Topology Control for Building a Large-Scale and Energy-Efficient Internet of Things

Huang, Jun; Duan, Qiang; Xing, Cong-Cong; Wang, Honggang

doi:10.1109/mwc.2017.1600193wc

Cited by 65 publications

(28 citation statements)

References 14 publications

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“…Considering the increasing number of interconnected devices, the size or scale of IoT software systems can grow consistently. The systems can achieve a more wide-scale coupled with complicated structure-controlling techniques, which brings new challenges to their design and deployment (Huang et al 2017). New solutions for architectural foundations, orchestration, and management are essential for dealing with scale issues, especially for Ultra Large Scale Systems such as Smart Cities and autonomous vehicles (Roca et al 2018).…”

Section: Output: Putting All Togethermentioning

confidence: 99%

On Challenges in Engineering IoT Software Systems

Motta

Oliveira

Travassos

2019

JSERD

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Contemporary software systems, such as the Internet of Things (IoT), Industry 4.0, and Smart Cities represent a technology changing that offer challenges for their construction since they are calling into question our traditional form of developing software. They are a promising paradigm for the integration of devices and communications technologies. It is leading to a shift in the classical monolithic view of development where stakeholders used to receive a software product at the end (that we have been doing for decades), to software systems incrementally materialized through physical objects interconnected by networks and with embedded software to support daily activities. Therefore, we need to revisit the classical way of developing software and start to consider the particularities required by these new sorts of applications. Since such software systems involve different concerns, this paper presents the results of an investigation towards defining a framework to support the software systems engineering of IoT applications. To support its representation, we evolved the Zachman’s Framework as an alternative to the organization of the framework architecture. The filling of such a framework is supported by a) 14 significant concerns of IoT applications, recovered from the technical literature, practitioner’s workshops and a Government Report; b) seven structured facets emerged from IoT data analysis, that together represent the engineering challenges to be faced both by researchers and practitioners towards the advancement of IoT in practice.

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Section: Output: Putting All Togethermentioning

confidence: 99%

On Challenges in Engineering IoT Software Systems

Motta

Oliveira

Travassos

2019

JSERD

View full text Add to dashboard Cite

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“…Lots of researchers have investigated network traffic prediction that is instructive for congestion control, predictive network planning, and intelligent routing [10][11][12][13][14]. The existing network traffic prediction techniques consist of four categories: linear time series methods, nonlinear time series methods, hybrid model methods, and decomposition model methods.…”

Section: Related Workmentioning

confidence: 99%

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Nie

Wang

Wan

et al. 2018

Wireless Communications and Mobile Computing

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Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

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“…The huge number of wireless devices leads to the growing energy consumption of wireless communications, which brings the increase of the greenhouse. Therefore, green communication (i.e., energy-efficient communication) has became a significant standard for future IoT networks, which means that the low energy consumption and high data throughput should be simultaneously satisfied [2], [3]. To realize the requirement of the massive wireless devices connectivity in IoT networks, non-orthogonal multiple access (NOMA) technique can be as an effective candidate because it allows multiple devices to connect to the network with the same time frequency resource [4].…”

Section: Introductionmentioning

confidence: 99%

Green Communication for NOMA-Based CRAN

Hao

Chu

Zhou

et al. 2019

IEEE Internet Things J.

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Abstract-The wireless devices will rapidly growth with the application of the Internet of Thing (IoT), which results in the huge power consumption. To realize the green communication, in this paper, we study the energy efficiency (EE) problem in a non-orthogonal multiple access (NOMA)-based CRAN, where microwave and millimeter wave (mmWave) are applied in fronthaul and access links, respectively. Based on this, we formulate the power optimization problem to maximize the EE of the system subject to the fronthaul capacity and transmit power constraints. Due to the formulated problem is nonconvex, we first transform the fractional objective function into the subtractive form. After that, an algorithm containing outer and inner loops is proposed to deal with this non-convex problem. In particular, in the outer loop, the 1 -norm technique is adopted to transform the nonconvex fronthaul capacity constraint into the convex one; while in the inner loop, the weighted minimum mean square error (WMMSE) approach is applied to solve the formulated problem. Simulation results demonstrate that the proposed NOMA scheme can obtain higher EE as well as throughput in comparison with the conventional orthogonal multiple access (OMA) scheme.

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Topology Control for Building a Large-Scale and Energy-Efficient Internet of Things

Cited by 65 publications

References 14 publications

On Challenges in Engineering IoT Software Systems

On Challenges in Engineering IoT Software Systems

Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Green Communication for NOMA-Based CRAN

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