Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

Sun, Gang; Zhou, Run; Sun, Jian; Yu, Hongfang; Vasilakos, Athanasios V.

doi:10.1109/jiot.2020.2970995

Cited by 68 publications

(34 citation statements)

References 51 publications

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“…For training, they used different inductive learning models such as Decision Trees, k-Nearest Neighbor, Naive Bayes, and Bayes Net and evaluated their approach using five-fold cross-validation. Authors in [40][41][42][43][44][45][46] discussed the network protocol to use and process the sensors and wearable devices data over the network. The research [13] proposed an assisted approach that helps the participants to live healthily.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…We implemented and deployed our experimental prototype system on a commercial desktop computer (64-bit Windows 10, i5-8500, 3.0Hz, 16GB memory) [27,28]. In order to reduce identification time for whole Ipv4 cyberspace, we crawl a part of Internet-connected IoT devices in Censys.…”

Section: Algorithm 1 Value Iteration Algorithmmentioning

confidence: 99%

A Time-efficient Multi-Protocol Probe Scheme for Fine-grain IoT Device Identification

Chen

et al. 2020

Sensors

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Internet of Things (IoT) devices connected to the Internet are exploding, which poses a significant threat for their management and security protection. IoT device identification is a prerequisite for discovering, monitoring, and protecting these devices. Although we can identify the device type easily through grabbing protocol banner information, both brand and model of different types of device are various and diverse. We should therefore utilize multi-protocol probes to improve the fineness of device identification and obtain the corresponding brand and model. However, it is still a challenge to balance between the multi-protocol probe overhead and the identification fineness. To solve this problem, we proposed a time-efficient multi-protocol probe scheme for fine-grain devices identification. We first adopted the concept of reinforcement learning to model the banner-based device identification process into a Markov decision process (MDP). Through the value iteration algorithm, an optimal multi-protocol probe sequence is generated for a type-known IoT device, and then the optimal multi-protocol probes sequence segment is extracted based on the gain threshold of identification accuracy. We took 132,835 webcams as the sample data to experiment. The experimental results showed that our optimal multi-protocol probes sequence segment could reduce the identification time of webcams’ brand and model by 50.76% and achieve the identification accuracy of 90.5% and 92.3% respectively. In addition, we demonstrated that our time-efficient optimal multi-protocol probe scheme could also significantly improve the identification efficiency of other IoT devices, such as routers and printers.

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“…Considering energy wastage and ignorance of processing requests by the network, the authors proposed an EAR-ADS algorithm (including energy-aware routing and an adaptive delayed shutdown mechanism) in [16]. This algorithm deployed dynamic service function chains (SFC), which means offline and on-off nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective

et al. 2020

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Advertising over smart devices is one of the growing trends in the information technology domain. Most of the Android application (app) developers generate revenue through the use of ads, but on the other hand, the end users get the free app. However, the excessive number of ads infers hidden costs with respect to energy consumption, network utilization, and user comfort. These factors affect the app rating and reviews. Consequently, developers require a technique to balance app performance through optimized mobile ad usage. Therefore, in this paper, we extend an existing work and propose an energy-efficient method that uses gamma correction to reduce the hidden costs of a mobile app. In this approach, gamma correction efficiently balances the app performance by minimizing the size of ads. The size of the mobile ad is reduced by adjusting its pixels, reducing background color, and illuminating the content of the ad. After several experiments, it is deduced that our proposed approach efficiently saves mobile battery and developers can apply this approach to improve app rank and feedback.

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Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

Cited by 68 publications

References 51 publications

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

A Time-efficient Multi-Protocol Probe Scheme for Fine-grain IoT Device Identification

Towards Energy-Efficient Mobile Ad Optimization: An App Developer Perspective

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