Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning

Date

2019-01-01

Author

Al-Turjman, Fadi
Zahmatkesh, Hadi
Mostarda, Leonardo

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

217
views

0
downloads

In the cloud-based Internet of Things (IoT) environments, quantifying uncertainty is an important element input to keep the acceptable level of reliability in various configurations. In this paper, we aim to address the pricing model of delivering data over the cloud while taking into consideration the dynamic uncertainty factors such as network topology, transmission/reception energy, nodal charge and power, and computation capacity. These uncertainty factors are mapped to different nodes with varying capabilities to be processed using Artificial Intelligence (AI)-based algorithms. Accordingly, we aim to find a way to calculate and predict the price per big data service over the cloud using AI and deep learning. Therefore, in this paper, we propose a framework to address big data delivery issues in cloud-based IoT environments by considering uncertainty factors. We compare the performance of the framework using two AI-based techniques called Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA) in both centralized and distributed versions. The use of AI techniques can be applied in multilevel to provide a kind of deep learning to further improve the performance of the system under study. The results reveal that the distributed algorithm outperforms the centralized one. In addition, the results show that the GA has lower running time compared to the SAA in all the test cases such as 68% of improvement in the centralized version, and 66% of improvement in the distributed version in case when the size of uncertainty array is 256. Moreover, when the size of uncertainty array increases, the results show 60% speed up in the distributed GA compared to its centralized version. The improvements achieved would help the service providers to actually improve their profit using the proposed framework.

Subject Keywords

General Engineering, General Materials Science, General Computer Science

URI

https://hdl.handle.net/11511/66954

Journal

IEEE ACCESS

DOI

https://doi.org/10.1109/access.2019.2931637

Collections

Engineering, Article

Suggestions

OpenMETU
Core

Lithium-intercalation oxides for rechargeable batteries Ceder, Gerbrand; VanderVen, Anton; Aydınol, Mehmet Kadri (Springer Science and Business Media LLC, 1998-09-01) Since the introduction of the LixC/LiCoO2 cell, rechargeable lithium batteries have become the technology of choice for applications where volume or weight are a consideration (e.g., laptop computers and cell phones). The focus of current research in cathode-active materials is on less-expensive or higher-performance materials than LiCoO2. This article illustrates how first-principles calculations can play a critical role in obtaining the understanding needed to design improved cathode oxides.
On-demand conversation customization for services in large smart environments Elgedawy, I. (IBM, 2011-01-01) Services in large smart environments, as defined in this paper, are "aware" of their users' contexts and goals and are able to automatically interact with one another in order to achieve these goals. Unfortunately, interactions between services (i.e., service conversations) are not necessarily compatible, as services could have different interfaces (i.e., signature incompatibilities), as well as different logic for message ordering (i.e., protocol incompatibilities). Such conversation incompatibilities crea...
Numerical study on effects of computational domain length on flow field in standing wave thermoacoustic couple MERGEN, SÜHAN; Yıldırım, Ender; TÜRKOĞLU, HAŞMET (Elsevier BV, 2019-03-01) For the analysis of thermoacoustic (TA) devices, computational methods are commonly used. In the computational studies found in the literature, the flow domain has been modelled differently by different researchers. A common approach in modelling the flow domain is to truncate the computational domain around the stack, instead of modelling the whole resonator to save computational time. However, where to truncate the domain is not clear. In this study, we have investigated how the simulation results are aff...
On the performance, availability and energy consumption modelling of clustered IoT systems Ever, Enver; Shah, Purav; Mostarda, Leonardo; Omondi, Fredrick; Gemikonakli, Orhan (Springer Science and Business Media LLC, 2019-12-01) Wireless sensor networks (WSNs) form a large part of the ecosystem of the Internet of Things (IoT), hence they have numerous application domains with varying performance and availability requirements. Limited resources that include processing capability, queue capacity, and available energy in addition to frequent node and link failures degrade the performance and availability of these networks. In an attempt to efficiently utilise the limited resources and to maintain the reliable network with efficient da...
Operation assignment and capacity allocation problem in automated manufacturing systems Bilgin, Selin; Azizoğlu, Meral (Elsevier BV, 2009-03-01) We address an operation assignment and capacity allocation problem that arises in semiconductor industries and flexible manufacturing systems. We assume the automated machines have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. We show that the problem is NP-hard in the strong sense, develop two heuristics and a Tabu Search procedure. The results of our computational tests have revealed that o...

Citation Formats

F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning,” IEEE ACCESS, pp. 115749–115759, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66954.