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Future Internet, Volume 10, Issue 7 (July 2018)

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Cover Story (view full-size image) In the content delivery system, proxies receive web requests from clients and forward to redundant [...] Read more.
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Open AccessArticle Network Measurement and Performance Analysis at Server Side
Future Internet 2018, 10(7), 67; https://doi.org/10.3390/fi10070067
Received: 14 June 2018 / Revised: 13 July 2018 / Accepted: 13 July 2018 / Published: 16 July 2018
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Abstract
Network performance diagnostics is an important topic that has been studied since the Internet was invented. However, it remains a challenging task, while the network evolves and becomes more and more complicated over time. One of the main challenges is that all network
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Network performance diagnostics is an important topic that has been studied since the Internet was invented. However, it remains a challenging task, while the network evolves and becomes more and more complicated over time. One of the main challenges is that all network components (e.g., senders, receivers, and relay nodes) make decision based only on local information and they are all likely to be performance bottlenecks. Although Software Defined Networking (SDN) proposes to embrace a centralize network intelligence for a better control, the cost to collect complete network states in packet level is not affordable in terms of collection latency, bandwidth, and processing power. With the emergence of the new types of networks (e.g., Internet of Everything, Mission-Critical Control, data-intensive mobile apps, etc.), the network demands are getting more diverse. It is critical to provide finer granularity and real-time diagnostics to serve various demands. In this paper, we present EVA, a network performance analysis tool that guides developers and network operators to fix problems in a timely manner. EVA passively collects packet traces near the server (hypervisor, NIC, or top-of-rack switch), and pinpoints the location of the performance bottleneck (sender, network, or receiver). EVA works without detailed knowledge of application or network stack and is therefore easy to deploy. We use three types of real-world network datasets and perform trace-driven experiments to demonstrate EVA’s accuracy and generality. We also present the problems observed in these datasets by applying EVA. Full article
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Open AccessArticle Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks
Future Internet 2018, 10(7), 66; https://doi.org/10.3390/fi10070066
Received: 9 June 2018 / Revised: 5 July 2018 / Accepted: 9 July 2018 / Published: 11 July 2018
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Abstract
Device-to-device (D2D) communication is considered as one of the key enabling technologies for fifth-generation (5G) networks as it allows data offloading generated by the huge number of connected devices. In this respect, group-oriented services are among the most interesting usage scenarios. Indeed, D2D
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Device-to-device (D2D) communication is considered as one of the key enabling technologies for fifth-generation (5G) networks as it allows data offloading generated by the huge number of connected devices. In this respect, group-oriented services are among the most interesting usage scenarios. Indeed, D2D can improve the performance of the conventional multicast scheme (CMS) in cellular networks, which is known to suffer from low spectral efficiency. Security is a further key field of investigation for 5G systems, as any threat to privacy and security may lead to both deteriorated user experience and inefficient network resources’ utilization. Security issues are even more in focus for D2D connections between devices that are in mutual proximity. To improve the CMS performance and also sustain security requirements of the 5G network, this work proposes a secure D2D data transmission algorithm. Making use of mechanisms such as encryption and signature, this algorithm aims to protect the exchanged data and the privacy of the devices involved in the communication. A simulation campaign conducted using MATLAB shows the ability of the proposed solution to take advantage of the establishment of secure D2D communications and efficiently utilize network resources. Full article
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Open AccessArticle Performance Analysis of Hybrid Optical–Acoustic AUV Swarms for Marine Monitoring
Future Internet 2018, 10(7), 65; https://doi.org/10.3390/fi10070065
Received: 30 May 2018 / Revised: 6 July 2018 / Accepted: 7 July 2018 / Published: 10 July 2018
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Abstract
Autonomous Underwater Vehicles (AUVs) are assuming an important role in the monitoring and mapping of marine ecosystems, especially for their ability to explore harsh environments. AUV swarm can collect data operating autonomously for long periods enabling new applications in this field. However, the
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Autonomous Underwater Vehicles (AUVs) are assuming an important role in the monitoring and mapping of marine ecosystems, especially for their ability to explore harsh environments. AUV swarm can collect data operating autonomously for long periods enabling new applications in this field. However, the mission duration is usually limited also by the high power consumption required for acoustic transmissions. A new generation of devices complements the acoustic modem with an optical modem that can provide a communication channel with higher capacity and lower power consumption with respect to the acoustic channel. However, the optical link that uses the visible light is very sensitive to the water turbidity that can strongly limit the link coverage. In this paper, we evaluate the networking performances of the Venus vessel, a real AUV prototype equipped with an acoustical modem and an optical modem. The presented analysis aims to evaluate key system parameters allowing to select the best way to set up network communications according to the surrounding conditions (e.g., quality of water) and to the application requirements. Simulation results account for the case of ports or basins, where the water quality is poor and the use of the optical modem is strongly limited by distance. We evaluate system performance in terms of transmission delay in the network and we also provide a power–capacity trade-off. Full article
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Open AccessArticle Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
Future Internet 2018, 10(7), 64; https://doi.org/10.3390/fi10070064
Received: 20 June 2018 / Revised: 4 July 2018 / Accepted: 7 July 2018 / Published: 9 July 2018
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Abstract
Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link,
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Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers. Full article
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Open AccessArticle Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
Future Internet 2018, 10(7), 63; https://doi.org/10.3390/fi10070063
Received: 21 May 2018 / Revised: 28 June 2018 / Accepted: 4 July 2018 / Published: 7 July 2018
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Abstract
In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators’ subjective Quality of Experience (QoE). The human-centered QoE determination commonly requires human subject experimentation, which we combine with Electroencephalography (EEG) measurements to move towards automatized and
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In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators’ subjective Quality of Experience (QoE). The human-centered QoE determination commonly requires human subject experimentation, which we combine with Electroencephalography (EEG) measurements to move towards automatized and generalized possibilities of determining the QoE. We evaluate the prediction performance for spherical/immersive images displayed with a mobile device VR viewer (Spherical Virtual Reality (SVR)) with the help of only four-position EEG data gathered at the forehead, which correlates well with practical applicability. We find that QoS levels can be predicted more reliably (directly with R2=0.68 or based on profiles with R2=0.9) than the QoE, which exhibits significant error levels. Additional comparison with previous approaches for the Spherical Augmented Reality (SAR) QoE indicates better predictability in AR scenarios over VR. Full article
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Open AccessArticle The GDPR beyond Privacy: Data-Driven Challenges for Social Scientists, Legislators and Policy-Makers
Future Internet 2018, 10(7), 62; https://doi.org/10.3390/fi10070062
Received: 8 April 2018 / Revised: 11 June 2018 / Accepted: 4 July 2018 / Published: 6 July 2018
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Abstract
While securing personal data from privacy violations, the new General Data Protection Regulation (GDPR) explicitly challenges policymakers to exploit evidence from social data-mining in order to build better policies. Against this backdrop, two issues become relevant: the impact of Big Data on social
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While securing personal data from privacy violations, the new General Data Protection Regulation (GDPR) explicitly challenges policymakers to exploit evidence from social data-mining in order to build better policies. Against this backdrop, two issues become relevant: the impact of Big Data on social research, and the potential intersection between social data mining, rulemaking and policy modelling. The work aims at contributing to the reflection on some of the implications of the ‘knowledge-based’ policy recommended by the GDPR. The paper is thus split into two parts: the first describes the data-driven evolution of social sciences, raising methodological and epistemological issues; the second focuses on the interplay between data-driven social research, rule-making and policy modelling, in the light of the policy model fostered by GDPR. Some theoretical reflections about the role of evidence in rule-making will be considered to introduce a discussion on the intersection between data-driven social research and policy modelling and to sketch hypotheses on its future evolutions. Full article
Open AccessArticle Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach
Future Internet 2018, 10(7), 61; https://doi.org/10.3390/fi10070061
Received: 21 May 2018 / Revised: 6 June 2018 / Accepted: 19 June 2018 / Published: 1 July 2018
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Abstract
The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving
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The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving the subway schedule chosen by passengers and windows of service time, etc. Moreover, synchronous transfer between the shuttle and feeder bus is fully accounted for in the problem. A mixed-integer linear programming model is formulated to minimize the total travel time for all passengers, which consists of ride-time for vehicles from the pickup locations to the rail station and wait-time for passengers taking the subway beforehand. Different from conventional methods, the proposed model benefits from using a real distribution of passenger demand aggregated from cellular data and travel time or the distance matrix obtained from an open GIS tool. A distributed genetic algorithm is further designed to obtain meta-optimal solutions in a reasonable amount of time. When applied to design a feeder bus system in Nanjing City, China, case study results reveal that the total travel time of the proposed model was reduced by 2.46% compared to the traditional model. Sensitivity analyses were also further performed to investigate the impact of the number of vehicles on the output. Finally, the difference in results of Cplex, standard GA, and the proposed algorithm were compared to prove the validity of the algorithm. Full article
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Open AccessArticle A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay
Future Internet 2018, 10(7), 60; https://doi.org/10.3390/fi10070060
Received: 28 March 2018 / Revised: 24 May 2018 / Accepted: 22 June 2018 / Published: 1 July 2018
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Abstract
Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical
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Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay. Full article
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Open AccessArticle Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems
Future Internet 2018, 10(7), 59; https://doi.org/10.3390/fi10070059
Received: 10 May 2018 / Revised: 18 June 2018 / Accepted: 24 June 2018 / Published: 26 June 2018
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Abstract
We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode
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We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode group delay spread in multi-mode fibers under different coupling regimes are investigated. Results show that although the clock tone vanishing has a known solution for single-mode systems, in multi-mode systems even for low group delay spread, strong coupling will cause clock tone extinction, making it harder to implement an effective clock recovery scheme. Full article
(This article belongs to the Special Issue Recent Advances in DSP-Based Optical Communications)
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Open AccessArticle Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
Future Internet 2018, 10(7), 58; https://doi.org/10.3390/fi10070058
Received: 22 May 2018 / Revised: 12 June 2018 / Accepted: 21 June 2018 / Published: 25 June 2018
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Abstract
A federation of heterogeneous testbeds, which provides a wide range of services, attracts many experimenters from academia and industry to evaluate novel future Internet architectures and network protocols. The candidate experimenter reserves the appropriate testbeds’ resources based on various diverse criteria. Since several
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A federation of heterogeneous testbeds, which provides a wide range of services, attracts many experimenters from academia and industry to evaluate novel future Internet architectures and network protocols. The candidate experimenter reserves the appropriate testbeds’ resources based on various diverse criteria. Since several testbeds offer similar resources, a trust mechanism between the users and the providers will facilitate the proper selection of testbeds. This paper proposes a fuzzy reputation-based trust framework that is based on a modification of the fuzzy VIKOR multi-criteria decision making method and combines the user’s opinion from previously-conducted experiments with retrieved monitoring data from the utilized testbeds, in order to quantify the reputation of each testbed and the credibility of the experimenter. The proposed framework can process various types of numeric and linguistic data in an on-line fashion and can be easily extended for new types of testbeds and services. Data from active federated testbeds are used to evaluate the performance of the fuzzy reputation-based trust framework under dynamic conditions. Furthermore, a comparison of the proposed framework with another existing state of the art trust framework for federated testbeds is presented, and its superiority is demonstrated. Full article
(This article belongs to the Special Issue 10th Anniversary Feature Papers)
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Open AccessArticle Dynamic Cost-Aware Routing of Web Requests
Future Internet 2018, 10(7), 57; https://doi.org/10.3390/fi10070057
Received: 29 May 2018 / Revised: 15 June 2018 / Accepted: 18 June 2018 / Published: 21 June 2018
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Abstract
Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of
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Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of traffic generated by new applications, e.g., the Internet of Things. To support the massive traffic generated by the enormous user base and number of devices with reliability and high quality, web services run from redundant servers. As new servers need to be regularly deployed at different geographical locations, energy costs have become a source of major concern for operators. We propose a cost aware method for routing web requests across replicated and distributed servers that can exploit the spatial and temporal variations of both electricity prices and the server network. The method relies on a learning automaton that makes per-request decisions, which can be computed much faster than regular global optimization methods. Using simulation and testbed measurements, we show the cost reductions that are achievable with minimal impact on performance compared to standard web routing algorithms. Full article
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Open AccessArticle Big Data Perspective and Challenges in Next Generation Networks
Future Internet 2018, 10(7), 56; https://doi.org/10.3390/fi10070056
Received: 28 March 2018 / Revised: 19 May 2018 / Accepted: 27 May 2018 / Published: 21 June 2018
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Abstract
With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency
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With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks. Full article
(This article belongs to the Section Big Data and Augmented Intelligence)
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