Demo Data from EDBT 2026 / reviewers in the wild / expert
Wanli Gong
dblp:289/8351
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0003-1815-5919ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Edge and fog computing · 38% Software-defined and programmable networks · 38% Internet of things and sensor networks · 24% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Edge and fog computing
application placement |
0.5 | 1 | 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021 |
Internet of things and sensor networks › iot services
iot service provisioning |
0.5 | 1 | 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021 |
Edge and fog computing › mobile edge computing
mobile edge cloud |
0.5 | 1 | 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021 |
Software-defined and programmable networks
network function virtualization |
0.5 | 1 | 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021 |
Software-defined and programmable networks › network function virtualization
virtual network function placement |
0.5 | 1 | 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge Clouds · IEEE Trans. Mob. Comput. 2021 |
Methods — techniques the papers use, named apart from their topics
heuristic algorithm · 0.5approximation algorithm · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | NFV-Enabled IoT Service Provisioning in Mobile Edge CloudsabstractConventional Internet of Things (IoT) applications involve data capture from various sensors in environments, and the captured data then is processed in remote clouds. However, some critical IoT applications (e.g., autonomous vehicles) require a much lower response latency and more secure guarantees than those offered by remote clouds today. Mobile edge clouds (MEC) supported by the network function virtualization (NFV) technique have been envisioned as an ideal platform for supporting such IoT applications. Specifically, MECs enable to handle IoT applications in edge networks to shorten network latency, and NFV enables agile and low-cost network functions to run in low-cost commodity servers as virtual machines (VMs). One fundamental problem for the provisioning of IoT applications in an NFV-enabled MEC is where to place virtualized network functions (VNFs) for IoT applications in the MEC, such that the operational cost of provisioning IoT applications is minimized. In this paper, we first address this fundamental problem, by considering a special case of the IoT application placement problem, where the IoT application and VNFs of each service request are consolidated into a single location (gateway or cloudlet), for which we propose an exact solution and an approximation algorithm with a provable approximation ratio. We then develop a heuristic algorithm that controls the resource violation ratios of edge clouds in the network. For the IoT application placement problem for IoT applications where their VNFs can be placed to multiple locations, we propose an efficient heuristic that jointly places the IoT application and its VNFs. We finally study the performance of the proposed algorithms by simulations and implementations in a real test-bed, Experimental results show that the performance of the proposed algorithms outperform their counterparts by at least 10 percent. Zichuan Xu, Wanli Gong, Qiufen Xia, Weifa Liang, Omer F. Rana, Guowei Wu 0001 |
IEEE Trans. Mob. Comput. | 2 |