Cognitive machine-to-machine communications: visions and potentials for the smart grid

Zhang, Yan; Yu, Rong; Nekovee, Maziar; Liu, Yi; Xie, Shengli; Gjessing, Stein

doi:10.1109/mnet.2012.6201210

Cited by 367 publications

(172 citation statements)

References 10 publications

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“…It arises a diversity of scalable smart communication systems, e.g., wireless sensor networks (WSNs), smart grid and smart home [19], [20]. The smart communication systems require smart devices (smart-phones, smart appliances, sensors, robots, surveillance devices) connected together.…”

Section: Implications Of Our Resultsmentioning

confidence: 99%

“…In addition, a base station can also work in a full-duplex mode, i.e., transmissions and receptions can occur in parallel. Our proposed MC-IS networks have provided a solution to the new applications, such as Device-to-Device (D2D) networks [18], wireless sensor networks (WSNs), smart grid and smart home [19], [20]. For example, the theoretical analysis on the throughput and the delay of MC-IS networks can be used to analyze the performance of the overlaid D2D networks (refer to Section VII-C for more details).…”

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confidence: 99%

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On Capacity and Delay of Multichannel Wireless Networks With Infrastructure Support

Dai

Wong

Wang

2017

IEEE Trans. Veh. Technol.

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Abstract-In this paper, we propose a novel multi-channel network with infrastructure support, called an MC-IS network, which has not been studied in the literature. To the best of our knowledge, we are the first to study such an MC-IS network. Our proposed MC-IS network has a number of advantages over three existing conventional networks, namely a single-channel wireless ad hoc network (called an SC-AH network), a multichannel wireless ad hoc network (called an MC-AH network) and a single-channel network with infrastructure support (called an SC-IS network). In particular, the network capacity of our proposed MC-IS network is √ n log n times higher than that of an SC-AH network and an MC-AH network and the same as that of an SC-IS network, where n is the number of nodes in the network. The average delay of our MC-IS network is log n/n times lower than that of an SC-AH network and an MC-AH network, and min{CI , m} times lower than the average delay of an SC-IS network, where CI and m denote the number of channels dedicated for infrastructure communications and the number of interfaces mounted at each infrastructure node, respectively. Our analysis on an MC-IS network equipped with omni-directional antennas only has been extended to an MC-IS network equipped with directional antennas only, which are named as an MC-IS-DA network. We show that an MC-IS-DA network has an even lower delay of , an MC-IS-DA can further reduce the delay by 24 times lower that of an MC-IS network and reduce the delay by 288 times lower than that of an SC-IS network.

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Section: Implications Of Our Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

On Capacity and Delay of Multichannel Wireless Networks With Infrastructure Support

Dai

Wong

Wang

2017

IEEE Trans. Veh. Technol.

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“…During the practical implementation of the CR and spectrum sensing (SS) technique researchers had to design cooperative frameworks, choose cooperative secondary users and transmit cooperative information during the spectrum sensing, further developing the framework and algorithms for the group handoff of secondary users and enhancing security [29]. Researchers are already working on designs of protocols for different IoT application, based on cognitive radio, and some of them are presented in [49][50][51]122]. Technologies in this group are used together to optimize 5G performance requirements (e.g., a prototype of mmWave integrated HetNet in [123] and HetNet that incorporates massive multiple-input and multiple-output (mMIMO) and mmWave technologies [217]).…”

Section: Wide and Flexible Bandwidth Technologymentioning

confidence: 99%

IoT’s Tiny Steps towards 5G: Telco’s Perspective

Cero¹,

Husić

Baraković

2017

Symmetry

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Abstract:The numerous and diverse applications of the Internet of Things (IoT) have the potential to change all areas of daily life of individuals, businesses, and society as a whole. The vision of a pervasive IoT spans a wide range of application domains and addresses the enabling technologies needed to meet the performance requirements of various IoT applications. In order to accomplish this vision, this paper aims to provide an analysis of literature in order to propose a new classification of IoT applications, specify and prioritize performance requirements of such IoT application classes, and give an insight into state-of-the-art technologies used to meet these requirements, all from telco's perspective. A deep and comprehensive understanding of the scope and classification of IoT applications is an essential precondition for determining their performance requirements with the overall goal of defining the enabling technologies towards fifth generation (5G) networks, while avoiding over-specification and high costs. Given the fact that this paper presents an overview of current research for the given topic, it also targets the research community and other stakeholders interested in this contemporary and attractive field for the purpose of recognizing research gaps and recommending new research directions.

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“…Efficient communications may require changes in parameters other than frequency, such as modulation, error-control coding, transmit power or activity pattern. As cognitive networks penetrate application space [2], accurate modeling of other (cognitive) spectrum users becomes essentially important for prediction of available "spectrum holes". That is why we do not limit ourselves to the usual CR model involving primary user (PU) and secondary user (SU) [3].…”

Section: Introductionmentioning

confidence: 99%

I/O HSMM: Learning behavioral dynamics of a cognitive wireless network node from spectrum sensing

Kokalj-Filipović

Goodman

Acosta

et al. 2016

2016 Annual Conference on Information Science and Systems (CISS)

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Abstract-We introduce a generative model, dubbed I/O HSMM, for learning the bi-modal behavioral dynamics of a network of cognitive radios (CRs). Each of the two modes of the CRs is represented as a Hidden Semi-Markov model (HSMM), where the states, state durations and emissions, transition probabilities between states, and transitions between modes are uncovered based solely on RF spectrum sensing. The learning of the CR dynamics is non-parametric and derived from the Hierarchical Dirichlet Process (HDP), with the switching between the two modes modeled as a latent variable that is estimated as a part of the learning process. The non-parametric model provides flexibility in handling unknown communication protocols. We evaluate the quality of learning against ground truth, and demonstrate that this approach is promising and merits extension to more complex models.

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Cognitive machine-to-machine communications: visions and potentials for the smart grid

Cited by 367 publications

References 10 publications

On Capacity and Delay of Multichannel Wireless Networks With Infrastructure Support

On Capacity and Delay of Multichannel Wireless Networks With Infrastructure Support

IoT’s Tiny Steps towards 5G: Telco’s Perspective

I/O HSMM: Learning behavioral dynamics of a cognitive wireless network node from spectrum sensing

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