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Future Internet, Volume 10, Issue 3 (March 2018)

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Cover Story (view full-size image) The figure describes the modeling process used in our research. Since we model motion profiles in a [...] Read more.
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Open AccessArticle A Statistical Analysis of Industrial Penetration and Internet Intensity in Taiwan
Future Internet 2018, 10(3), 31; https://doi.org/10.3390/fi10030031
Received: 10 January 2018 / Revised: 7 March 2018 / Accepted: 13 March 2018 / Published: 16 March 2018
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Abstract
This paper is the first to investigate the effect of industrial penetration (geographic concentration of industries) and internet intensity (the proportion of enterprises that uses the internet) for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations
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This paper is the first to investigate the effect of industrial penetration (geographic concentration of industries) and internet intensity (the proportion of enterprises that uses the internet) for Taiwan manufacturing firms, and analyses whether the relationships are substitutes or complements. The sample observations are based on a unique set of data, namely 153,081 manufacturing plants, and covers 26 two-digit industry categories and 358 geographical townships in Taiwan. The Heckman sample selection model is used to accommodate sample selectivity for unobservable data for firms that use the internet. The empirical results from Heckman’s two-stage estimation show that: (1) a higher degree of industrial penetration will not affect the probability that firms will use the internet, but it will affect the total expenditure on internet intensity; (2) for two-digit SIC (Standard Industrial Classification) industries, industrial penetration generally decreases the total expenditure on internet intensity; and, (3) industrial penetration and internet intensity are substitutes. Full article
Open AccessArticle Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction
Future Internet 2018, 10(3), 30; https://doi.org/10.3390/fi10030030
Received: 19 January 2018 / Revised: 21 February 2018 / Accepted: 8 March 2018 / Published: 12 March 2018
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Abstract
This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers’ one or several
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This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers’ one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm. Full article
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Open AccessArticle Understanding How GitHub Supports Curation Repositories
Future Internet 2018, 10(3), 29; https://doi.org/10.3390/fi10030029
Received: 4 February 2018 / Revised: 5 March 2018 / Accepted: 8 March 2018 / Published: 10 March 2018
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Abstract
In recent years, software developers have started to appropriate GitHub repositories to curate resources, in order to systematically select, evaluate, and organize existing artifacts for preservation and future use. Curation behaviors in social media sites, such as users’ experiences to curate tweets from
[...] Read more.
In recent years, software developers have started to appropriate GitHub repositories to curate resources, in order to systematically select, evaluate, and organize existing artifacts for preservation and future use. Curation behaviors in social media sites, such as users’ experiences to curate tweets from Twitter and pins on Pinterest, are well documented. However, GitHub, as a social coding platform, presents a new context for this activity, raising questions about the nature of curation on this task-driven online work site. To explore and understand curation on GitHub, we compared and contrasted curation repositories with software repositories using activity logs and analyzed the content of popular curation repositories. Our results show that: (1) curation repositories have become a favorite category of repositories in GitHub; (2) curation repositories leverage collaborative features and practices native to GitHub in new ways; (3) curation repositories collect and preserve high-quality resources for the software developers’ community. Our results suggest that curation is becoming increasingly important to the software developers’ community, and current practices can be better supported with tools designed specifically for curation. Full article
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Open AccessEditorial Ecosystemic Evolution Fed by Smart Systems
Future Internet 2018, 10(3), 28; https://doi.org/10.3390/fi10030028
Received: 5 March 2018 / Revised: 6 March 2018 / Accepted: 6 March 2018 / Published: 10 March 2018
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(This article belongs to the Special Issue Ecosystemic Evolution Feeded by Smart Systems) Printed Edition available
Open AccessArticle Security Awareness in Software-Defined Multi-Domain 5G Networks
Future Internet 2018, 10(3), 27; https://doi.org/10.3390/fi10030027
Received: 28 December 2017 / Revised: 9 February 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
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Abstract
Fifth generation (5G) technologies will boost the capacity and ease the management of mobile networks. Emerging virtualization and softwarization technologies enable more flexible customization of network services and facilitate cooperation between different actors. However, solutions are needed to enable users, operators, and service
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Fifth generation (5G) technologies will boost the capacity and ease the management of mobile networks. Emerging virtualization and softwarization technologies enable more flexible customization of network services and facilitate cooperation between different actors. However, solutions are needed to enable users, operators, and service providers to gain an up-to-date awareness of the security and trustworthiness of 5G systems. We describe a novel framework and enablers for security monitoring, inferencing, and trust measuring. The framework leverages software-defined networking and big data technologies to customize monitoring for different applications. We present an approach for sharing security measurements across administrative domains. We describe scenarios where the correlation of multi-domain information improves the accuracy of security measures with respect to two threats: end-user location tracking and Internet of things (IoT) authentication storms. We explore the security characteristics of data flows in software networks dedicated to different applications with a mobile network testbed. Full article
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Open AccessArticle TwinNet: A Double Sub-Network Framework for Detecting Universal Adversarial Perturbations
Future Internet 2018, 10(3), 26; https://doi.org/10.3390/fi10030026
Received: 27 January 2018 / Revised: 22 February 2018 / Accepted: 28 February 2018 / Published: 6 March 2018
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Abstract
Deep neural network has achieved great progress on tasks involving complex abstract concepts. However, there exist adversarial perturbations, which are imperceptible to humans, which can tremendously undermine the performance of deep neural network classifiers. Moreover, universal adversarial perturbations can even fool classifiers on
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Deep neural network has achieved great progress on tasks involving complex abstract concepts. However, there exist adversarial perturbations, which are imperceptible to humans, which can tremendously undermine the performance of deep neural network classifiers. Moreover, universal adversarial perturbations can even fool classifiers on almost all examples with just a single perturbation vector. In this paper, we propose TwinNet, a framework for neural network classifiers to detect such adversarial perturbations. TwinNet makes no modification of the protected classifier. It detects adversarially perturbated examples by enhancing different types of features in dedicated networks and fusing the output of the networks later. The paper empirically shows that our framework can identify adversarial perturbations effectively with a slight loss in accuracy when predicting normal examples, which outperforms state-of-the-art works. Full article
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Open AccessArticle Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
Future Internet 2018, 10(3), 25; https://doi.org/10.3390/fi10030025
Received: 18 January 2018 / Revised: 17 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
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Abstract
The overlay integration of low-power femtocells over macrocells in a heterogeneous network (HetNet) plays an important role in dealing with the increasing demand of spectral efficiency, coverage and higher data rates, at a nominal cost to network operators. However, the downlink (DL) transmission
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The overlay integration of low-power femtocells over macrocells in a heterogeneous network (HetNet) plays an important role in dealing with the increasing demand of spectral efficiency, coverage and higher data rates, at a nominal cost to network operators. However, the downlink (DL) transmission power of an inadequately deployed femtocell causes inter-cell interference (ICI), which leads to severe degradation and sometimes link failure for nearby macrocell users. In this paper, we propose an adaptive network sensing (ANS) technique for downlink power control to obviate the ICI. The simulation results have shown that the ANS power control technique successfully decreases the cell-edge macro user’s interference and enhances the throughput performance of macro users, while also optimizing the coverage and capacity of the femtocell. When compared with the Femto User Equipment (FUE)-assisted and Macro User Equipment (MUE)-assisted power control technique, the proposed technique offers a good tradeoff in reducing interference to macro users, while maintaining the quality of service (QoS) requirement of the femtocell users. Full article
(This article belongs to the Special Issue Massive MIMO Communication and Networking Systems)
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Open AccessArticle How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0
Future Internet 2018, 10(3), 24; https://doi.org/10.3390/fi10030024
Received: 10 December 2017 / Revised: 16 February 2018 / Accepted: 24 February 2018 / Published: 1 March 2018
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Abstract
We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The
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We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products’ quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies. Full article
(This article belongs to the Special Issue Big Data and Internet of Thing)
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Open AccessArticle SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
Future Internet 2018, 10(3), 23; https://doi.org/10.3390/fi10030023
Received: 28 December 2017 / Revised: 9 February 2018 / Accepted: 22 February 2018 / Published: 27 February 2018
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Abstract
Software Defined Networking (SDN) has proved itself to be a backbone in the new network design and is quickly becoming an industry standard. The idea of separation of control plane and data plane is the key concept behind SDN. SDN not only allows
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Software Defined Networking (SDN) has proved itself to be a backbone in the new network design and is quickly becoming an industry standard. The idea of separation of control plane and data plane is the key concept behind SDN. SDN not only allows us to program and monitor our networks but it also helps in mitigating some key network problems. Distributed denial of service (DDoS) attack is among them. In this paper we propose a collaborative DDoS attack mitigation scheme using SDN. We design a secure controller-to-controller (C-to-C) protocol that allows SDN-controllers lying in different autonomous systems (AS) to securely communicate and transfer attack information with each other. This enables efficient notification along the path of an ongoing attack and effective filtering of traffic near the source of attack, thus saving valuable time and network resources. We also introduced three different deployment approaches i.e., linear, central and mesh in our testbed. Based on the experimental results we demonstrate that our SDN based collaborative scheme is fast and reliable in efficiently mitigating DDoS attacks in real time with very small computational footprints. Full article
(This article belongs to the collection Information Systems Security)
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Open AccessArticle Learning and Mining Player Motion Profiles in Physically Interactive Robogames
Future Internet 2018, 10(3), 22; https://doi.org/10.3390/fi10030022
Received: 30 October 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 26 February 2018
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Abstract
Physically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players’ activity is relevant both to understand their engagement, as well as
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Physically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players’ activity is relevant both to understand their engagement, as well as to understand specific strategies they adopted, which in turn can foster game adaptation. Following such directions and given the lack of quantitative methods for player modeling in PIRG, we propose a methodology for representing players as a mixture of existing player’s types uncovered from data. This is done by dealing both with the intrinsic uncertainty associated with the setting and with the agent necessity to act in real time to support the game interaction. Our methodology first focuses on encoding time series data generated from player-robot interaction into images, in particular Gramian angular field images, to represent continuous data. To these, we apply latent Dirichlet allocation to summarize the player’s motion style as a probabilistic mixture of different styles discovered from data. This approach has been tested in a dataset collected from a real, physical robot game, where activity patterns are extracted by using a custom three-axis accelerometer sensor module. The obtained results suggest that the proposed system is able to provide a robust description for the player interaction. Full article
(This article belongs to the Special Issue Engaging in Interaction with Robots)
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