Open Access Open Access  Restricted Access Subscription Access

Analysis on Internet of Things Weather Fog

Aapi Aeron

Abstract


Network analysis and optimization are arising exploration that calls for the number of realities like increased vacuity, coffers operation, performance analysis, sequestration- acquainted evaluation among others. The dynamic advancements in technological edges increase vacuity, connectivity of the bias that provides and controls the network resource operation, reduced quiescence and bandwidth kinds that contribute to the smart invention including smart megacity, smart homes, among others. In this paper, we dissect the performance of mobile edge computing of the Internet of effects (IoT) that determines the service quality of the network as seen in other calculating arenas. Further still, we propose a performance model called Erlang Performance Model (EPM) Erlang formulas grounding different hypotheticals during data transmission. The composition provides a deep understanding of queuing propositions concerning the discipline of First-In-First-Out (FIFO) in mobile edge networks through serving fine computations of virtual parameters. The paper proved that Erlang formulas could be alternately used in designing performance models as we illustrated. The simulated MATLAB results showed that assaying the performance creates reduces traffic/ business during data transmission and better determination of grade and quality of service to the druggies and smart communication providers.

 


Full Text:

PDF

References


Z. Ning, X. Kong, F. Xia, W. Hou, and X. Wang, "Green and a sustainable cloud of things: Enabling collaborative edge computing," IEEE Communications Magazine.2019.57(1):72–78p.

T. X. Tran and D. Pompili, “Joint task offloading and resource allocation for multi-server mobile-edge computing networks,” IEEE Transactions on Vehicular Technology.2019.68(1): 856–868p.

C. Li, J. Bai, and J. Tang, “Joint optimization of data placement and scheduling for improving user experience in edge computing,” Journal of Parallel and Distributed Computing.2019.125:93–105p.

A. Brogi, S. Forti, and A. Ibrahim, “Predictive analysis to support fog application deployment,” Fog and Edge Computing: Principles and Paradigms.2019.191–221p.

S. Li, D. Zhai, P. Du, and T. Han, “Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks,” Science China Information Sciences.2019.62(2):29307p.

T. T. Nguyen, L. Le, and Q. Le-Trung, “Computation offloading in MIMO based mobile edge computing systems under perfect and imperfect CSI estimation,” IEEE Transactions on Services Computing, 2019.

J. Ren, Y. He, G. Huang, G. Yu, Y. Cai, and Z. Zhang, “An Edge-Computing Based Architecture for Mobile Augmented Reality,” IEEE Network, 2019.

S. Ammirato, F. Sofo, A. M. Felicetti, and C. Raso, “A methodology to support the adoption of IoT innovation and its application to the Italian bank branch security context,” European Journal of Innovation Management.2019.22(1):146–174p.

B. Afzal, M. Umair, G. A. Shah, and E. Ahmed, “Enabling IoT platforms for social IoT applications: vision, feature mapping, and challenges,” Future Generation Computer Systems,2019.92:718–731p.

V. V. Sood, S. Sharma, and R. Khanna, “Performance Evaluation of Cognitive Internet of Things in Asynchronous Distributed Space-Time Block Codes over Two-Wave Diffuse Power Fading Channel,” in Engineering Vibration, Communication and Information Processing, Springer, 2019,. 131–141p.


Refbacks

  • There are currently no refbacks.