| Nome |
# |
|
A Pre-Filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems, file e1dcb335-9a65-7715-e053-1705fe0a6cc9
|
550
|
|
Hyperledger Fabric Blockchain: Chaincode Performance Analysis, file 8549d41d-0f33-431b-a925-57f1b5d7c2cc
|
461
|
|
The Need of Multidisciplinary Approaches and Engineering Tools for the Development and Implementation of the Smart City Paradigm, file e1dcb335-dc12-7715-e053-1705fe0a6cc9
|
442
|
|
Differentiated service/data migration for edge services leveraging container characteristics, file e1dcb333-fde4-7715-e053-1705fe0a6cc9
|
210
|
|
Mobile Cloud Support for Semantic-Enriched Speech Recognition in Social Care, file e1dcb335-ad27-7715-e053-1705fe0a6cc9
|
209
|
|
A survey on fog computing for the Internet of Things, file e1dcb338-77f5-7715-e053-1705fe0a6cc9
|
189
|
|
Spatial-aware approximate big data stream processing, file e1dcb335-ee14-7715-e053-1705fe0a6cc9
|
179
|
|
ParticipAct: A Large-Scale Crowdsensing Platform, file e1dcb338-5603-7715-e053-1705fe0a6cc9
|
175
|
|
Industry 4.0 Solutions for Interoperability: A Use Case about Tools and Tool Chains in the Arrowhead Tools Project, file e1dcb337-8803-7715-e053-1705fe0a6cc9
|
158
|
|
Toward Fog-Based Mobile Crowdsensing Systems: State of the Art and Opportunities, file e1dcb338-8d1b-7715-e053-1705fe0a6cc9
|
121
|
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Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments, file e1dcb334-95e7-7715-e053-1705fe0a6cc9
|
103
|
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Machine Learning for Predictive Diagnostics at the Edge: An IIoT Practical Example, file a2f5a29e-4358-4033-8eff-a316bdbc99c2
|
96
|
|
Virtual network function embedding in real cloud environments, file e1dcb338-30aa-7715-e053-1705fe0a6cc9
|
92
|
|
A Framework for TSN-enabled Virtual Environments for Ultra-Low Latency 5G Scenarios, file ff635781-c360-41a6-94ef-947302821574
|
90
|
|
Big Spatial Data Management for the Internet of Things: A Survey, file 941a1413-18c3-482b-b412-adba0171f94e
|
75
|
|
Improved Adaptation and Survivability via Dynamic Service Composition of Ubiquitous Computing Middleware, file e1dcb335-526a-7715-e053-1705fe0a6cc9
|
75
|
|
Empowering mobile crowdsensing through social and ad hoc networking, file e1dcb338-2ff2-7715-e053-1705fe0a6cc9
|
75
|
|
Cooperative vehicular traffic monitoring in realistic low penetration scenarios: The COLOMBO experience, file e1dcb331-b306-7715-e053-1705fe0a6cc9
|
71
|
|
Structured Sparse Ternary Compression for Convolutional Layers in Federated Learning, file 3b28f317-fc2b-467e-aa8b-f33ee3273bd2
|
66
|
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Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing, file e1dcb337-92d8-7715-e053-1705fe0a6cc9
|
62
|
|
Interoperable blockchains for highly-integrated supply chains in collaborative manufacturing, file e1dcb339-79fb-7715-e053-1705fe0a6cc9
|
61
|
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BDMaaS+: Business-driven and Simulation-based Optimization of IT Services in the Hybrid Cloud, file 632d0cd2-9a95-4c4a-8ad0-00b0c5df3db3
|
55
|
|
Game theory in mobile crowdsensing: A comprehensive survey, file e1dcb337-46d9-7715-e053-1705fe0a6cc9
|
55
|
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Enabling Smart Manufacturing by Empowering Data Integration with Industrial IoT Support, file 84e921a7-5bd4-4dd7-b5cf-d305abf75c79
|
50
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Meeting Stringent QoS Requirements in IIoT-based Scenarios, file 4851e849-e8eb-4236-bad9-eb1f31ade7f0
|
49
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A digital twin decision support system for the urban facility management process, file a103f4ec-1e36-49b4-b012-27329659627c
|
45
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5G-Kube: Complex Telco Core Infrastructure Deployment Made Low-Cost, file 7b7ccb0f-9eb5-46c8-8057-90bf1a3c338a
|
41
|
|
Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing, file e1dcb338-7149-7715-e053-1705fe0a6cc9
|
39
|
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Virtual Environments as Enablers of Civic Awareness and Engagement, file 709f76e3-1f3b-44b5-a966-ec0e986ea41d
|
38
|
|
The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection, file 068c9c46-e72d-4a27-b9db-b326a4dcaece
|
35
|
|
Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures, file b966364c-2b00-4113-acf6-a2ba42400c0a
|
35
|
|
A Probabilistic Model for the Deployment of Human-Enabled Edge Computing in Massive Sensing Scenarios, file 2a18f920-d67b-48aa-8855-7eb07bc83071
|
32
|
|
Edge-enabled Mobile Crowdsensing to Support Effective Rewarding for Data Collection in Pandemic Events, file 93f86ec0-eecb-4827-9f76-453090159483
|
29
|
|
An sdn‐enabled architecture for it/ot converged networks: A proposal and qualitative analysis under ddos attacks, file e1dcb339-0a79-7715-e053-1705fe0a6cc9
|
28
|
|
Understanding Human Mobility for CrowdSensing Strategies with the ParticipAct Data Set, file 7cc9b6b3-3be3-4727-abc1-cc8160ed4b4d
|
25
|
|
MSN: A Playground Framework for Design and Evaluation of MicroServices-Based sdN Controller, file e1dcb339-26fe-7715-e053-1705fe0a6cc9
|
25
|
|
An Efficient and Reliable Multi-Cloud Provider Monitoring Solution, file 4c87018b-5f28-4bd5-ad7a-21a64d3de29e
|
24
|
|
Feasibility of Commodity WiFi for Operations Control in an Autonomous Production Site, file 93c1aba5-307e-4b5e-aaf0-fa201b56ef2b
|
23
|
|
FlowChain: The Playground for Federated Learning in Industrial Internet of Things Environments, file ea14e7c3-0759-4fef-b28b-750bd4b0f05a
|
23
|
|
HS-AUTOFIT: A highly scalable AUTOFIT application for Cloud and HPC environments, file e7046499-87c5-4c92-aebe-d7fadd8b2d2c
|
22
|
|
Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS, file b370b352-5d25-42b4-a99b-7b076fdb0fa3
|
21
|
|
A New Agent-Based Intelligent Network Architecture, file 318594f3-3ad5-404f-8b62-d1be0c5d793c
|
20
|
|
Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines, file e1dcb332-32f9-7715-e053-1705fe0a6cc9
|
20
|
|
Impact of Evolutionary Community Detection Algorithms for Edge Selection Strategies, file 860716d2-7125-44fd-b365-84d3ade24d67
|
19
|
|
Qos‐aware approximate query processing for smart cities spatial data streams, file e1dcb339-2dff-7715-e053-1705fe0a6cc9
|
18
|
|
Impact of Softwarization in Microservices-based SDN Controller, file 99a877bf-de2c-4590-a641-cb032b4e1b97
|
14
|
|
KuberneTSN: a Deterministic Overlay Network for Time-Sensitive Containerized Environments, file 0fa71e0b-4faf-460a-9a43-bd2704d62b49
|
13
|
|
Decentralised Learning in Federated Deployment Environments, file 2cc0ced9-fb76-4759-9700-809024c663ea
|
13
|
|
Measuring the impact of COVID-19 restrictions on mobility: A real case study from Italy, file e1dcb339-192d-7715-e053-1705fe0a6cc9
|
13
|
|
How mobility and sociality reshape the context: A decade of experience in mobile crowdsensing, file e1dcb339-7e04-7715-e053-1705fe0a6cc9
|
12
|
|
Modeling Digital Twins of Kubernetes-Based Applications, file c05f1098-141a-4f6f-9408-e8963cbe813d
|
11
|
|
A Geo-Distributed Architectural Approach Favouring Smart Tourism Development in the 5G Era, file e1dcb337-9539-7715-e053-1705fe0a6cc9
|
11
|
|
A Framework for QoS- Enabled Semantic Routing in Industrial Networks: Overall Architecture and Primary Protocols, file c0517a82-b7cb-4595-82ec-1c0095ef513d
|
10
|
|
MQTT-based Middleware for Container Support in Fog Computing Environments, file e1dcb334-aded-7715-e053-1705fe0a6cc9
|
10
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|
Testing the scalability of the HS-AUTOFIT tool in a high-performance computing environment, file e1dcb339-6b53-7715-e053-1705fe0a6cc9
|
10
|
|
Handling Data Handoff of AI-based Applications in Edge Computing Systems, file 80addad6-a888-4df6-9e5e-4cf6dca93e79
|
9
|
|
Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data, file 9c0b45c1-32dd-43eb-9741-4ae9f5139f67
|
7
|
|
The Need of Multidisciplinary Approaches and Engineering Tools for the Development and Implementation of the Smart City Paradigm, file e1dcb331-8207-7715-e053-1705fe0a6cc9
|
7
|
|
Optimization strategies for the selection of mobile edges in hybrid crowdsensing architectures, file e1dcb335-91dd-7715-e053-1705fe0a6cc9
|
7
|
|
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities, file e1dcb337-80a1-7715-e053-1705fe0a6cc9
|
7
|
|
SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join Processor, file 13012aa0-33b4-447f-957a-e8c170cbc78f
|
6
|
|
SACHER Project. A Cloud Platform and Integrated Services for Cultural Heritage and for Restoration, file e1dcb331-d580-7715-e053-1705fe0a6cc9
|
6
|
|
INTERNATIONAL JOURNAL OF ADAPTIVE, RESILIENT AND AUTONOMIC SYSTEMS, file e1dcb32c-696c-7715-e053-1705fe0a6cc9
|
5
|
|
MQTT-Driven sustainable node discovery for internet of things-fog environments, file e9167ed1-01e2-433d-8e55-b03a9766df77
|
5
|
|
Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees, file f2c3bbd8-8e71-4222-ad66-da5f9a85e19d
|
5
|
|
The Service Node Placement Problem in Software-Defined Fog Networks, file d97abf8f-d3e5-439e-b203-827183d082ef
|
4
|
|
A Crowdsensing Campaign and Data Analytics for Assisting Urban Mobility Pattern Determination, file e1dcb332-0919-7715-e053-1705fe0a6cc9
|
4
|
|
Toward Fog-Based Mobile Crowdsensing Systems: State of the Art and Opportunities, file e1dcb334-ebe4-7715-e053-1705fe0a6cc9
|
4
|
|
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities, file e1dcb335-06be-7715-e053-1705fe0a6cc9
|
4
|
|
Mobile Cloud Support for Semantic-Enriched Speech Recognition in Social Care, file e1dcb335-ad1f-7715-e053-1705fe0a6cc9
|
4
|
|
Virtual Environments as Enablers of Civic Awareness and Engagement, file e1dcb337-90f4-7715-e053-1705fe0a6cc9
|
4
|
|
Efficient QoS-Aware Spatial Join Processing for Scalable NoSQL Storage Frameworks, file e91a964f-5c6b-4e22-81fa-735fc59f3138
|
4
|
|
Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking, file 122ad757-3cf8-4448-8f7d-131238947c8f
|
3
|
|
SIRDAM4.0: a Support Infrastructure for Reliable Data Acquisition and Management in Industry 4.0, file 821ed798-f545-48ab-b3c5-5afbe520aa8d
|
3
|
|
Supporting vPLC Networking over TSN with Kubernetes in Industry 4.0, file 9d710ed5-6d42-4332-94c4-05c924c07e91
|
3
|
|
Toward Industrial Private AI: A Two-Tier Framework for Data and Model Security, file c13aac33-922f-4f3e-b3e5-a6390cf8ae7a
|
3
|
|
null, file e1dcb331-03ab-7715-e053-1705fe0a6cc9
|
3
|
|
Improving OpenStack Networking: Advantages and Performance of Native SDN Integration, file e1dcb331-eafc-7715-e053-1705fe0a6cc9
|
3
|
|
Enabling Smart Manufacturing by Empowering Data Integration with Industrial IoT Support, file e1dcb335-b323-7715-e053-1705fe0a6cc9
|
3
|
|
The rhythm of the crowd: Properties of evolutionary community detection algorithms for mobile edge selection, file e1dcb336-f855-7715-e053-1705fe0a6cc9
|
3
|
|
The Service Node Placement Problem in Software-Defined Fog Networks, file e1dcb337-5774-7715-e053-1705fe0a6cc9
|
3
|
|
HS-AUTOFIT: A highly scalable AUTOFIT application for Cloud and HPC environments, file e1dcb337-8b43-7715-e053-1705fe0a6cc9
|
3
|
|
Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking, file e1dcb338-fdcd-7715-e053-1705fe0a6cc9
|
3
|
|
Feasibility of Commodity WiFi for Operations Control in an Autonomous Production Site, file 227b7b37-f372-41d2-a73d-09b2e1bb410e
|
2
|
|
Impact of Softwarization in Microservices-based SDN Controller, file 5c95cc4e-63fe-453f-a495-b6b2409d524f
|
2
|
|
QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios, file 5f6ba232-6624-4009-8403-b2f3a7a96d2b
|
2
|
|
An Event and Service Mesh Architecture Supporting Service Integration in Society 5.0 enabled Smart Cities, file 91a57b1e-b7f1-4662-8451-8017defa75d7
|
2
|
|
QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios, file aa0d905a-fd54-4e4b-aff0-466a5c9da37e
|
2
|
|
A New Agent-Based Intelligent Network Architecture, file b25e9e8a-eb3e-4ee7-ae40-9525232ca92c
|
2
|
|
A Multicloud Observability Support Based on ElasticSearch for Cloud-native Smart Cities Services, file cbd6c06a-c57d-407e-b426-beb870c017c3
|
2
|
|
VM consolidation: A real case based on OpenStack Cloud, file e1dcb32b-b2a2-7715-e053-1705fe0a6cc9
|
2
|
|
Design of energy-efficient cloud systems via network and resource virtualization, file e1dcb32c-e831-7715-e053-1705fe0a6cc9
|
2
|
|
Scalable and cost-effective assignment of mobile crowdsensing tasks based on profiling trends and prediction: The ParticipAct living lab experience, file e1dcb32e-286b-7715-e053-1705fe0a6cc9
|
2
|
|
ParticipAct: A Large-Scale Crowdsensing Platform, file e1dcb32e-29e8-7715-e053-1705fe0a6cc9
|
2
|
|
null, file e1dcb32e-e49a-7715-e053-1705fe0a6cc9
|
2
|
|
Empowering mobile crowdsensing through social and ad hoc networking, file e1dcb330-12cb-7715-e053-1705fe0a6cc9
|
2
|
|
The Trap Coverage Area Protocol for Scalable Vehicular Target Tracking, file e1dcb330-1c87-7715-e053-1705fe0a6cc9
|
2
|
|
Efficient spark-based framework for big geospatial data query processing and analysis, file e1dcb330-8e7f-7715-e053-1705fe0a6cc9
|
2
|
|
null, file e1dcb330-f453-7715-e053-1705fe0a6cc9
|
2
|
|
A survey on fog computing for the Internet of Things, file e1dcb332-005d-7715-e053-1705fe0a6cc9
|
2
|
| Totale |
4.607 |