Demo Data from EDBT 2026 / reviewers in the wild / expert
Steven Perez
dblp:331/4212
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
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 |
Wireless networking · 70% Internet of things and sensor networks · 23% Physical-layer communications · 7% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless networking › random access
ALOHA |
0.6 | 1 | 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022 |
Internet of things and sensor networks
LPWAN |
0.6 | 1 | 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022 |
Wireless networking
medium access control |
0.6 | 1 | 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022 |
Wireless networking
network capacity |
0.6 | 1 | 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022 |
Physical-layer communications
zadoff-chu sequences |
0.2 | 1 | 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area Networks · IEEE/ACM Trans. Netw. 2022 |
Methods — techniques the papers use, named apart from their topics
testbed experiments · 0.6simulation · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | ZCNET: Achieving High Capacity in Low Power Wide Area NetworksabstractIn this paper, a novel LPWAN technology, ZCNET, is proposed, which achieves significantly higher network capacity than existing solutions, such as LoRa, Sigfox, and RPMA. The capacity boost of ZCNET is mainly due to two reasons. First, a ZCNET node transmits signals that occupy a small fraction of the signal space, resulting in a low collision probability. Second, ZCNET supports 8 parallel root channels within a single frequency channel by using 8 Zadoff-Chu (ZC) root sequences. The root channels do not severely interfere with each other, mainly because the interference power is spread evenly over the entire signal space. A simple ALOHA-style protocol is used for medium access, with which a node randomly chooses the root channel and the range it occupies within the root channel. ZCNET has been extensively tested with both real-world experiments on the USRP and simulations, and the results confirm that ZCNET achieves significant gains over LoRa, Sigfox, and RPMA. ZCNET will likely better accommodate the explosive growth of IoT network sizes and meet the demand of IoT applications. Raghav Rathi, Steven Perez, Jumana Bukhari, Yaoguang Zhong |
IEEE/ACM Trans. Netw. | 3 |