Published: Jan 1, 2016
Converted to Gold OA:
DOI: 10.4018/IJDST.20160101.Pre
Volume 7
Maria Fazio, Rajiv Ranjan, Massimo Villari
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MLA
Fazio, Maria, et al. "Special Issue on Advances in Cloud for Smart Cities." IJDST vol.7, no.1 2016: pp.4-6. http://doi.org/10.4018/IJDST.20160101.Pre
APA
Fazio, M., Ranjan, R., & Villari, M. (2016). Special Issue on Advances in Cloud for Smart Cities. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 4-6. http://doi.org/10.4018/IJDST.20160101.Pre
Chicago
Fazio, Maria, Rajiv Ranjan, and Massimo Villari. "Special Issue on Advances in Cloud for Smart Cities," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 4-6. http://doi.org/10.4018/IJDST.20160101.Pre
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Published: Jan 1, 2016
Converted to Gold OA:
DOI: 10.4018/IJDST.2016010101
Volume 7
Xihuang Sun, Peng Liu, Yan Ma, Dingsheng Liu, Yechao Sun
The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing...
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The explosion of data and the increase in processing complexity, together with the increasing needs of real-time processing and concurrent data access, make remote sensing data streaming processing a wide research area to study. This paper introduces current situation of remote sensing data processing and how timely remote sensing data processing can help build future smart cities. Current research on remote sensing data streaming is also introduced where the three typical and open-source stream processing frameworks are introduced. This paper also discusses some design concerns for remote sensing data streaming processing systems, such as data model and transmission, system model, programming interfaces, storage management, availability, etc. Finally, this research specifically addresses some of the challenges of remote sensing data streaming processing, such as scalability, fault tolerance, consistency, load balancing and throughput.
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Sun, Xihuang, et al. "Streaming Remote Sensing Data Processing for the Future Smart Cities: State of the Art and Future Challenges." IJDST vol.7, no.1 2016: pp.1-14. http://doi.org/10.4018/IJDST.2016010101
APA
Sun, X., Liu, P., Ma, Y., Liu, D., & Sun, Y. (2016). Streaming Remote Sensing Data Processing for the Future Smart Cities: State of the Art and Future Challenges. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 1-14. http://doi.org/10.4018/IJDST.2016010101
Chicago
Sun, Xihuang, et al. "Streaming Remote Sensing Data Processing for the Future Smart Cities: State of the Art and Future Challenges," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 1-14. http://doi.org/10.4018/IJDST.2016010101
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Published: Jan 1, 2016
Converted to Gold OA:
DOI: 10.4018/IJDST.2016010102
Volume 7
Meisong Wang, Charith Perera, Prem Prakash Jayaraman, Miranda Zhang, Peter Strazdins, R.K. Shyamsundar, Rajiv Ranjan
Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion...
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Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors' main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.
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Wang, Meisong, et al. "City Data Fusion: Sensor Data Fusion in the Internet of Things." IJDST vol.7, no.1 2016: pp.15-36. http://doi.org/10.4018/IJDST.2016010102
APA
Wang, M., Perera, C., Jayaraman, P. P., Zhang, M., Strazdins, P., Shyamsundar, R., & Ranjan, R. (2016). City Data Fusion: Sensor Data Fusion in the Internet of Things. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 15-36. http://doi.org/10.4018/IJDST.2016010102
Chicago
Wang, Meisong, et al. "City Data Fusion: Sensor Data Fusion in the Internet of Things," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 15-36. http://doi.org/10.4018/IJDST.2016010102
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Published: Jan 1, 2016
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DOI: 10.4018/IJDST.2016010103
Volume 7
Laura Belli, Simone Cirani, Luca Davoli, Gianluigi Ferrari, Lorenzo Melegari, Marco Picone
The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained...
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The Internet of Things (IoT) is expected to interconnect billions (around 50 by 2020) of heterogeneous sensor/actuator-equipped devices denoted as “Smart Objects” (SOs), characterized by constrained resources in terms of memory, processing, and communication reliability. Several IoT applications have real-time and low-latency requirements and must rely on architectures specifically designed to manage gigantic streams of information (in terms of number of data sources and transmission data rate). We refer to “Big Stream” as the paradigm which best fits the selected IoT scenario, in contrast to the traditional “Big Data” concept, which does not consider real-time constraints. Moreover, there are many security concerns related to IoT devices and to the Cloud. In this paper, we analyze security aspects in a novel Cloud architecture for Big Stream applications, which efficiently handles Big Stream data through a Graph-based platform and delivers processed data to consumers, with low latency. The authors detail each module defined in the system architecture, describing all refinements required to make the platform able to secure large data streams. An experimentation is also conducted in order to evaluate the performance of the proposed architecture when integrating security mechanisms.
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Belli, Laura, et al. "Applying Security to a Big Stream Cloud Architecture for the Internet of Things." IJDST vol.7, no.1 2016: pp.37-58. http://doi.org/10.4018/IJDST.2016010103
APA
Belli, L., Cirani, S., Davoli, L., Ferrari, G., Melegari, L., & Picone, M. (2016). Applying Security to a Big Stream Cloud Architecture for the Internet of Things. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 37-58. http://doi.org/10.4018/IJDST.2016010103
Chicago
Belli, Laura, et al. "Applying Security to a Big Stream Cloud Architecture for the Internet of Things," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 37-58. http://doi.org/10.4018/IJDST.2016010103
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Published: Jan 1, 2016
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DOI: 10.4018/IJDST.2016010104
Volume 7
Burak Kantarci, Kevin G. Carr, Connor D. Pearsall
With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides...
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With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides revealing information about social interactions between individuals, MSNs can assist smart city applications through crowdsensing services. In presence of malicious users who aim at misinformation through manipulation of their sensing data, trustworthiness arises as a crucial issue for the users who receive service from smart city applications. In this paper, the authors propose a new crowdsensing framework, namely Social Network Assisted Trustworthiness Assurance (SONATA) which aims at maximizing crowdsensing platform utility and minimizing the manipulation probability through vote-based trustworthiness analysis in dynamic social network architecture. SONATA adopts existing Sybil detection techniques to identify malicious users who aim at misinformation/disinformation at the crowdsensing platform. The authors present performance evaluation of SONATA under various crowdsensing scenarios in a smart city setting. Performance results show that SONATA improves crowdsensing utility under light and moderate arrival rates of sensing task requests when less than 7% of the users are malicious whereas crowdsensing utility is significantly improved under all task arrival rates if the ratio of malicious users to the entire population is at least 7%. Furthermore, under each scenario, manipulation ratio is close to zero under SONATA while trustworthiness unaware recruitment of social network users leads to a manipulation probability of 2.5% which cannot be tolerated in critical smart city applications such as disaster management or public safety.
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Kantarci, Burak, et al. "SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing." IJDST vol.7, no.1 2016: pp.59-78. http://doi.org/10.4018/IJDST.2016010104
APA
Kantarci, B., Carr, K. G., & Pearsall, C. D. (2016). SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 59-78. http://doi.org/10.4018/IJDST.2016010104
Chicago
Kantarci, Burak, Kevin G. Carr, and Connor D. Pearsall. "SONATA: Social Network Assisted Trustworthiness Assurance in Smart City Crowdsensing," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 59-78. http://doi.org/10.4018/IJDST.2016010104
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Published: Jan 1, 2016
Converted to Gold OA:
DOI: 10.4018/IJDST.2016010105
Volume 7
Dhavalkumar Thakker, Fan Yang-Turner, Dimoklis Despotakis
It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals...
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It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.
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Thakker, Dhavalkumar, et al. "User Interaction with Linked Data: An Exploratory Search Approach." IJDST vol.7, no.1 2016: pp.79-91. http://doi.org/10.4018/IJDST.2016010105
APA
Thakker, D., Yang-Turner, F., & Despotakis, D. (2016). User Interaction with Linked Data: An Exploratory Search Approach. International Journal of Distributed Systems and Technologies (IJDST), 7(1), 79-91. http://doi.org/10.4018/IJDST.2016010105
Chicago
Thakker, Dhavalkumar, Fan Yang-Turner, and Dimoklis Despotakis. "User Interaction with Linked Data: An Exploratory Search Approach," International Journal of Distributed Systems and Technologies (IJDST) 7, no.1: 79-91. http://doi.org/10.4018/IJDST.2016010105
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