| Nome |
# |
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The Need of Multidisciplinary Approaches and Engineering Tools for the Development and Implementation of the Smart City Paradigm, file e1dcb335-dc12-7715-e053-1705fe0a6cc9
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356
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A Pre-Filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems, file e1dcb335-9a65-7715-e053-1705fe0a6cc9
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277
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Application-Driven Network-Aware Digital Twin Management in Industrial Edge Environments, file e1dcb33a-0094-7715-e053-1705fe0a6cc9
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180
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Differentiated service/data migration for edge services leveraging container characteristics, file e1dcb333-fde4-7715-e053-1705fe0a6cc9
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179
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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
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124
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A survey on fog computing for the Internet of Things, file e1dcb338-77f5-7715-e053-1705fe0a6cc9
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116
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IoTwins: Design and implementation of a platform for the management of digital twins in industrial scenarios, file e1dcb33a-2a76-7715-e053-1705fe0a6cc9
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88
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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
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84
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SDN-Based Traffic Management Middleware for Spontaneous WMNs, file e1dcb337-4dd9-7715-e053-1705fe0a6cc9
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79
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Quality management of surveillance multimedia streams via federated SDN controllers in Fiwi-iot integrated deployment environments, file e1dcb331-88aa-7715-e053-1705fe0a6cc9
|
71
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Improved Adaptation and Survivability via Dynamic Service Composition of Ubiquitous Computing Middleware, file e1dcb335-526a-7715-e053-1705fe0a6cc9
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66
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Hybrid 5G optical-wireless SDN-based networks, challenges and open issues, file e1dcb330-5df7-7715-e053-1705fe0a6cc9
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63
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Cooperative vehicular traffic monitoring in realistic low penetration scenarios: The COLOMBO experience, file e1dcb331-b306-7715-e053-1705fe0a6cc9
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63
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Virtual network function embedding in real cloud environments, file e1dcb338-30aa-7715-e053-1705fe0a6cc9
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63
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Complexity Problems Handled by Big Data Technology, file e1dcb332-3f99-7715-e053-1705fe0a6cc9
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62
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The PeRvasive environment sensing and sharing solution, file e1dcb332-5363-7715-e053-1705fe0a6cc9
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62
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Spatial-aware approximate big data stream processing, file e1dcb335-ee14-7715-e053-1705fe0a6cc9
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58
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Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables, file e1dcb336-72cb-7715-e053-1705fe0a6cc9
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58
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Enabling multi-mission interoperable UAS using data-centric communications, file e1dcb331-add1-7715-e053-1705fe0a6cc9
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55
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Smart cities: Recent trends, methodologies, and applications, file e1dcb335-4f5d-7715-e053-1705fe0a6cc9
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52
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Multi-domain SDN controller federation in hybrid FiWi-MANET networks, file e1dcb331-88a3-7715-e053-1705fe0a6cc9
|
50
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Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications, file e1dcb338-61f6-7715-e053-1705fe0a6cc9
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46
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Impact of Interdisciplinary Research on Planning, Running, and Managing Electromobility as a Smart Grid Extension, file e1dcb337-c487-7715-e053-1705fe0a6cc9
|
40
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HOlistic pRocessing and NETworking (HORNET): An Integrated Solution for IoT-Based Fog Computing Services, file e1dcb336-b40b-7715-e053-1705fe0a6cc9
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37
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Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing, file e1dcb338-7149-7715-e053-1705fe0a6cc9
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34
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A Toolchain Architecture for Condition Monitoring Using the Eclipse Arrowhead Framework, file 1d06daab-d5be-4bcb-ae31-dae6340a47d9
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31
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A Cloud-Edge Orchestration Platform for the Innovative Industrial Scenarios of the IoTwins Project, file c4e69f66-c760-490c-abe5-ac7ee477f172
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21
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FlowChain: The Playground for Federated Learning in Industrial Internet of Things Environments, file ea14e7c3-0759-4fef-b28b-750bd4b0f05a
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21
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Multi-stage resource allocation in hybrid 25GEPON and LTE-Advanced Pro FiWi networks for 5G systems, file e1dcb331-ee1b-7715-e053-1705fe0a6cc9
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20
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Virtual Environments as Enablers of Civic Awareness and Engagement, file 709f76e3-1f3b-44b5-a966-ec0e986ea41d
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17
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Efficient Security and Authentication for Edge-Based Internet of Medical Things, file e1dcb338-d3fd-7715-e053-1705fe0a6cc9
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16
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Big Spatial Data Management for the Internet of Things: A Survey, file 941a1413-18c3-482b-b412-adba0171f94e
|
14
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A privacy-preserving cryptosystem for IoT E-healthcare, file d6592373-fc6f-456b-8735-bb51e7685644
|
13
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Interoperable blockchains for highly-integrated supply chains in collaborative manufacturing, file e1dcb339-79fb-7715-e053-1705fe0a6cc9
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12
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TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things, file 4ddacd0b-868d-4a63-9aac-7359940123bc
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10
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Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS, file b370b352-5d25-42b4-a99b-7b076fdb0fa3
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10
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PrioDeX: A Data Exchange Middleware for Efficient Event Prioritization in SDN-Based IoT Systems, file c0e0f2cb-4b23-46e1-96d6-452226017b4c
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10
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MQTT-based Middleware for Container Support in Fog Computing Environments, file e1dcb334-aded-7715-e053-1705fe0a6cc9
|
10
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Defining the Behavior of IoT Devices through the MUD Standard: Review, Challenges, and Research Directions, file e1dcb338-d790-7715-e053-1705fe0a6cc9
|
9
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Machine Learning for Predictive Diagnostics at the Edge: An IIoT Practical Example, file a2f5a29e-4358-4033-8eff-a316bdbc99c2
|
8
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Qos‐aware approximate query processing for smart cities spatial data streams, file e1dcb339-2dff-7715-e053-1705fe0a6cc9
|
8
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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
|
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Meeting Stringent QoS Requirements in IIoT-based Scenarios, file 4851e849-e8eb-4236-bad9-eb1f31ade7f0
|
6
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End-to-end QoS Management in Self-Configuring TSN Networks, file da9ed29d-fa97-44d4-98af-2bfa0dda3092
|
6
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|
MQTT-Driven sustainable node discovery for internet of things-fog environments, file e9167ed1-01e2-433d-8e55-b03a9766df77
|
6
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|
Emerging research areas in SIP-based converged services for extended Web clients, file e1dcb32c-c240-7715-e053-1705fe0a6cc9
|
5
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A middleware solution for wireless iot applications in sparse smart cities, file e1dcb330-6852-7715-e053-1705fe0a6cc9
|
5
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A Reference Model and Prototype Implementation for SDN-Based Multi Layer Routing in Fog Environments, file e1dcb337-ac01-7715-e053-1705fe0a6cc9
|
5
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An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing, file 3805402c-4ad3-4146-be82-e95d036e0856
|
4
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Editorial: Smart Space Technological Developments, file e1dcb32c-4239-7715-e053-1705fe0a6cc9
|
4
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The Big Data era in IoT-enabled smart farming: Re-defining systems, tools, and techniques, file e1dcb334-46d0-7715-e053-1705fe0a6cc9
|
4
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An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing, file e1dcb335-b0c6-7715-e053-1705fe0a6cc9
|
4
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Towards smarter cities: Learning from Internet of Multimedia Things-generated big data, file e1dcb336-7b43-7715-e053-1705fe0a6cc9
|
4
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Virtual Environments as Enablers of Civic Awareness and Engagement, file e1dcb337-90f4-7715-e053-1705fe0a6cc9
|
4
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Measuring the impact of COVID-19 restrictions on mobility: A real case study from Italy, file e1dcb339-192d-7715-e053-1705fe0a6cc9
|
4
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Special Issue on Cybersecurity Management in the Era of AI, file 79643b06-23ad-432f-9cc4-618b7afdc7bb
|
3
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null, file e1dcb331-03ab-7715-e053-1705fe0a6cc9
|
3
|
|
Guest Editorial for Special Issue on Emerging Peer to Peer (P2P) Network Technologies for Pervasive and Mobile Computing, file e1dcb331-b16c-7715-e053-1705fe0a6cc9
|
3
|
|
A privacy-preserving cryptosystem for IoT E-healthcare, file e1dcb336-bf75-7715-e053-1705fe0a6cc9
|
3
|
|
The Big Data era in IoT-enabled smart farming: Re-defining systems, tools, and techniques, file 57567970-6e19-4155-bb73-ebd75d99f808
|
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
|
|
A survey on fog computing for the Internet of Things, file e1dcb332-005d-7715-e053-1705fe0a6cc9
|
2
|
|
A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance, file e1dcb334-294f-7715-e053-1705fe0a6cc9
|
2
|
|
Self-Adaptive Management of SDN Distributed Controllers for Highly Dynamic IoT Networks, file e1dcb335-24ce-7715-e053-1705fe0a6cc9
|
2
|
|
The audit4cloud platform for auditing the networking performance of public clouds, file e1dcb335-a8a0-7715-e053-1705fe0a6cc9
|
2
|
|
Big Spatial Data Management for the Internet of Things: A Survey, file e1dcb336-7ceb-7715-e053-1705fe0a6cc9
|
2
|
|
Industry 4.0 Solutions for Interoperability: A Use Case about Tools and Tool Chains in the Arrowhead Tools Project, file e1dcb337-8660-7715-e053-1705fe0a6cc9
|
2
|
|
Machine Learning for Predictive Diagnostics at the Edge: An IIoT Practical Example, file e1dcb337-a7bd-7715-e053-1705fe0a6cc9
|
2
|
|
Application-Driven Network-Aware Digital Twin Management in Industrial Edge Environments, file 0a2e875f-825e-478a-ab4a-dab2c91d4433
|
1
|
|
QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios, file 5f6ba232-6624-4009-8403-b2f3a7a96d2b
|
1
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|
A Mobility-Based Deployment Strategy for Edge Data Centers, file 96dd99bb-793f-483c-b372-680c94737535
|
1
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Middleware for Differentiated Quality in Spontaneous Networks, file e1dcb32c-29dc-7715-e053-1705fe0a6cc9
|
1
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|
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
|
1
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An OCCI-compliant framework for fine-grained resource-aware management in Mobile Cloud Networking, file e1dcb32f-e122-7715-e053-1705fe0a6cc9
|
1
|
|
Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing, file e1dcb330-840a-7715-e053-1705fe0a6cc9
|
1
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|
LTE proximity discovery for supporting participatory mobile health communities, file e1dcb330-8410-7715-e053-1705fe0a6cc9
|
1
|
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Context Awareness for Adaptive Access Control Management in IoT Environments, file e1dcb330-e2ec-7715-e053-1705fe0a6cc9
|
1
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DRIVE: Discovery seRvice for fully-Integrated 5G enVironmEnt in the IoT, file e1dcb332-36c7-7715-e053-1705fe0a6cc9
|
1
|
|
Design Guidelines for Big Data Gathering in Industry 4.0 Environments, file e1dcb333-33c7-7715-e053-1705fe0a6cc9
|
1
|
|
Container Orchestration Engines: A Thorough Functional and Performance Comparison, file e1dcb333-d3e9-7715-e053-1705fe0a6cc9
|
1
|
|
FogDocker: Start Container Now, Fetch Image Later, file e1dcb334-1e82-7715-e053-1705fe0a6cc9
|
1
|
|
A social-driven edge computing architecture for mobile crowd sensing management, file e1dcb334-752f-7715-e053-1705fe0a6cc9
|
1
|
|
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications, file e1dcb334-7628-7715-e053-1705fe0a6cc9
|
1
|
|
Clustering of Spatial Data with DBSCAN: An Assessment of STARK, file e1dcb334-bd3b-7715-e053-1705fe0a6cc9
|
1
|
|
Toward self-adaptive software defined fog networking architecture for IIoT and industry 4.0, file e1dcb334-ee05-7715-e053-1705fe0a6cc9
|
1
|
|
Analysis of growth strategies in social media: The instagram use case, file e1dcb334-f0bd-7715-e053-1705fe0a6cc9
|
1
|
|
SDN-Based Traffic Management Middleware for Spontaneous WMNs, file e1dcb336-804b-7715-e053-1705fe0a6cc9
|
1
|
|
Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project, file e1dcb336-a161-7715-e053-1705fe0a6cc9
|
1
|
|
Meeting Stringent QoS Requirements in IIoT-based Scenarios, file e1dcb337-483f-7715-e053-1705fe0a6cc9
|
1
|
|
PrioDeX: A Data Exchange Middleware for Efficient Event Prioritization in SDN-Based IoT Systems, file e1dcb338-e684-7715-e053-1705fe0a6cc9
|
1
|
|
Efficient and Privacy Preserving Video Transmission in 5G-Enabled IoT Surveillance Networks: Current Challenges and Future Directions, file e1dcb338-ec75-7715-e053-1705fe0a6cc9
|
1
|
|
MIINT: Middleware for IIoT Platforms Integration, file e1dcb339-6da1-7715-e053-1705fe0a6cc9
|
1
|
|
RLQ: Workload Allocation With Reinforcement Learning in Distributed Queues, file ef47b047-00be-4c1c-8b80-929622023a10
|
1
|
| Totale |
2718 |