27,461 research outputs found
Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching
Fog computing is a promising architecture to
provide economical and low latency data services for future
Internet of Things (IoT)-based network systems. Fog computing
relies on a set of low-power fog nodes (FNs) that are located
close to the end users to offload the services originally targeting
at cloud data centers. In this paper, we consider a specific
fog computing network consisting of a set of data service operators
(DSOs) each of which controls a set of FNs to provide the
required data service to a set of data service subscribers (DSSs).
How to allocate the limited computing resources of FNs to all
the DSSs to achieve an optimal and stable performance is an
important problem. Therefore, we propose a joint optimization
framework for all FNs, DSOs, and DSSs to achieve the optimal
resource allocation schemes in a distributed fashion. In the
framework, we first formulate a Stackelberg game to analyze
the pricing problem for the DSOs as well as the resource allocation
problem for the DSSs. Under the scenarios that the DSOs
can know the expected amount of resource purchased by the
DSSs, a many-to-many matching game is applied to investigate
the pairing problem between DSOs and FNs. Finally, within the
same DSO, we apply another layer of many-to-many matching
between each of the paired FNs and serving DSSs to solve
the FN-DSS pairing problem. Simulation results show that our
proposed framework can significantly improve the performance
of the IoT-based network systems
Embedding Principal Component Analysis for Data Reductionin Structural Health Monitoring on Low-Cost IoT Gateways
Principal component analysis (PCA) is a powerful data reductionmethod for
Structural Health Monitoring. However, its computa-tional cost and data memory
footprint pose a significant challengewhen PCA has to run on limited capability
embedded platformsin low-cost IoT gateways. This paper presents a
memory-efficientparallel implementation of the streaming History PCA
algorithm.On our dataset, it achieves 10x compression factor and 59x
memoryreduction with less than 0.15 dB degradation in the
reconstructedsignal-to-noise ratio (RSNR) compared to standard PCA. More-over,
the algorithm benefits from parallelization on multiple cores,achieving a
maximum speedup of 4.8x on Samsung ARTIK 710
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
Recent advances in industrial wireless sensor networks towards efficient management in IoT
With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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