Big Data Meet Green Challenges: Big Data Toward Green Applications

Wu, Jinsong; Guo, Song; Li, Jie; Zeng, Deze

doi:10.1109/jsyst.2016.2550530

Cited by 339 publications

(149 citation statements)

References 68 publications

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“…Pollution can also originate from oil spills. Predicting such catastrophes very early can help save beaches, coastlines and waters [6], [32]. Marine oil spills can be detected using a large archive of remote sensing big data [10]; and a real-time warning can be generated from a quantitative data analysis using supervised oil system [277].…”

Section: B Cps-based Big Data Toward Green Applicationsmentioning

confidence: 99%

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Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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Section: B Cps-based Big Data Toward Green Applicationsmentioning

confidence: 99%

“…• Third, we provide a summary in big data, storage, and communications for CPS. • Fourth, we provide a broader view of big data meeting green challenges for CPS, unlike [6], [29]- [32]. The sections of this paper are organized as follows.…”

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confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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“…The second approach is based on reputation (Chen et al 2014;Cho et al 2012), which is defined as a probability that an agent is trustworthy. In recent years, the concept of reputation has shown itself to be useful in many areas of research in computer science particularly in the wireless body area networks, cloud computing (Wu et al 2016(Wu et al , 2014Huang et al 2017), and social networks (Yuan et al 2017;Chen et al 2017;Shen et al 2015), where interesting issues of trust and security manifest themselves.…”

Section: Related Workmentioning

confidence: 99%

Trust architecture and reputation evaluation for internet of things

Chen

Tian

Cui

et al. 2018

J Ambient Intell Human Comput

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Internet of Things (IoT) represents a fundamental infrastructure and set of techniques that support innovative services in various application domains. Trust management plays an important role in enabling the reliable data collection and mining, context-awareness, and enhanced user security in the IoT. The main tasks of trust management include trust architecture design and reputation evaluation. However, existing trust architectures and reputation evaluation solutions cannot be directly applied to the IoT, due to the large number of physical entities, the limited computation ability of physical entities, and the highly dynamic nature of the network. In comparison, it generally requires a general and flexible architecture to manage trust in such a dynamic environment as IoT. In this paper, we present IoTrust, a trust architecture that integrates Soft Defined Network (SDN) in IoT, and a cross-layer authorization protocol based on IoTrust. IoTrust and the protocol together provide a new insight for research on trust management in the IoT. For trust establishment, we further propose a Behavior-based Reputation Evaluation Scheme for the Node (BES) and an Organization Reputation Evaluation Scheme (ORES). Both our theoretical analysis and simulation results validate the efficiency of BES and ORES.

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“…The Markov Decision Process (MDP) method can be applied to design optimal steering policies through theoretical analysis of small scale networks, which can provide a guideline to practical network operation. Furthermore, advanced machine learning technologies can be implemented to devise spatial-temporal traffic steering method for large scale networks, based on the big data analysis in real systems [14] [15].…”

Section: ) Service Capability With Ehmentioning

confidence: 99%

Energy-Sustainable Traffic Steering for 5G Mobile Networks

Zhang

Zhou

et al. 2017

IEEE Commun. Mag.

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Renewable energy harvesting (EH) technology is expected to be pervasively utilized in the next generation (5G) mobile networks to support sustainable network developments and operations. However, the renewable energy supply is inherently random and intermittent, which could lead to energy outage, energy overflow, quality of service (QoS) degradation, etc. Accordingly, how to enhance renewable energy sustainability is a critical issue for green networking. To this end, an energy-sustainable traffic steering framework is proposed in this article, where the traffic load is dynamically adjusted to match with energy distributions in both spatial and temporal domains by means of inter-and intra-tier steering, caching and pushing. Case studies are carried out, which demonstrate the proposed framework can reduce on-grid energy demand while satisfying QoS requirements. Research topics and challenges of energy-sustainable traffic steering are also discussed.The next generation (5G) mobile networks are expected to connect trillions of devices and provide 1000-fold network capacity by 2020 compared with that in 2010. Network densification (i.e., deploying more small cell base stations (SBSs)) can effectively improve the network capacity through spectrum reuse, and thus is considered as the key cornerstone for 5G. However, network densification may lead to huge energy consumption, causing heavy burdens to network operators. To tackle the cumbersome energy consumption, energy harvesting (EH) technology can be leveraged. Particularly, energy harvesting enabled base stations (EH-BSs) can exploit renewable energy as supplementary or alternative power sources to reduce the operational expenditures (OPEX). In addition, EH-BSs can be deployed more flexibly without the constraint of power lines. By 2011, over ten thousands EH-BSs have been deployed globally, and this figure will increase to more than 400,000 by 2020 [1].Despite the potential advantages, the inherent randomness of renewable energy poses significant technical challenges to network operations. Specifically, the mismatch between harvested energy and traffic distributions may result in energy outage and/or energy overflow, degrading the quality of service (QoS) and energy utilization. Thus, in addition to energy efficiency, a new performance measure "energy sustainability" should be introduced to keep the energy outage and the energy overflow probabilities as low as possible [2]. To this end, we propose a traffic steering framework to enhance energy sustainability in networks with EH-BSs.Traffic steering goes beyond traffic offloading, which pro-actively adjusts traffic distribution to match with and better utilize network resources, aiming at enhancing network performance or providing better QoS [3]. The proposed framework encompasses three approaches: inter-tier steering, intra-tier steering, content caching and pushing. Specifically, inter-tier steering adjusts the traffic load of each tier according to the variation of renewable energy arrival rate in a large time sca...

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Big Data Meet Green Challenges: Big Data Toward Green Applications

Cited by 339 publications

References 68 publications

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Trust architecture and reputation evaluation for internet of things

Energy-Sustainable Traffic Steering for 5G Mobile Networks

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