Skip to main content
SpringerLink
Log in

Scalable aggregation plan (SAP) for portable network in a box (NIB) architecture

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Network in a Box (NIB) assimilates hardware and software composition in a portable form where the network functions are expanded in an ad-hoc manner. The explicit environmental features do not provide sufficient information instantly. Therefore, to cope up with the explicit environmental conditions and to maximize the data aggregation process, scalable aggregation plan (SAP) for NIB is introduced in this article. SAP focuses in maximizing data accumulation from the available resources through recurrent linear and discrete analysis. The liveliness of the information and its availability are the prime constraints that determine the efficiency of SAP. For this purpose, selection of precise aggregation is mandatory irrespective of the levels in aggregation and multi-hop data transmission. The reliability of the aggregation from the available resources is assessed using time-dependent slot and interval allocation. The performance of the proposed SAP is validated using appropriate experiments, and the performance metrics such as aggregation delay, backlog error, transmission time, and aggregation rate are used for evaluation.

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

Similar content being viewed by others

References

  1. Pozza M, Rao A, Flinck H, Tarkoma S (2018) Network-in-a-box: A survey about on-demand flexible networks. IEEE Communications Surveys & Tutorials 20(3):2407–2428

    Article  Google Scholar 

  2. Abedifar V, Furdek M, Muhammad A, Eshghi M, Wosinska L (2018) Routing, Modulation, and Spectrum Assignment in Programmable Networks Based on Optical White Boxes. Journal of Optical Communications and Networking 10(9):723

    Article  Google Scholar 

  3. Kivits E, Hof PMVD (2018) On representations of linear dynamic networks. IFAC-PapersOnLine 51(15):838–843

    Article  Google Scholar 

  4. Copeland R, Copeland M, Ahvar S, Crespi N, Shagdar O, Durand R (2019) Automotive virtual edge communicator (AVEC) with vehicular inter-agent service orchestration and resourcing (ViSOR). Ann Telecommun 74(9–10):655–662

    Article  Google Scholar 

  5. Miyamura T, Misawa A, Kani J-I (2019) Resource optimization of optical aggregation network for efficient software-defined datacenters. Opt Switch Netw 32:41–50

    Article  Google Scholar 

  6. Wang G, Zhao Y, Ying Y, Huang J, Winter RM (2018) Data aggregation point placement problem in neighborhood area networks of smart grid. Mobile Networks and Applications 23(4):696–708

    Article  Google Scholar 

  7. Yin X, Li S, Lin Y (Dec. 2019) A novel hierarchical data aggregation with particle swarm optimization for internet of things. Mobile Networks and Applications 24(6):1994–2001

    Article  Google Scholar 

  8. Minh QT, Le VA, Dang TK, Nam T and Kitahara T (2019) "Flow aggregation for SDN-based delay-insensitive traffic control in mobile core networks," in IET Communications, vol. 13, no. 8, pp. 1051–1060, 14 5 2019

  9. Han D, Minn H, Tefek U, Lim TJ (Jan. 2019) Network dimensioning, QoE maximization, and power control for multi-tier machine-type communications. in IEEE Transactions on Communications 67(1):859–872

  10. Wan R, Xiong N, Hu Q, Wang H, and Shang J (2019) “Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking, vol. 2019, no. 1

  11. Cao J, Zhang X, Zhang C, Feng J (Aug. 2018) Improved convolutional neural network combined with rough set theory for data aggregation algorithm. J Ambient Intell Humaniz Comput 11(2):647–654

    Article  Google Scholar 

  12. Mosavvar I, Ghaffari A (2018) Data aggregation in wireless sensor networks using firefly algorithm. Wirel Pers Commun 104(1):307–324

    Article  Google Scholar 

  13. Zeng P, Pan B, Choo K-KR, Liu H (2020) MMDA: multidimensional and multidirectional data aggregation for edge computing-enhanced IoT. J Syst Archit 106:101713

    Article  Google Scholar 

  14. Breunig DA, Schneider M (2019) Multi-protocol data aggregation and Acquisition for Distributed Control Systems. Procedia CIRP 81:310–315

    Article  Google Scholar 

  15. Yin B, Wei X (2019) Communication-efficient data aggregation tree construction for complex queries in IoT applications. IEEE Internet Things J 6(2):3352–3363

    Article  MathSciNet  Google Scholar 

  16. Chen Y, Martinez-Ortega J-F, Castillejo P, Lopez L (2019) A Homomorphic-based multiple data aggregation scheme for smart grid. IEEE Sensors J 19(10):3921–3929

    Article  Google Scholar 

  17. Li J, Siddula M, Cheng X, Cheng W, Tian Z, Li Y (2020) Approximate data aggregation in sensor equipped IoT networks. Tsinghua Sci Technol 25(1):44–55

    Article  Google Scholar 

  18. Jia B, Hao L, Zhang C, Zhao H, Khan M (Dec. 2018) An IoT service aggregation method based on dynamic planning for QoE restraints. Mobile Networks and Applications 24(1):25–33

    Article  Google Scholar 

  19. López OLA, Alves H, Nardelli PHJ, Latva-Aho M (2019) Hybrid resource scheduling for aggregation in massive machine-type communication networks. Ad Hoc Netw 94:101932

    Article  Google Scholar 

  20. Gurusamy U, Hariharan K, and Manikandan MSK (2020) “Path optimization of box-covering based routing to minimize average packet delay in software defined network,” Peer-to-Peer Networking and Applications

  21. Shuai L, Wang L, Miao L, Zhou X (2019) S-boxes construction based on the Cayley graph of the symmetric group for UASNs. IEEE Access 7:38826–38832

    Article  Google Scholar 

  22. Amoon M, Altameem T, Altameem A (2020) RRAC: role based reputed access control method for mitigating malicious impact in intelligent IoT platforms. Comput Commun 151:238–246

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding this research group No. (RGP – 1436-035).

Author information

Authors and Affiliations

  1. Computer Science, CC, King Saud University, Riyadh, 11437, Saudi Arabia

    Torki Altameem

  2. College of Applied Studies, King Saud University, Riyadh, Saudi Arabia

    Ayman Altameem

Authors
  1. Torki Altameem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayman Altameem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Torki Altameem.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications

Guest Editor: Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Altameem, T., Altameem, A. Scalable aggregation plan (SAP) for portable network in a box (NIB) architecture. Peer-to-Peer Netw. Appl. 14, 2295–2305 (2021). https://doi.org/10.1007/s12083-020-00920-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00920-7

Keywords

Search

Navigation