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1 |
Multimodal biometric authentication for mobile edge computing
Multimodal biometric authentication for mobile edge computing-2021 In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) sys- tem designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing.© 2021 Elsevier Inc. All rights reserved. Keywords: Privacy Preserving Biometric Authentication (PPBA) | Mobile Edge Computing (MEC) | Multimodal Biometrics | Hill Climbing Attacks (HCA) | Euclidean distance | Malicious security |
مقاله انگلیسی |
2 |
Cooperative control strategy for plug-in hybrid electric vehicles based on a hierarchical framework with fast calculation
استراتژی کنترل تعاونی برای وسایل نقلیه برقی هیبریدی پلاگین بر اساس یک چارچوب سلسله مراتبی با محاسبه سریع-2020 Developing optimal control strategies with capability of real-time implementation for plug-in hybrid
electric vehicles (PHEVs) has drawn explosive attention. In this study, a novel hierarchical control
framework is proposed for PHEVs to achieve the instantaneous vehicle-environment cooperative control.
The mobile edge computation units (MECUs) and the on-board vehicle control units (VCUs) are included
as the distributed controllers, which enable vehicle-environment cooperative control and reduce the
computation intensity on the vehicle by transferring partial work from VCUs to MECUs. On this basis, a
novel cooperative control strategy is designed to successively achieve the energy management planned
by the iterative dynamic programming (IDP) in MECUs and the energy utilization management achieved
by the model predictive control (MPC) algorithm in the VCU. The performance of raised control strategy
is validated by simulation analysis, highlighting that the cooperative control strategy can achieve superior
performance in real-time application that is close to the global optimization results solved offline. Keywords: Cooperative control strategy | Hierarchical framework | Iterative dynamic programming (IDP) | Model predictive control (MPC) | Plug-in hybrid electric vehicles (PHEVs) |
مقاله انگلیسی |
3 |
به سمت لبه هوشمند: ارتباطات بی سیم به یادگیری ماشین میرسد
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 31 احیای هوش مصنوعی در اواخر (AI) تقریباً در هر شاخهای از علم و فناوری، انقلابی ایجاد کرده است. با توجه به گجتهای تلفن همراه هوشمند و همه جا حاضر و دستگاههای اینترنت اشیا (IoT)، انتظار میرود که اکثر برنامههای هوشمند را بتوان در لبهی شبکههای بی سیم استقرار داد. این روند باعث شده است، تمایل قوی برای تحقق «لبه هوشمند» ایجاد شود تا از برنامههای کاربردی مجهز به AI در دستگاههای لبه مختلف استفاده شود. بر این اساس، یک حوزهی پژوهشی جدید به نام یادگیری لبه به ظهور رسیده است که از دو رشته عبور میکند و انقلابی در آنها ایجاد میکند: ارتباطات بی سیم و یادگیری ماشین. یک موضوع اصلی در یادگیری لبه غلبه بر قدرت محاسباتی محدود و همچنین دادههای محدود در هر دستگاه لبه است. این امر با استفاده از پلت فرم محاسبات لبه تلفن همراه (MEC) و استخراج دادههای عظیم توزیع شده در تعداد زیادی دستگاه لبه محقق شده است. در چنین سیستمهایی، یادگیری از داده توزیع شده و برقراری ارتباط بین سرور لبه و دستگاهها دو جنبهی حیاتی و مهم است و همجوشی آنها، چالشهای پژوهشی جدید و زیادی را به همراه دارد. این مقاله از یک مجموعه جدید از اصول طراحی برای ارتباطات بی سیم در یادگیری لبه پشتیبانی میکند که در مجموع ارتباطات یادگیری محور نامیده میشوند. مثالهای گویایی ارائه شدند تا اثربخشی این اصول طراحی مشخص شوند و برای این منظور فرصتهای تحقیقاتی منحصر به فردی شناسایی شدند.
کلمات کلیدی: سرورها | مدل سازی جوی | هوش مصنوعی | پایگاه های داده توزیع شده | ارتباطات بی سیم | یادگیری ماشین | مدل سازی محاسباتی |
مقاله ترجمه شده |
4 |
Edge Concierge: Democratizing Cost-Effective and Flexible Network Operations using Network Layer AI at Private Network Edges
Edge Concierge: دموکراتیک کردن عملیات شبکه با هزینه و مقرون به صرفه و انعطاف پذیر با استفاده ازهوش مصنوعی لایه لایه در لبه های شبکه خصوصی-2020 We observe two major revolutionary trends in network
operations: democratization of cost-effective and flexible
communication means for vertical players, such as public safety,
by private mobile networking combined with edge computing,
and automatic and autonomic network operations empowered
by Artificial Intelligence (AI). Further innovations are required
for making private networking readily available for vertical
players that are reluctant to acquire expertise in complex network
operations. We propose Edge Concierge, of which concept is to
democratize cost-effective and flexible network operations using
network layer AI at private network edges. Edge Concierge assists
smart network operations for private mobile network operators
and energy saving by changing working state of AI-empowered
anomaly detection applications by network layer AI. We also
employ unsupervised machine learning using Hidden Markov
Model (HMM) for estimating contexts by solely observing network
traffic at mobile edge computing (MEC) middle boxes. In
detail, we design a system of real-time and self-learning context
estimation by a multi-level probabilistic state transition model
trained by unsupervised learning, which is implemented in a
commodity PC. In order to evaluate our proposed system, we
take public safety context of smart cities as an example use case
and show the benefits. |
مقاله انگلیسی |
5 |
Multiple contents offloading mechanism in AI-enabled opportunistic networks
مکانیسم تخلیه محتوای چندگانه در شبکه های فرصت طلب مجهز به هوش مصنوعی-2020 With the rapid growth of mobile devices and the emergence of 5G applications, the burden of cellular
and the use of the licensed band have enormous challenges. In order to solve this problem, opportunity
communication is regarded as a potential solution. It can use unlicensed bands to forward content to users
under delay-tolerance constraints, as well as reduce cellular data traffic. Since opportunity communication is
easily interrupted when User Equipment (UE) is moving, we adopt Artificial Intelligence (AI) to predict the
location of the mobile UE. Then, the meta-heuristic algorithm is used to allocate multiple contents. In addition,
deep learning-based methods almost need a lot of training time. Based on real-time requirements of the
network, we propose AI-enabled opportunistic networks architecture, combined with Mobile Edge Computing
(MEC) to implement edge AI applications. The simulation results show that the proposed multiple contents
offloading mechanism can reduce cellular data traffic through UE location prediction and cache allocation. Keywords: Opportunistic networks | MEC | Offloading | Content caching |
مقاله انگلیسی |
6 |
A taxonomy of AI techniques for 6G communication networks
طبقه بندی تکنیک های هوش مصنوعی برای شبکه های ارتباطی 6G-2020 With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by
2030, many institutes worldwide have started to explore various issues and challenges in 6G communication
networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many
applications currently not viable by today’s 4G and 5G communication standards. The current 5G technology
has security and privacy issues which makes its usage in limited applications. In such an environment, we
believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead
cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled
6G communication technology, which can be used in wide range of future applications. In this article, we
explore how AI can be integrated into different applications such as object localization, UAV communication,
surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption
of AI techniques in intelligent transport system. Keywords: Artificial Intelligence | 6G | Communication networks | Mobile edge computing | Intelligent transportation system |
مقاله انگلیسی |
7 |
A Survey on the Computation Offloading Approaches in Mobile Edge Computing: A Machine Learning-based Perspective
بررسی رویکردهای بارگیری محاسبات در محاسبات لبه موبایل: دیدگاه مبتنی بر یادگیری ماشین-2020 With the rapid developments in emerging mobile technologies, utilizing resource-hungry mobile applications
such as media processing, online Gaming, Augmented Reality (AR), and Virtual Reality (VR) play an essential
role in both businesses and entertainments. To soften the burden of such complexities incurred by fast developments
of such serving technologies, distributed Mobile Edge Computing (MEC) has been developed, aimed
at bringing the computation environments near the end-users, usually in one hop, to reach predefined requirements.
In the literature, offloading approaches are developed to connect the computation environments to
mobile devices by transferring resource-hungry tasks to the near servers. Because of some rising problems such as
inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem,
the computation offloading issues consider as the essential challenging problems in the MEC environment.
However, to the best of the author’s knowledge, in spite of its significance, in machine learning-based (ML-based)
computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC
environment. In this paper, we provide a review on the ML-based computation offloading mechanisms in the
MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial
topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: Reinforcement
learning-based mechanisms, supervised learning-based mechanisms, and unsupervised learning-based mechanisms.
Next, these classes are compared with each other based on the essential features such as performance
metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed,
as well. Finally, open issues and uncovered or inadequately covered future research challenges are
argued, and the survey is concluded. Keywords: Computation offloading | Mobile edge computing | Machine learning | Reinforcement learning | Supervised learning | Unsupervised learning |
مقاله انگلیسی |
8 |
Deep reinforcement learning based mobile edge computing for intelligent Internet of Things
یادگیری تقویتی عمیق مبتنی بر محاسبات لبه تلفن همراه برای اینترنت هوشمند اشیا-2020 In this paper, we investigate mobile edge computing (MEC) networks for intelligent internet of
things (IoT), where multiple users have some computational tasks assisted by multiple computational
access points (CAPs). By offloading some tasks to the CAPs, the system performance can be improved
through reducing the latency and energy consumption, which are the two important metrics of
interest in the MEC networks. We devise the system by proposing the offloading strategy intelligently
through the deep reinforcement learning algorithm. In this algorithm, Deep Q-Network is used to
automatically learn the offloading decision in order to optimize the system performance, and a neural
network (NN) is trained to predict the offloading action, where the training data is generated from
the environmental system. Moreover, we employ the bandwidth allocation in order to optimize the
wireless spectrum for the links between the users and CAPs, where several bandwidth allocation
schemes are proposed. In further, we use the CAP selection in order to choose one best CAP to assist the
computational tasks from the users. Simulation results are finally presented to show the effectiveness
of the proposed reinforcement learning offloading strategy. In particular, the system cost of latency
and energy consumption can be reduced significantly by the proposed deep reinforcement learning
based algorithm. Keywords: Deep reinforcement learning | Intelligent IoT | Mobile edge computing |
مقاله انگلیسی |
9 |
Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT
تخصیص منابع پیشرفته در محاسبات لبه تلفن همراه با استفاده از الگوریتم MOACO مبتنی بر یادگیری تقویت کننده برای IIOT-2020 The Mobile networks deploy and offers a multiaspective approach for various resource allocation paradigms
and the service based options in the computing segments with its implication in the Industrial Internet of Things
(IIOT) and the virtual reality. The Mobile edge computing (MEC) paradigm runs the virtual source with the
edge communication between data terminals and the execution in the core network with a high pressure load.
The demand to meet all the customer requirements is a better way for planning the execution with the support
of cognitive agent. The user data with its behavioral approach is clubbed together to fulfill the service type for
IIOT. The swarm intelligence based and reinforcement learning techniques provide a neural caching for the
memory within the task execution, the prediction provides the caching strategy and cache business that delay
the execution. The factors affecting this delay are predicted with mobile edge computing resources and to
assess the performance in the neighboring user equipment. The effectiveness builds a cognitive agent model to
assess the resource allocation and the communication network is established to enhance the quality of service.
The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has
been applied to deal with the accurate resource allocation between the end users in the way of creating the
cost mapping tables creations and optimal allocation in MEC Keywords: Mobile edge computing | Industrial IOT | Reinforcement learning | Multi objective ant colony optimization | Resource allocation | Cognitive agent |
مقاله انگلیسی |
10 |
‘‘DRL + FL’’: An intelligent resource allocation model based on deep reinforcement learning for Mobile Edge Computing
"DRL + FL": یک مدل تخصیص منابع هوشمند مبتنی بر یادگیری تقویت عمیق برای محاسبات لبه تلفن همراه-2020 With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal
devices with limited resources can only run the model training part of most intelligent applications in the
cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause
of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important
extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage
resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency
and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and
maintenance, MEC also has the problems of limited network resources and computing resources, and the edge
network environment is complex and mutative. Therefore, how to reasonably allocate network resources and
computing resources in a changeable MEC environment has become a great aporia. To combat this issue,
this paper proposes an intelligent resource allocation model ‘‘DRL + FL’’. Based on this model, an intelligent
resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to
adaptively allocate network and computing resources. At the same time, the model integrates the FL framework
with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems
of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data
when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results
show that the proposed ‘‘DRL + FL’’ model is superior to the traditional resource allocation algorithms SDR
and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average
energy consumption of the system, minimizing the average service delay, and balancing resource allocation. Keywords: Mobile edge computing | Intelligent resource allocation | Deep reinforcement learning | Federated learning |
مقاله انگلیسی |