pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang, Lizhe; Ma, Yan; Yan, Jining; Chang, Victor; Zomaya, Albert Y.

doi:10.1016/j.future.2016.06.009

Cited by 169 publications

(85 citation statements)

References 46 publications

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“…For example, SpatialHadoop [51], which is an open-source system, has formed a complete ecological service form geospatial computing to applications. pipsCloud [52], a cloud-enabled high performance computing (HPC) platform for large-scale remote sensing applications, provides Hilbert-R+ tree and RS workflow processing across data centers. The applications are more extensive, involving environmental change [53], urban facilities [54], land cover [55], precision agriculture [33,56], and disaster warning [57] among others.…”

Section: Cloud Computing For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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Section: Cloud Computing For Beodmentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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“…It enables the realization of smart cities, smart health care, and smart infrastructures and services that can enhance our quality of life and improve the utilization of resources . In large‐scale remote sensing applications, territorial or worldwide scope multispectral and multitemporal remote sensing data sets are utilized for data processing to meet the need for more precise and up‐to‐date knowledge . The number of connected smart objects is estimated to reach 212 billion by the end of 2020 .…”

Section: Introductionmentioning

confidence: 99%

“…2 In large-scale remote sensing applications, territorial or worldwide scope multispectral and multitemporal remote sensing data sets are utilized for data processing to meet the need for more precise and up-to-date knowledge. 3 The number of connected smart objects is estimated to reach 212 billion by the end of 2020. 4,5 Such a large number of connected smart objects will generate huge volumes of data that need to be analyzed and stored.…”

Section: Introductionmentioning

confidence: 99%

Service level agreement specification for end‐to‐end IoT application ecosystems

et al. 2019

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Summary With an ever‐increasing variety and complexity of Internet of Things (IoT) applications delivered by increasing numbers of service providers, there is a growing demand for an automated mechanism that can monitor and regulate the interaction between the parties involved in IoT service provision and delivery. This mechanism needs to take the form of a contract, which, in this context, is referred to as a service level agreement (SLA). As a first step toward SLA monitoring and management, an SLA specification is essential. We believe that current SLA specification formats are unable to accommodate the unique characteristics of the IoT domain, such as its multilayered nature. Therefore, we propose a grammar for a syntactical structure of an SLA specification for IoT. The grammar is built based on a proposed conceptual model that considers the main concepts that can be used to express the requirements for hardware and software components of an IoT application on an end‐to‐end basis. We followed the goal question metric approach to evaluate the generality and expressiveness of the proposed grammar by reviewing its concepts and their predefined lists of vocabularies against two use cases with a considerable number of participants whose research interests are mainly related to IoT. The results of the analysis show that the proposed grammar achieved 91.70% of its generality goal and 93.43% of its expressiveness goal.

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“…First, the fog nodes are able to broadcast their positions to the mobile sensor who passed by. Second, the fog nodes are able to collect sensing data from the mobile sensors and send them to the remote datacenters, and then more sophisticated analysis can be done at the datacenters by big data techniques …”

Section: Introductionmentioning

confidence: 99%

Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

Shu

Yan

et al. 2018

Softw Pract Exp

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Burst or leakage in drinkable water distribution system has occurred frequently in recent years, causing severe damages, economic loss, and long-lasting society impact. A viable solution is to use agile inline mobile sensors to detect and so as to mitigate the burst or leakage. Distinguishing from online fixed sensors, mobile sensors can swim freely along the piles in water distribution network, thus giving a more precise detection. To combat the low power, low computation, and low communication capability of mobile sensors, the newly emerged fog computing provides a promising means to gather and preprocess the sensing data. In practice, due to the budget limitation, we can deploy a limited number of sensors and fog nodes in the system. This introduces a challenging problem on how to deploy them in the system, ie, sensor and fog node placement. We first formulate mobile sensor placement (MSP) as a path cover problem and prove it as NP-complete, and then we propose a customized genetic algorithm and a mixed greedy algorithm to solve MSP and fog node placement, respectively. The correctness and efficiency of the proposed algorithm are illustrated by a comprehensive experiment. Moreover, some critical factors, eg, sensor battery lifetime and movement pattern, are all extensively investigated and the results show the coverage ratio is sensitive to these factors.

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pipsCloud: High performance cloud computing for remote sensing big data management and processing

Cited by 169 publications

References 46 publications

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Service level agreement specification for end‐to‐end IoT application ecosystems

Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

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