A Robust Time Synchronization Scheme for Industrial Internet of Things

Qiu, Tie; Zhang, Yushuang; Qiao, Daji; Zhang, Xiaoyun; Wymore, Mathew L.; Sangaiah, Arun Kumar

doi:10.1109/tii.2017.2738842

Cited by 112 publications

(62 citation statements)

References 27 publications

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“…And the user-specific parameters are uniformly distributed random values within (1, 3). In addition, according to the data obtained from the = 0 // to initialize the reward of every sub-area to 0 (3) End for (4) …”

Section: Methodology and Setupmentioning

confidence: 99%

“…Internet of Things (IoT) [1][2][3][4][5] and cloud computing [6][7][8][9][10][11][12][13] take advantage of the ubiquity of smart sensor-equipped devices such as smartphones, iPad, and vehicle sensor devices All of these are to collect information with low cost and provide a new paradigm for solving the complex data sensing based applications from the significant demands of critical infrastructure such as surveillance systems [14][15][16], remote patient care systems in healthcare, intelligent traffic management, and automated vehicles in transportation environmental [17][18][19] and weather monitoring systems. In such applications, due to the need to collect a wide range and large amount of data even in a long-term continuous period, the cost of traditional method deploying sensing devices or specialized employee to collect the data is so high limiting the development of applications [20,21].…”

Section: Introductionmentioning

confidence: 99%

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A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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To tackle the issue in deep crowd sensing, a Time and Location Correlation Incentive (TLCI) scheme is proposed for deep data gathering in crowdsourcing networks. In TLCI scheme, a metric named "Quality of Information Satisfaction Degree" (QoISD) is to quantify how much collected sensing data can satisfy the application's QoI requirements mainly in terms of data quantity and data coverage. Two incentive algorithms are proposed to satisfy QoISD with different view. The first algorithm is to ensure that the application gets the specified sensing data to maximize the QoISD. Thus, in the first incentive algorithm, the reward for data sensing is to maximize the QoISD. The second algorithm is to minimize the cost of the system while meeting the sensing data requirement and maximizing the QoISD. Thus, in the second incentive algorithm, the reward for data sensing is to maximize the QoISD per unit of reward. Finally, we compare our proposed scheme with existing schemes via extensive simulations. Extensive simulation results well justify the effectiveness of our scheme. The QoISD can be optimized by 81.92%, and the total cost can be reduced by 31.38%.

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Section: Methodology and Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Liu

et al. 2018

Wireless Communications and Mobile Computing

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“…The authors present a power-efficient and computationally simple solution that achieves ms-level time synchronization (one transmission per 3 seconds). Qiu et al [17] present R-Sync, a robust time synchronization scheme for the Industrial IoT that focuses on identifying isolated nodes that have lost their synchronization and pull them back in the network. Dong et al [18] present a secure time synchronization scheme that is resilient to sybil attacks (i.e.…”

Section: Introductionmentioning

confidence: 99%

Temperature-Resilient Time Synchronization for the Internet of Things

Elsts

Fafoutis

Duquennoy

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-Networks deployed in real-world conditions have to cope with dynamic, unpredictable environmental temperature changes. These changes affect the clock rate on network nodes, and can cause faster clock de-synchronization compared to situations where devices are operating under stable temperature conditions. Wireless network protocols such as Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard are affected by this problem, since they require tight clock synchronization among all nodes for the network to remain operational. This paper proposes a method for autonomously compensating temperature-dependent clock rate changes. After a calibration stage, nodes continuously perform temperature measurements to compensate for clock drifts at run-time. The method is implemented on low-power IoT nodes and evaluated through experiments in a temperature chamber, indoor and outdoor environments, as well as with numerical simulations. The results show that applying the method reduces the maximum synchronization error more than 10 times. In this way, the method allows reduce the total energy spent for time synchronization, which is practically relevant concern for low data rate, low energy budget TSCH networks, especially those exposed to environments with changing temperature.

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“…A great number of methods have been proposed to deal with the network traffic prediction problem in traditional IP backbone networks [4][5][6][7][8]. Statistical methods have been widely adopted in this field.…”

Section: Introductionmentioning

confidence: 99%

“…With the variety of network services and applications, the characteristics of network traffic are much more complex. For instance, it exhibits some nonlinear features [4,6]. Hence, some methods refer to the GARCH model to model network traffic for prediction.…”

Section: Introductionmentioning

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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A Robust Time Synchronization Scheme for Industrial Internet of Things

Cited by 112 publications

References 27 publications

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

A Time and Location Correlation Incentive Scheme for Deep Data Gathering in Crowdsourcing Networks

Temperature-Resilient Time Synchronization for the Internet of Things

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

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