Tang, H., Zhang, Y., Xin, Y., & Luo, Y. (2019). Flexible replica placement for enhancing the availability in edge computing environment. Computer Communications, 146, 1–14.CrossRef
48.
Mohammadi, B., & Navimipour, N. J. (2019). Data replication mechanisms in the peer-to-peer networks. International Journal of Communication Systems, 32(14), e3996.CrossRef
49.
Zhang, B., Wang, X., & Huang, M. (2014). Intelligent multiple data replica placement scheme for cloud storage. Journal of Frontiers of Computer Science & Technology, 10, 3.
50.
Xue, M. (2015). Replica placement in cloud storage based on minimal blocking probability. In The 5th international conference on computer engineering and networks (Vol. 259, p. 048). SISSA Medialab.
51.
Spaho, E., Barolli, L., & Xhafa, F. (2014). Data replication strategies in P2P systems: A survey. In 2014 17th international conference on network-based information systems (pp. 302–309). IEEE.
52.
Mansouri, N., & Javidi, M. M. (2019). A review of replica replacement techniques in grid computing and cloud computing. Journal of Algorithms and Computation, 51(2), 134–151.
53.
Milani, B. A., & Navimipour, N. J. (2017). A systematic literature review of the data replication techniques in the cloud environments. Big Data Research, 10, 1–7.CrossRef
54.
Zhao, P., Sun, X., Shang, J., Lin, J., Dong, M., & Li, B. (2019). A dynamic convergent replica selection strategy based on cloud storage. In 2019 international conference on artificial intelligence and advanced manufacturing (AIAM) (pp. 473–478). IEEE.
55.
Xiuguo, W. (2018). A security-aware data replica placement strategy based on fuzzy evaluation in the cloud. Journal of Intelligent & Fuzzy Systems, 35(1), 243–255.CrossRef
56.
Bagheri, M., Mukhatov, A., Abedinia, O., Naderi, M. S., Naderi, M. S., & Ghadimi, N. (2018). Application and design of new controller based on fuzzy PID and FACTS devices in multi-machine power system. In 2018 IEEE international conference on environment and electrical engineering and 2018 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe) (pp. 1–6). IEEE.
57.
Cao, Y., et al. (2019). Optimal operation of CCHP and renewable generation-based energy hub considering environmental perspective: An epsilon constraint and fuzzy methods. Sustainable Energy, Grids and Networks, 20, 100274.CrossRef
58.
Toukir Imam, R. M. R. (2011). Implementation and performance analysis of fuzzy replica replacement algorithm in data grid.
59.
Khodaei, H., Hajiali, M., Darvishan, A., Sepehr, M., & Ghadimi, N. (2018). Fuzzy-based heat and power hub models for cost-emission operation of an industrial consumer using compromise programming. Applied Thermal Engineering, 137, 395–405.CrossRef
60.
Angela JennifaSujana, J., Revathi, T., & Joshua Rajanayagam, S. (2020). Fuzzy-based security-driven optimistic scheduling of scientific workflows in cloud computing. IETE Journal of Research, 66(2), 224–241.CrossRef
61.
Ghilavizadeh, Z., Mirabedini, S., & Harounabadi, A. (2013). A new fuzzy optimal data replication method for data grid. Management Science Letters, 3(3), 927–936.CrossRef
62.
Wang, Y., & Chen, Y. (2014). A comparison of Mamdani and Sugeno fuzzy inference systems for traffic flow prediction. Journal of Computers, 9(1), 12–21.CrossRef
63.
Mansouri, N., & Javidi, M. M. (2018). A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers. The Journal of Supercomputing, 74(10), 5349–5372.CrossRef
64.
Nivetha, N., & Vijayakumar, D. (2016). Modeling fuzzy based replication strategy to improve data availabiity in cloud datacenter. In 2016 international conference on computing technologies and intelligent data engineering (ICCTIDE'16) (pp. 1–6). IEEE.
65.
Mansouri, N., Zade, B. M. H., & Javidi, M. M. (2020). A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing. Journal of Network and Computer Applications, 171, 102811.CrossRef
66.
Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470–1477). IEEE.
67.
Krynicki, K., Jaen, J., & Mocholí, J. A. (2014). Ant colony optimisation for resource searching in dynamic peer-to-peer grids. International Journal of Bio-Inspired Computation, 6(3), 153–165.CrossRef
68.
Shojaatmand, A., Saghiri, N., Hashemi, S., Dezfoli, M. A., & Khouzestan, I. (2011). Improving replica selection in data grid using a dynamic ant algorithm. International Journal of Information, 3(4), 139.
69.
Azad, P., Navimipour, N. J., & Hosseinzadeh, M. (2019). A fuzzy-based method for task scheduling in the cloud environments using inverted ant colony optimisation algorithm. International Journal of Bio-Inspired Computation, 14(2), 125–137.CrossRef
70.
Azad, P., & Navimipour, N. J. (2017). An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. International Journal of Cloud Applications and Computing (IJCAC), 7(4), 20–40.CrossRef
71.
Huang, T., Lin, W., Xiong, C., Pan, R., & Huang, J. (2020). An ant colony optimization-based multiobjective service replicas placement strategy for fog computing. IEEE Transactions on Cybernetics, 1, 1–14.
72.
Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2019). An improved hybrid fuzzy-ant colony algorithm applied to load balancing in cloud computing environment. Procedia Computer Science, 151, 519–526.CrossRef
73.
Neyoy, H., Castillo, O., & Soria, J. (2013). Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. Recent advances on hybrid intelligent systems (pp. 259–271). Springer.
74.
Duan, X.-C., Li, Y.-N., Jia, H.-L., Zhao, Z.-G., & Li, C. (2017). Research on ACO algorithm initial pheromone screening in HDFS copy selection. Transducer and Microsystem Technologies, 4, 9.
75.
Asghari, S., & Navimipour, N. J. (2019). Cloud service composition using an inverted ant colony optimisation algorithm. International Journal of Bio-Inspired Computation, 13(4), 257–268.CrossRef
76.
Tawfeek, M. A., El-Sisi, A., Keshk, A. E., & Torkey, F. A. (2013). Cloud task scheduling based on ant colony optimization. In 2013 8th international conference on computer engineering & systems (ICCES) (pp. 64–69). IEEE.
77.
Jabbarpour, M. R., Malakooti, H., Noor, R. M., Anuar, N. B., & Khamis, N. (2014). Ant colony optimisation for vehicle traffic systems: Applications and challenges. International Journal of Bio-Inspired Computation, 6(1), 32–56.CrossRef
78.
Heidari, A., & Navimipour, J. N. (2021). A new SLA-aware method for discovering the cloud services using an improved nature-inspired optimization algorithm. PeerJ Computer Science, 7, e539.CrossRef
79.
Asghari, S., & Navimipour, N. J. (2019). Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm. Peer-to-Peer Networking and Applications, 12(1), 129–142.CrossRef
80.
Abedinia, O., Amjady, N., & Ghadimi, N. J. C. I. (2017). Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Computational Intelligence, 34, 241–260.MathSciNetCrossRef
81.
Santiago, A., Dorronsoro, B., Nebro, A. J., Durillo, J. J., Castillo, O., & Fraire, H. J. (2019). A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME. Information Sciences, 471, 233–251.MathSciNetMATHCrossRef
82.
Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2019). FACO: A hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing. Journal of Ambient Intelligence and Humanized Computing, 11, 3975–3987.CrossRef
83.
Zhao, W., Xu, X., Xiong, N., & Wang, Z. (2008). A weight-based dynamic replica replacement strategy in data grids. In 2008 IEEE Asia-Pacific services computing conference (pp. 1544–1549). IEEE.
84.
Teng, M., & Junzhou, L. (2005). A prediction-based and cost-based replica replacement algorithm research and simulation. In 19th international conference on advanced information networking and applications (AINA'05) volume 1 (AINA papers) (Vol. 1, pp. 935–940). IEEE.
85.
Sashi, K., & Thanamani, A. S. (2011). Dynamic replication in a data grid using a modified BHR region based algorithm. Future Generation Computer Systems, 27(2), 202–210.CrossRef
Metadaten
Titel
A Fuzzy Logic-Based Method for Replica Placement in the Peer to Peer Cloud Using an Optimization Algorithm
verfasst von
Behnaz Mohammadi
Nima Jafari Navimipour
Publikationsdatum
26.08.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-08936-9

Weitere Artikel der Ausgabe 2/2022

Wireless Personal Communications 2/2022 Zur Ausgabe

OriginalPaper

Steganography in Color Animated Image Sequence for Secret Data Sharing Using Secure Hash Algorithm

OriginalPaper

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments

OriginalPaper

An Evaluation of UAV Path Following and Collision Avoidance Using NFMGOA Control Algorithm

OriginalPaper

Smart City Healthcare Cyber Physical System: Characteristics, Technologies  and Challenges

ReviewPaper

How Blockchain Can Automate KYC: Systematic Review

OriginalPaper

Deep Learning based Early Prediction Scheme for Breast Cancer

Neuer Inhalt

    Bildnachweise