Mobility as a Service (MaaS): A D2D-Based Information Centric Network Architecture for Edge-Controlled Content Distribution

Chandrasekaran, Ganesh; Wang, Ning; Hassanpour, Masoud; Xu, Mingwei; Tafazolli, Rahim

doi:10.1109/access.2017.2781736

Cited by 25 publications

(14 citation statements)

References 35 publications

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“…These carrier multiple bands can be continuous (adjacent) or non-continuous. The non-continuous components are divided into inter-band and intra-band components (15) . It has 4x4 MIMO in uplink and 8x8 MIMO in downlink.…”

Section: Network Model and Mathematical Preliminariesmentioning

confidence: 99%

System capacity evaluation for mobility management in LTE-advanced D2D networks

lowast¹,

Siddesh²

2020

IJST

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Objectives: This study aims to evaluate the system capacity for mobility management in Long Term Evolution-Advanced (LTE-A) Device to Device (D2D) networks with suitable handover schemes. Methods: This paper introduces power control mechanisms for 5G based on the distance between D2D terminals. Interference is a major issue which reduces the performance of D2D communication. The proposed system introduces an algorithm based on greedy algorithmic approach for interference mitigation with easy handover for system capacity estimation. The implementation is carried out using LabVIEW platform and results are analyzed. Findings: The existing mobile communication approaches transfer calls through base stations irrespective of the distance between the mobile terminals. User mobility is an important factor for D2D communication and proper selection of mobility patterns that helps in increasing the energy efficiency and throughput by reducing interference. Efficient utilization of these mobility patterns are shown in the proposed method for developing an optimal time efficient mobility aware caching mechanism with lower complexity. In the recent works, less importance has been given for users' contact time duration while they are moving with different speeds. The simulation results show an enhancement in the system capacity of approximately 480 bits/Hz for 50 devices and 290 bits/Hz for 20 D2D users. The simulation results prove that the proposed approach gives better performance than the existing methods.

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Section: Network Model and Mathematical Preliminariesmentioning

confidence: 99%

System capacity evaluation for mobility management in LTE-advanced D2D networks

lowast¹,

Siddesh²

2020

IJST

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“…Towards this goal, changes in the location of mobile users need to be modelled first. In order to generate location-based uncertainty values we have taken the following points into considerations i) A number of studies suggested normally distributed locations of mobile users in communication [30] [31], [32], . Thus, based on these results, we have used the Gaussian PDF to generate data for any specific location of the mobile user in a twodimensional grid, (X: longitude and Y: latitude).…”

Section: -Locationmentioning

confidence: 99%

Uncertainty-Aware Authentication Model for Fog Computing in IoT

Heydari

Mylonas

Katos

et al. 2019

2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC)

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Since the term "Fog Computing" has been coined by Cisco Systems in 2012, security and privacy issues of this promising paradigm are still open challenges. Among various security challenges, Access Control is a crucial concern for all cloud computing-like systems (e.g. Fog computing, Mobile edge computing) in the IoT era. Therefore, assigning the precise level of access in such an inherently scalable, heterogeneous and dynamic environment is not easy to perform. This work defines the uncertainty challenge for authentication phase of the access control in fog computing because on one hand fog has a number of characteristics that amplify uncertainty in authentication and on the other hand applying traditional access control models does not result in a flexible and resilient solution. Therefore, we have proposed a novel prediction model based on the extension of Attribute Based Access Control (ABAC) model. Our data-driven model is able to handle uncertainty in authentication. It is also able to consider the mobility of mobile edge devices in order to handle authentication. In doing so, we have built our model using and comparing four supervised classification algorithms namely as Decision Tree, Naïve Bayes, Logistic Regression and Support Vector Machine. Our model can achieve authentication performance with 88.14% accuracy using Logistic Regression.

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“…This was achieved by quantifying the number of cache users, request users, serving time of a content request. In [20], mobile content, content distribution, mobile as a service, user mobility data analytics, content aware mobile network at the edge in 5G network are discussed. The authors proposed a device level information centric networking architecture for intelligent content distribution operations that utilizes context information based on mobility and content features.…”

Section: A Mapping Of Existing Related Workmentioning

confidence: 99%