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Big Data and Cloud Computing for Sensor Networks
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Big Data and Cloud Computing for Sensor Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 September 2016) | Viewed by 35786

Special Issue Editor

Prof. Dr. Yike Guo

E-Mail Website
Guest Editor
Department of Computing, Imperial College London, 180 Queen's Gate, London SW7 2BZ, UK
Interests: cloud; big data; informatics; e-science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The potential of big data has been unleashed by the so-called Internet of Things, which is actually the internet of sensors and actuators. Sensors are everywhere in our lives, from our smartphones, to our game controllers, to our cars. With such pervasive sensing, computing and connectivity, everything we do creates a rich digital footprint. All these data captured contain valuable insights. A big data approach will enable business users, along with data scientists, to fully unlock the value in data. Low-cost sensor networks combined with affordable access to cloud computing have inspired a wave of innovations that are making our lives better. For example, if we can integrate healthcare with smart sensors, passive monitoring and big data analytics to measure, monitor and interpret daily activity levels for elderly patients, we can develop various useful applications for patients and medical professionals. But all opportunities come with challenges. The ability to discover, store, clean, analyse, and model big sensor data still needs to be developed.

In this Special Issue, we will focus on those researchers trying to apply big data methodologies to uncover the value from sensor data in various areas such as traffic, environment, smart grids, maintenance management, healthcare, etc., or designing novel infrastructure, services, and applications to support big sensor data analytics. Topics related to the ecosystem around sensor data (e.g., sensor data economy, sensor data privacy and ownership) are also welcome.

Prof. Dr. Yike Guo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Published Papers (6 papers)

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Research

8038 KiB  
Article
A Visual Analytics Approach for Station-Based Air Quality Data
by Yi Du, Cuixia Ma, Chao Wu, Xiaowei Xu, Yike Guo, Yuanchun Zhou and Jianhui Li
Sensors 2017, 17(1), 30; https://doi.org/10.3390/s17010030 - 24 Dec 2016
Cited by 16 | Viewed by 6494
Abstract
With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several [...] Read more.
With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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13353 KiB  
Article
Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media
by Jiansu Pu, Zhiyao Teng, Rui Gong, Changjiang Wen and Yang Xu
Sensors 2016, 16(12), 2194; https://doi.org/10.3390/s16122194 - 20 Dec 2016
Cited by 6 | Viewed by 5648
Abstract
Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring [...] Read more.
Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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2028 KiB  
Article
Performance Prediction of a MongoDB-Based Traceability System in Smart Factory Supply Chains
by Yong-Shin Kang, Il-Ha Park and Sekyoung Youm
Sensors 2016, 16(12), 2126; https://doi.org/10.3390/s16122126 - 14 Dec 2016
Cited by 25 | Viewed by 6997
Abstract
In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing [...] Read more.
In the future, with the advent of the smart factory era, manufacturing and logistics processes will become more complex, and the complexity and criticality of traceability will further increase. This research aims at developing a performance assessment method to verify scalability when implementing traceability systems based on key technologies for smart factories, such as Internet of Things (IoT) and BigData. To this end, based on existing research, we analyzed traceability requirements and an event schema for storing traceability data in MongoDB, a document-based Not Only SQL (NoSQL) database. Next, we analyzed the algorithm of the most representative traceability query and defined a query-level performance model, which is composed of response times for the components of the traceability query algorithm. Next, this performance model was solidified as a linear regression model because the response times increase linearly by a benchmark test. Finally, for a case analysis, we applied the performance model to a virtual automobile parts logistics. As a result of the case study, we verified the scalability of a MongoDB-based traceability system and predicted the point when data node servers should be expanded in this case. The traceability system performance assessment method proposed in this research can be used as a decision-making tool for hardware capacity planning during the initial stage of construction of traceability systems and during their operational phase. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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556 KiB  
Article
Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data
by Qingqing Xie and Liangmin Wang
Sensors 2016, 16(12), 1993; https://doi.org/10.3390/s16121993 - 25 Nov 2016
Cited by 10 | Viewed by 4774
Abstract
With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the [...] Read more.
With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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4162 KiB  
Article
Secure Nearest Neighbor Query on Crowd-Sensing Data
by Ke Cheng, Liangmin Wang and Hong Zhong
Sensors 2016, 16(10), 1545; https://doi.org/10.3390/s16101545 - 22 Sep 2016
Cited by 4 | Viewed by 5329
Abstract
Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing [...] Read more.
Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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2840 KiB  
Article
Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment
by Qi Liu, Weidong Cai, Dandan Jin, Jian Shen, Zhangjie Fu, Xiaodong Liu and Nigel Linge
Sensors 2016, 16(9), 1386; https://doi.org/10.3390/s16091386 - 30 Aug 2016
Cited by 15 | Viewed by 5788
Abstract
Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence [...] Read more.
Distributed Computing has achieved tremendous development since cloud computing was proposed in 2006, and played a vital role promoting rapid growth of data collecting and analysis models, e.g., Internet of things, Cyber-Physical Systems, Big Data Analytics, etc. Hadoop has become a data convergence platform for sensor networks. As one of the core components, MapReduce facilitates allocating, processing and mining of collected large-scale data, where speculative execution strategies help solve straggler problems. However, there is still no efficient solution for accurate estimation on execution time of run-time tasks, which can affect task allocation and distribution in MapReduce. In this paper, task execution data have been collected and employed for the estimation. A two-phase regression (TPR) method is proposed to predict the finishing time of each task accurately. Detailed data of each task have drawn interests with detailed analysis report being made. According to the results, the prediction accuracy of concurrent tasks’ execution time can be improved, in particular for some regular jobs. Full article
(This article belongs to the Special Issue Big Data and Cloud Computing for Sensor Networks)
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