| Nome |
# |
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Detailed Analysis of Skype Traffic, file e384c42d-fd9b-d4b2-e053-9f05fe0a1d67
|
2.179
|
|
KISS: Stochastic Packet Inspection Classifier for UDP Traffic, file e384c42e-103b-d4b2-e053-9f05fe0a1d67
|
1.978
|
|
Experiences of Internet Traffic Monitoring with Tstat, file e384c42e-17da-d4b2-e053-9f05fe0a1d67
|
1.685
|
|
Characterization of ISP Traffic: Trends, User Habits, and Access Technology Impact, file e384c42e-164a-d4b2-e053-9f05fe0a1d67
|
1.507
|
|
Live Traffic Monitoring with Tstat: Capabilities and Experiences, file e384c42e-17ff-d4b2-e053-9f05fe0a1d67
|
918
|
|
Measurement of IPTV traffic from an Operative Network, file e384c42e-056d-d4b2-e053-9f05fe0a1d67
|
771
|
|
Self-Chord: a Bio-Inspired P2P Framework for Self-Organizing Distributed Systems, file e384c42e-1039-d4b2-e053-9f05fe0a1d67
|
759
|
|
P2P-TV systems under adverse network conditions: a measurement study, file e384c42d-fda7-d4b2-e053-9f05fe0a1d67
|
754
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|
Investigating Overlay Topologies and Dynamics of P2P-TV Systems: The Case of SopCast, file e384c42e-154a-d4b2-e053-9f05fe0a1d67
|
676
|
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Energy Profiling of ISP Points of Presence, file e384c42e-1994-d4b2-e053-9f05fe0a1d67
|
642
|
|
Two Schemes to Reduce Latency in Short Lived TCP Flows, file e384c42e-0465-d4b2-e053-9f05fe0a1d67
|
636
|
|
TCP smart framing: a segmentation algorithm to reduce TCP latency, file e384c42d-e5c4-d4b2-e053-9f05fe0a1d67
|
628
|
|
Impact of adverse network conditions on P2P-TV systems: Experimental evidence, file e384c42e-14e6-d4b2-e053-9f05fe0a1d67
|
623
|
|
Efficient Uplink Bandwidth Utilization in P2P-TV Streaming Systems, file e384c42e-0d3b-d4b2-e053-9f05fe0a1d67
|
596
|
|
Modeling sleep modes gains with random graphs, file e384c42e-14e7-d4b2-e053-9f05fe0a1d67
|
574
|
|
Stochastic Packet Inspection for TCP Traffic1, file e384c42e-0e5b-d4b2-e053-9f05fe0a1d67
|
561
|
|
Passive characterization of sopcast usage in residential ISPs, file e384c42e-154b-d4b2-e053-9f05fe0a1d67
|
561
|
|
Passive analysis of TCP anomalies, file e384c42e-0228-d4b2-e053-9f05fe0a1d67
|
555
|
|
Modeling sleep mode gains in energy-aware networks, file e384c42e-2969-d4b2-e053-9f05fe0a1d67
|
450
|
|
On the intertwining between capacity scaling and TCP congestion control, file e384c42e-1992-d4b2-e053-9f05fe0a1d67
|
449
|
|
Evidences Behind Skype Outage, file e384c42e-06a2-d4b2-e053-9f05fe0a1d67
|
442
|
|
KISS: Stochastic Packet Inspection Classifier for UDP Traffic, file e384c42e-103a-d4b2-e053-9f05fe0a1d67
|
421
|
|
Estimating packet loss rate in the access through application-level measurements, file e384c42e-1eb9-d4b2-e053-9f05fe0a1d67
|
414
|
|
QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff, file e384c42e-103c-d4b2-e053-9f05fe0a1d67
|
406
|
|
Understanding Skype Signaling, file e384c42e-16a7-d4b2-e053-9f05fe0a1d67
|
400
|
|
Chunk Distribution in Mesh-Based Large-Scale P2P Streaming Systems: A Fluid Approach, file e384c42e-056a-d4b2-e053-9f05fe0a1d67
|
372
|
|
Comparison of energy efficiency in PSTN and VoIP systems, file e384c42e-1991-d4b2-e053-9f05fe0a1d67
|
371
|
|
Exploiting Heterogeneity in P2P Video Streaming, file e384c42e-0838-d4b2-e053-9f05fe0a1d67
|
325
|
|
Strengthening measurements from the edges: application-level packet loss rate estimation, file e384c42e-2c03-d4b2-e053-9f05fe0a1d67
|
311
|
|
Network awareness in P2P-TV applications, file e384c42e-11f7-d4b2-e053-9f05fe0a1d67
|
306
|
|
A delay-based aggregate rate control for P2P streaming systems, file e384c42e-260f-d4b2-e053-9f05fe0a1d67
|
302
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|
Hose rate control for P2P-TV streaming systems, file e384c42e-154f-d4b2-e053-9f05fe0a1d67
|
284
|
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Network interface power management and TCP congestion control: a troubled marriage, file e384c42f-0461-d4b2-e053-9f05fe0a1d67
|
229
|
|
An abacus for P2P-TV traffic classification, file e384c42e-06a1-d4b2-e053-9f05fe0a1d67
|
206
|
|
A comparative study of RTC applications, file e384c432-a167-d4b2-e053-9f05fe0a1d67
|
148
|
|
Greener RAN operation through machine learning, file e384c431-5baf-d4b2-e053-9f05fe0a1d67
|
135
|
|
Online Classification of RTC Traffic, file e384c433-0f15-d4b2-e053-9f05fe0a1d67
|
126
|
|
Power Management and TCP Congestion Control: Friends or Foes?, file e384c42e-2972-d4b2-e053-9f05fe0a1d67
|
123
|
|
Energy consumption for data distribution in content delivery networks, file e384c42f-0e7c-d4b2-e053-9f05fe0a1d67
|
103
|
|
Modeling the interaction between TCP and Rate Adaptation, file e384c42e-2967-d4b2-e053-9f05fe0a1d67
|
100
|
|
Designing Resource-on-Demand Strategies for Dense WLANs, file e384c42e-fb76-d4b2-e053-9f05fe0a1d67
|
87
|
|
Impact of Charging Infrastructure and Policies on Electric Car Sharing Systems, file e384c432-c427-d4b2-e053-9f05fe0a1d67
|
85
|
|
What's my App?: ML-based classification of RTC applications, file e384c433-d825-d4b2-e053-9f05fe0a1d67
|
43
|
|
Dimensioning Renewable Energy Systems to Power Mobile Networks, file e384c432-8fc6-d4b2-e053-9f05fe0a1d67
|
35
|
|
Coping with power outages in mobile networks, file e384c432-83dd-d4b2-e053-9f05fe0a1d67
|
33
|
|
Processing ANN Traffic Predictions for RAN Energy Efficiency, file e384c432-92a2-d4b2-e053-9f05fe0a1d67
|
31
|
|
Hybrid Energy Production Analysis and Modelling for Radio Access Network Supply, file e384c433-859f-d4b2-e053-9f05fe0a1d67
|
25
|
|
Advanced Sleep Modes to comply with delay constraints in energy efficient 5G networks, file e384c434-2eca-d4b2-e053-9f05fe0a1d67
|
25
|
|
Caching at the edge in high energy-efficient wireless access networks, file e384c432-6513-d4b2-e053-9f05fe0a1d67
|
24
|
|
Queueing systems to study the energy consumption of a campus WLAN, file e384c42e-2fcf-d4b2-e053-9f05fe0a1d67
|
22
|
|
Accounting for the Varying Supply of Solar Energy when Designing Wireless Access Networks, file e384c42f-b595-d4b2-e053-9f05fe0a1d67
|
20
|
|
On the Use of Small Solar Panels and Small Batteries to Reduce the RAN Carbon Footprint, file e384c432-ab61-d4b2-e053-9f05fe0a1d67
|
20
|
|
Modelling Solar Powered UAV-BS for 5G and Beyond, file e384c433-ae71-d4b2-e053-9f05fe0a1d67
|
20
|
|
Small Solar Panels Can Drastically Reduce the Carbon Footprint of Radio Access Networks, file e384c432-d990-d4b2-e053-9f05fe0a1d67
|
18
|
|
The Impact of Quantization on the Design of Solar Power Systems for Cellular Base Stations, file e384c42f-b2a0-d4b2-e053-9f05fe0a1d67
|
15
|
|
A Novel Energy Model for Renewable Energy-Enabled Cellular Networks Providing Ancillary Services to the Smart Grid, file e384c432-8fc5-d4b2-e053-9f05fe0a1d67
|
15
|
|
Enhancing fairness for short-lived TCP flows in 802.11b WLANs, file e384c42d-e5c5-d4b2-e053-9f05fe0a1d67
|
13
|
|
Load Management with Predictions of Solar Energy Production for Cloud Data Centers, file e384c432-ab62-d4b2-e053-9f05fe0a1d67
|
13
|
|
Can High Altitude Platforms Make 6G Sustainable?, file e384c434-6a7b-d4b2-e053-9f05fe0a1d67
|
13
|
|
Household users cooperation to reduce cost in green mobile networks, file e384c432-bf0c-d4b2-e053-9f05fe0a1d67
|
10
|
|
From self-sustainable Green Mobile Networks to enhanced interaction with the Smart Grid, file e384c430-2720-d4b2-e053-9f05fe0a1d67
|
8
|
|
An approximate model for the computation of blocking probabilities in cellular networks with repeated calls, file e384c430-65a5-d4b2-e053-9f05fe0a1d67
|
8
|
|
Reducing the operational cost of cloud data centers through renewable energy, file e384c432-c1b7-d4b2-e053-9f05fe0a1d67
|
8
|
|
Caching in the Air: High Altitude Platform Stations for Urban Environments, file e384c434-9ff8-d4b2-e053-9f05fe0a1d67
|
7
|
|
Media Access Schemes for Indirect Diffused Free-Space Optical Networks, file e384c433-3a20-d4b2-e053-9f05fe0a1d67
|
6
|
|
A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting, file e384c434-d835-d4b2-e053-9f05fe0a1d67
|
6
|
|
Sustainability, a Key Issue of 5G Network Ecosystem, file e384c432-e784-d4b2-e053-9f05fe0a1d67
|
5
|
|
Media Access Schemes for Indirect Diffused Free-Space Optical Networks, file e384c432-f601-d4b2-e053-9f05fe0a1d67
|
5
|
|
Real-Time Classification of Real-Time Communications, file e69d1dcf-1a7d-4f84-b364-9acca83c3c3a
|
5
|
|
Dynamic Resource Provisioning for Energy Efficiency in Wireless Access Networks: a Survey and an Outlook, file e384c42e-2f75-d4b2-e053-9f05fe0a1d67
|
4
|
|
Evaluation of Flying Caching Servers in {UAV}-{BS} based realistic environment, file e384c434-0082-d4b2-e053-9f05fe0a1d67
|
4
|
|
Designing a hybrid renewable energy source system to feed the wireless access network, file e384c434-7c6e-d4b2-e053-9f05fe0a1d67
|
4
|
|
A collaborative caching over PLC for remote areas, file f18814c1-d03e-44e3-9503-b3e85e579895
|
4
|
|
ReCoCo: Reinforcement learning-based Congestion control for Real-time applications, file 48b64ee9-d085-420e-8e96-7943fee5b8be
|
3
|
|
High Altitude Platform Stations: the New Network Energy Efficiency Enabler in the 6G Era, file 8479e420-68a3-474e-ba80-3a19a11f5047
|
3
|
|
Network Sharing for sustainability and resilience in the era of 5G and beyond, file 9e9c4967-7030-4374-b270-90c891c5ad94
|
3
|
|
Trading off delay and energy saving through Advanced Sleep Modes in 5G RANs, file b8e092c8-71e6-4f86-8e23-56be43169dc6
|
3
|
|
Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks, file bcc1045c-c35c-4c87-b9ea-c0b2150f85f2
|
3
|
|
Traffic Anomaly Detection Using Deep Semi-Supervised Learning at the Mobile Edge, file cd10e452-ab0a-4bba-8996-1c01590695b6
|
3
|
|
Energy Efficient Wireless Internet Access with Cooperative Cellular Networks, file e384c42e-0cab-d4b2-e053-9f05fe0a1d67
|
3
|
|
Greener RAN operation through machine learning, file e384c431-492c-d4b2-e053-9f05fe0a1d67
|
3
|
|
Renewable energy-enabled wireless networks, file e384c432-cbe5-d4b2-e053-9f05fe0a1d67
|
3
|
|
Retina: An open-source tool for flexible analysis of RTC traffic, file e384c434-0ffb-d4b2-e053-9f05fe0a1d67
|
3
|
|
A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting, file e384c434-8079-d4b2-e053-9f05fe0a1d67
|
3
|
|
Renewable powered Battery Swapping Stations for sustainable urban mobility, file 00bb4569-48d0-4afd-ab7b-be0e063f1411
|
2
|
|
Cost saving and ancillary service provisioning in green Mobile Networks, file 081240cd-a35f-4bb2-a097-3740cb3455cd
|
2
|
|
Machine learning empowered computer networks, file c46751db-8390-4b40-839c-77f4e0aacff3
|
2
|
|
Integrating Aerial Base Stations for sustainable urban mobile networks, file da69b0c2-0572-46ad-b974-99d7d78f9658
|
2
|
|
Autonomic Interface Selection for Mobile Wireless Users, file e384c42d-ff04-d4b2-e053-9f05fe0a1d67
|
2
|
|
Radio Resource Management for Improving Energy Self-sufficiency of Green Mobile Networks, file e384c42f-7474-d4b2-e053-9f05fe0a1d67
|
2
|
|
WiFi offloading for enhanced interaction with the Smart Grid in green mobile networks, file e384c42f-8626-d4b2-e053-9f05fe0a1d67
|
2
|
|
Sustainability, a Key Issue of 5G Network Ecosystem, file e384c432-a22a-d4b2-e053-9f05fe0a1d67
|
2
|
|
A comparative study of RTC applications, file e384c432-c38a-d4b2-e053-9f05fe0a1d67
|
2
|
|
Sustainability, a Key Issue of 5G Network Ecosystem, file e384c432-c4ac-d4b2-e053-9f05fe0a1d67
|
2
|
|
RAN energy efficiency and failure rate through ANN traffic predictions processing, file e384c434-3d8b-d4b2-e053-9f05fe0a1d67
|
2
|
|
RAN energy efficiency and failure rate through ANN traffic predictions processing, file e384c434-5ca9-d4b2-e053-9f05fe0a1d67
|
2
|
|
Guest Editorial: Special Issue on Energy Efficiency for Internet of Things, file e384c434-e6bb-d4b2-e053-9f05fe0a1d67
|
2
|
|
For a Sustainable Future of Communications and Networking, file 0517b3ef-8f5e-46ce-9a82-66b09603e2d2
|
1
|
|
Energy Efficient Management of two Cellular Access Networks, file 1a0bace8-18fa-4423-993f-31415ec98277
|
1
|
|
ReCoCo: Reinforcement learning-based Congestion control for Real-time applications, file 3ea84c74-7247-40bd-aca5-8cc6bf93b0af
|
1
|
| Totale |
23.729 |
Archivio Istituzionale della Ricerca