7,491 research outputs found
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
Machine Intelligence Techniques for Next-Generation Context-Aware Wireless Networks
The next generation wireless networks (i.e. 5G and beyond), which would be
extremely dynamic and complex due to the ultra-dense deployment of
heterogeneous networks (HetNets), poses many critical challenges for network
planning, operation, management and troubleshooting. At the same time,
generation and consumption of wireless data are becoming increasingly
distributed with ongoing paradigm shift from people-centric to machine-oriented
communications, making the operation of future wireless networks even more
complex. In mitigating the complexity of future network operation, new
approaches of intelligently utilizing distributed computational resources with
improved context-awareness becomes extremely important. In this regard, the
emerging fog (edge) computing architecture aiming to distribute computing,
storage, control, communication, and networking functions closer to end users,
have a great potential for enabling efficient operation of future wireless
networks. These promising architectures make the adoption of artificial
intelligence (AI) principles which incorporate learning, reasoning and
decision-making mechanism, as natural choices for designing a tightly
integrated network. Towards this end, this article provides a comprehensive
survey on the utilization of AI integrating machine learning, data analytics
and natural language processing (NLP) techniques for enhancing the efficiency
of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data
acquisition, knowledge discovery, network planning, operation and management of
the next generation wireless networks. A brief case study utilizing the AI
techniques for this network has also been provided.Comment: ITU Special Issue N.1 The impact of Artificial Intelligence (AI) on
communication networks and services, (To appear
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Towards combinatorial modeling of wireless technology generations
The paper addresses the following problems: (1) a brief survey on wireless
mobile communication technologies including evolution, history evolution (e.g.,
chain of system generations 0G, 1G, 2G, 3G, 4G, 5G, 6G, 7G); (2) using a
hierarchical structural modular approach to the generations of the wireless
communication systems (i.e., hierarchical combinatorial modeling of the
communication technologies), (3) illustrative usage of two-stage combinatorial
approach to improvement/forecasting of the communication technology (a version
of 5G) (on the basis of multiple choice problem). Numerical examples illustrate
the suggested combinatorial approach.Comment: 20 pages, 13 figures, 9 table
Cyber Physical Systems: Prospects and Challenges
Cyber physical systems CPSs embodies the conception as well as the
implementation of the integration of the state-of-art technologies in sensing,
communication, computing, and control. Such systems incorporate new trends such
as cloud computing, mobile computing, mobile sensing, new modes of
communications, wearables, etc. In this article we give an exposition of the
architecture of a typical CPS system and the prospects of such systems in the
development of the modern world. We illustrate the three major challenges faced
by a CPS system: the need for rigorous numerical computation, the limitation of
the current wireless communication bandwidth, and the computation/storage
limitation by mobility and energy consumption. We address each one of these
exposing the current techniques devised to solve each one of them
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
The Role of Cloud-MANET Framework in the Internet of Things (IoT)
In the next generation of computing, Mobile ad-hoc network (MANET) will play
a very important role in the Internet of Things (IoT). The MANET is a kind of
wireless networks that are self-organizing and auto connected in a
decentralized system. Every device in MANET can be moved freely from one
location to another in any direction. They can create a network with their
neighbors smart devices and forward data to another device. The IoT-Cloud-MANET
framework of smart devices is composed of IoT, cloud computing, and MANET. This
framework can access and deliver cloud services to the MANET users through
their smart devices in the IoT framework where all computations, data handling,
and resource management are performed. The smart devices can move from one
location to another within the range of the MANET network. Various MANETs can
connect to the same cloud, they can use cloud service in a real time. For
connecting the smart device of MANET to cloud needs integration with mobile
apps. My main contribution in this research links a new methodology for
providing secure communication on the internet of smart devices using MANET
Concept in 5G. The research methodology uses the correct and efficient
simulation of the desired study and can be implemented in a framework of the
Internet of Things in 5G.Comment: arXiv admin note: text overlap with arXiv:1902.0974
Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities
The ever-increasing mobile data demands have posed significant challenges in
the current radio access networks, while the emerging computation-heavy
Internet of things (IoT) applications with varied requirements demand more
flexibility and resilience from the cloud/edge computing architecture. In this
article, to address the issues, we propose a novel air-ground integrated mobile
edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and
assist the communication, caching, and computing of the edge network. In
specific, we present the detailed architecture of AGMEN, and investigate the
benefits and application scenarios of drone-cells, and UAV-assisted edge
caching and computing. Furthermore, the challenging issues in AGMEN are
discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure
Delay constrained Energy Optimization for Edge Cloud Offloading
Resource limited user-devices may offload computation to a cloud server, in
order to reduce power consumption and lower the execution time. However, to
communicate to the cloud server over a wireless channel, additional energy is
consumed for transmitting the data. Also a delay is introduced for offloading
the data and receiving the response. Therefore, an optimal decision needs to be
made that would reduce the energy consumption, while simultaneously satisfying
the delay constraint. In this paper, we obtain an optimal closed form solution
for these decision variables in a multi-user scenario. Furthermore, we
optimally allocate the cloud server resources to the user devices, and evaluate
the minimum delay that the system can provide, for a given bandwidth and number
of user devices.Comment: Published in ICC workshop 201
Generalized Sparse and Low-Rank Optimization for Ultra-Dense Networks
Ultra-dense network (UDN) is a promising technology to further evolve
wireless networks and meet the diverse performance requirements of 5G networks.
With abundant access points, each with communication, computation and storage
resources, UDN brings unprecedented benefits, including significant improvement
in network spectral efficiency and energy efficiency, greatly reduced latency
to enable novel mobile applications, and the capability of providing massive
access for Internet of Things (IoT) devices. However, such great promises come
with formidable research challenges. To design and operate such complex
networks with various types of resources, efficient and innovative
methodologies will be needed. This motivates the recent introduction of highly
structured and generalizable models for network optimization. In this article,
we present some recently proposed large-scale sparse and low-rank frameworks
for optimizing UDNs, supported by various motivating applications. A special
attention is paid on algorithmic approaches to deal with nonconvex objective
functions and constraints, as well as computational scalability.Comment: This paper has been accepted by IEEE Communication Magazine, Special
Issue on Heterogeneous Ultra Dense Network
- …