EDBT 2026 Demo / reviewers in the wild / expert
Zhi Shang
dblp:43/8112
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorComputer networks · 1
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 |
Internet of things and sensor networks · 100% | |
| Network and information security
1 paper |
Cyber-physical and IoT security · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet of things and sensor networks › wireless sensor network
wireless rechargeable sensor network |
0.4 | 1 | 2019 | CoDoC: A Novel Attack for Wireless Rechargeable Sensor Networks through Denial of Charge · INFOCOM 2019 |
Cyber-physical and IoT security › wireless sensor network security
wireless rechargeable sensor network security |
0.1 | 1 | 2019 | CoDoC: A Novel Attack for Wireless Rechargeable Sensor Networks through Denial of Charge · INFOCOM 2019 |
Methods — techniques the papers use, named apart from their topics
request prediction · 0.8collaborative attacking algorithm · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | CoDoC: A Novel Attack for Wireless Rechargeable Sensor Networks through Denial of ChargeabstractWireless rechargeable sensor networks (WRSNs), benefiting from recent breakthrough in wireless power transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods focus on scheduling algorithms and system optimization, and the issue of charging security/threat is ignored, causing it vulnerable to attacks. In this paper, we develop a novel attack for WRSN through Denial of Charge (DoC) aiming at maximizing destructiveness. At first, we form a generalized on-demand charging model, which provides fundamental basis for designing charging attacks. Then a request prediction method (RPM) is introduced for predicting the emergences of charging requests. Afterwards, a Collaborative DoC attacking algorithm (CoDoC) is developed, which tempers/modifies and generates fake charging requests, yielding normal nodes exhausted. Finally, to demonstrate the outperformed features of CoDoC, extensive simulations and test-bed experiments are conducted. The results show that, CoDoC outperforms in making sensor exhausted as well as causing missing events. Chi Lin 0001, Zhi Shang, Wan Du, Jiankang Ren, Lei Wang 0005, Guowei Wu 0001 |
INFOCOM | 2 |
| 2005 | Application of artificial intelligence CFD based on neural network in vapor-water two-phase flow
Zhi Shang |
Eng. Appl. Artif. Intell. | 1 |