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Future Internet, Volume 8, Issue 1 (March 2016) – 7 articles

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21822 KiB  
Article
Analyzing the Bitcoin Network: The First Four Years
by Matthias Lischke and Benjamin Fabian
Future Internet 2016, 8(1), 7; https://doi.org/10.3390/fi8010007 - 7 Mar 2016
Cited by 155 | Viewed by 23288
Abstract
In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, [...] Read more.
In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network. Full article
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Article
MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
by Tinghuai Ma, Xichao Xu, Meili Tang, Yuanfeng Jin and Wenhai Shen
Future Internet 2016, 8(1), 6; https://doi.org/10.3390/fi8010006 - 1 Mar 2016
Cited by 11 | Viewed by 5631
Abstract
Meteorological technology has evolved rapidly in recent years to provide enormous, accurate and personalized advantages in the public service. Large volumes of observational data are generated gradually by technologies such as geographical remote sensing, meteorological radar satellite, etc. that makes data analysis in [...] Read more.
Meteorological technology has evolved rapidly in recent years to provide enormous, accurate and personalized advantages in the public service. Large volumes of observational data are generated gradually by technologies such as geographical remote sensing, meteorological radar satellite, etc. that makes data analysis in weather forecasting more precise but also poses a threat to the traditional method of data storage. In this paper, we present MHBase, (Meteorological data based on HBase (Hadoop Database), a distributed real-time query scheme for meteorological data based on HBase. The calibrated data obtained from terminal devices will be partitioned into HBase and persisted to HDFS (the Hadoop Distributed File System). We propose two algorithms (the Indexed Store and the Indexed Retrieve Algorithms) to implement a secondary index using HBase Coprocessors, which allow MHbase to provide high performance data querying on columns other than rowkey. Experimental results show that the performance of MHBase can satisfy the basic demands of meteorological business services. Full article
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1585 KiB  
Article
A Framework for Security Transparency in Cloud Computing
by Umar Mukhtar Ismail, Shareeful Islam, Moussa Ouedraogo and Edgar Weippl
Future Internet 2016, 8(1), 5; https://doi.org/10.3390/fi8010005 - 17 Feb 2016
Cited by 18 | Viewed by 10067
Abstract
Individuals and corporate users are persistently considering cloud adoption due to its significant benefits compared to traditional computing environments. The data and applications in the cloud are stored in an environment that is separated, managed and maintained externally to the organisation. Therefore, it [...] Read more.
Individuals and corporate users are persistently considering cloud adoption due to its significant benefits compared to traditional computing environments. The data and applications in the cloud are stored in an environment that is separated, managed and maintained externally to the organisation. Therefore, it is essential for cloud providers to demonstrate and implement adequate security practices to protect the data and processes put under their stewardship. Security transparency in the cloud is likely to become the core theme that underpins the systematic disclosure of security designs and practices that enhance customer confidence in using cloud service and deployment models. In this paper, we present a framework that enables a detailed analysis of security transparency for cloud based systems. In particular, we consider security transparency from three different levels of abstraction, i.e., conceptual, organisation and technical levels, and identify the relevant concepts within these levels. This allows us to provide an elaboration of the essential concepts at the core of transparency and analyse the means for implementing them from a technical perspective. Finally, an example from a real world migration context is given to provide a solid discussion on the applicability of the proposed framework. Full article
(This article belongs to the Special Issue Security in Cloud Computing and Big Data)
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Article
Context-Based Energy Disaggregation in Smart Homes
by Francesca Paradiso, Federica Paganelli, Dino Giuli and Samuele Capobianco
Future Internet 2016, 8(1), 4; https://doi.org/10.3390/fi8010004 - 27 Jan 2016
Cited by 41 | Viewed by 8494
Abstract
In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a [...] Read more.
In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments. Full article
(This article belongs to the Special Issue Ecosystemic Evolution Feeded by Smart Systems)
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Editorial
Acknowledgement to Reviewers of Future Internet in 2015
by Future Internet Editorial Office
Future Internet 2016, 8(1), 3; https://doi.org/10.3390/fi8010003 - 22 Jan 2016
Viewed by 3181
Abstract
The editors of Future Internet would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...] Full article
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Article
Detection of Intelligent Intruders in Wireless Sensor Networks
by Yun Wang, William Chu, Sarah Fields, Colleen Heinemann and Zach Reiter
Future Internet 2016, 8(1), 2; https://doi.org/10.3390/fi8010002 - 20 Jan 2016
Cited by 12 | Viewed by 4940
Abstract
Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent [...] Read more.
Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances. Full article
(This article belongs to the Special Issue Internet of Things)
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Article
Priority Queues with Fractional Service for Tiered Delay QoS
by Gary Chang and Chung-Chieh Lee
Future Internet 2016, 8(1), 1; https://doi.org/10.3390/fi8010001 - 29 Dec 2015
Cited by 3 | Viewed by 4275
Abstract
Packet scheduling is key to quality of service (QoS) capabilities of broadband wired and wireless networks. In a heterogeneous traffic environment, a comprehensive QoS packet scheduler must strike a balance between flow fairness and access delay. Many advanced packet scheduling solutions have targeted [...] Read more.
Packet scheduling is key to quality of service (QoS) capabilities of broadband wired and wireless networks. In a heterogeneous traffic environment, a comprehensive QoS packet scheduler must strike a balance between flow fairness and access delay. Many advanced packet scheduling solutions have targeted fair bandwidth allocation while protecting delay-constrained traffic by adding priority queue(s) on top of a fair bandwidth scheduler. Priority queues are known to cause performance uncertainties and, thus, various modifications have been proposed. In this paper, we present a packet queueing engine dubbed Fractional Service Buffer (FSB), which, when coupled with a configurable flow scheduler, can achieve desired QoS objectives, such as fair throughputs and differentiated delay guarantees. Key performance metrics, such as delay limit and probability of delay limit violation, are derived as a function of key FSB parameters for each delay class in the packet queueing engine using diffusion approximations. OPNET simulations verify these analytical results. Full article
(This article belongs to the Special Issue Managing QoS and QoE Levels in Wired and Wireless Data Networks)
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