2,647 research outputs found
A dynamic edge caching framework for mobile 5G networks
© 2002-2012 IEEE. Mobile edge caching has emerged as a new paradigm to provide computing, networking resources, and storage for a variety of mobile applications. That helps achieve low latency, high reliability, and improve efficiency in handling a very large number of smart devices and emerging services (e.g., IoT, industry automation, virtual reality) in mobile 5G networks. Nonetheless, the development of mobile edge caching is challenged by the decentralized nature of edge nodes, their small coverage, limited computing, and storage resources. In this article, we first give an overview of mobile edge caching in 5G networks. After that, its key challenges and current approaches are discussed. We then propose a novel caching framework. Our framework allows an edge node to authorize the legitimate users and dynamically predicts and updates their content demands using the matrix factorization technique. Based on the prediction, the edge node can adopt advanced optimization methods to determine optimal content to store so as to maximize its revenue and minimize the average delay of its mobile users. Through numerical results, we demonstrate that our proposed framework provides not only an effective caching approach, but also an efficient economic solution for the mobile service provider
Internet of Things From Hype to Reality
The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions
Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms
The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation
A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System
IoT has enabled the rapid growth of smart remote healthcare applications.
These IoT-based remote healthcare applications deliver fast and preventive
medical services to patients at risk or with chronic diseases. However,
ensuring data security and patient privacy while exchanging sensitive medical
data among medical IoT devices is still a significant concern in remote
healthcare applications. Altered or corrupted medical data may cause wrong
treatment and create grave health issues for patients. Moreover, current remote
medical applications' efficiency and response time need to be addressed and
improved. Considering the need for secure and efficient patient care, this
paper proposes a lightweight Blockchain-based and Fog-enabled remote patient
monitoring system that provides a high level of security and efficient response
time. Simulation results and security analysis show that the proposed
lightweight blockchain architecture fits the resource-constrained IoT devices
well and is secure against attacks. Moreover, the augmentation of Fog computing
improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of
Things; Engineering Cyber Physical Human Systems" journal on January 9, 202
Interpretability of AI in Computer Systems and Public Policy
Advances in Artificial Intelligence (AI) have led to spectacular innovations and sophisticated systems for tasks that were thought to be capable only by humans. Examples include playing chess and Go, face and voice recognition, driving vehicles, and more. In recent years, the impact of AI has moved beyond offering mere predictive models into building interpretable models that appeal to human logic and intuition because they ensure transparency and simplicity and can be used to make meaningful decisions in real-world applications. A second trend in AI is characterized by important advancements in the realm of causal reasoning. Identifying causal relationships is an important aspect of scientific endeavors in a variety of fields. Causal models and Bayesian inference can help us gain better domain-specific insight and make better data-driven decisions because of their interpretability. The main objective of this dissertation was to adapt theoretically sound AI-based interpretable data-analytic approaches to solve domain-specific problems in the two un-related fields of Storage Systems and Public Policy. For the first task, we considered the well-studied problem of cache replacement problem in computing systems, which can be modeled as a variant of the well-known Multi-Armed Bandit (MAB) problem with delayed feedback and decaying costs, and developed an algorithm called EXP4-DFDC. We proved theoretically that EXP4-DFDC exhibits an important feature called vanishing regret. Based on the theoretical analysis, we designed a machine-learning algorithm called ALeCaR, with adaptive hyperparameters. We used extensive experiments on a wide range of workloads to show that ALeCaR performed better than LeCaR, the best machine learning algorithm for cache replacement at that time. We concluded that reinforcement machine learning can offer an outstanding approach for implementing cache management policies. For the second task, we used Bayesian networks to analyze the service request data from three 311 centers providing non-emergency services in the cities of Miami-Dade, New York City, and San Francisco. Using a causal inference approach, this study investigated the presence of inequities in the quality of the 311 services to neighborhoods with varying demographics and socioeconomic status. We concluded that the services provided by the local governments showed no detectable biases on the basis of race, ethnicity, or socioeconomic status
Performance evaluation of cooperation strategies for m-health services and applications
Health telematics are becoming a major improvement for patients’ lives, especially for
disabled, elderly, and chronically ill people. Information and communication technologies have
rapidly grown along with the mobile Internet concept of anywhere and anytime connection.
In this context, Mobile Health (m-Health) proposes healthcare services delivering, overcoming
geographical, temporal and even organizational barriers. Pervasive and m-Health services aim
to respond several emerging problems in health services, including the increasing number of
chronic diseases related to lifestyle, high costs in existing national health services, the need
to empower patients and families to self-care and manage their own healthcare, and the need
to provide direct access to health services, regardless the time and place. Mobile Health (m-
Health) systems include the use of mobile devices and applications that interact with patients
and caretakers. However, mobile devices have several constraints (such as, processor, energy,
and storage resource limitations), affecting the quality of service and user experience. Architectures
based on mobile devices and wireless communications presents several challenged issues
and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections,
noises, limited bandwidths, and network delays. In this sense, cooperation-based
approaches are presented as a solution to solve such limitations, focusing on increasing network
connectivity, communication rates, and reliability. Cooperation is an important research topic
that has been growing in recent years. With the advent of wireless networks, several recent
studies present cooperation mechanisms and algorithms as a solution to improve wireless networks
performance. In the absence of a stable network infrastructure, mobile nodes cooperate
with each other performing all networking functionalities. For example, it can support intermediate
nodes forwarding packets between two distant nodes.
This Thesis proposes a novel cooperation strategy for m-Health services and applications.
This reputation-based scheme uses a Web-service to handle all the nodes reputation and networking
permissions. Its main goal is to provide Internet services to mobile devices without
network connectivity through cooperation with neighbor devices. Therefore resolving the above
mentioned network problems and resulting in a major improvement for m-Health network architectures
performances. A performance evaluation of this proposal through a real network
scenario demonstrating and validating this cooperative scheme using a real m-Health application
is presented. A cryptography solution for m-Health applications under cooperative environments,
called DE4MHA, is also proposed and evaluated using the same real network scenario and
the same m-Health application. Finally, this work proposes, a generalized cooperative application
framework, called MobiCoop, that extends the incentive-based cooperative scheme for
m-Health applications for all mobile applications. Its performance evaluation is also presented
through a real network scenario demonstrating and validating MobiCoop using different mobile
applications
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
Federated learning plays an important role in the process of smart cities.
With the development of big data and artificial intelligence, there is a
problem of data privacy protection in this process. Federated learning is
capable of solving this problem. This paper starts with the current
developments of federated learning and its applications in various fields. We
conduct a comprehensive investigation. This paper summarize the latest research
on the application of federated learning in various fields of smart cities.
In-depth understanding of the current development of federated learning from
the Internet of Things, transportation, communications, finance, medical and
other fields. Before that, we introduce the background, definition and key
technologies of federated learning. Further more, we review the key
technologies and the latest results. Finally, we discuss the future
applications and research directions of federated learning in smart cities
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