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Rong L. Zheng

dblp:41/9176 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-authorTheory of computation · 1Applied, interdisciplinary, general and emerging computing · 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
2 papers
Internet of things and sensor networks · 58% Physical-layer communications · 25% Datacenter networks · 12%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks › wireless sensor network
data aggregation
0.222010
Order-Optimal Data Aggregation in Regular Wireless Sensor Networks · IEEE Trans. Inf. Theory 2010
Toward Optimal Data Aggregation in Random Wireless Sensor Networks · INFOCOM 2007
Internet of things and sensor networks
wireless sensor network
0.222010
Order-Optimal Data Aggregation in Regular Wireless Sensor Networks · IEEE Trans. Inf. Theory 2010
Toward Optimal Data Aggregation in Random Wireless Sensor Networks · INFOCOM 2007
Physical-layer communications › beamforming
distributed beamforming
0.122010
Order-Optimal Data Aggregation in Regular Wireless Sensor Networks · IEEE Trans. Inf. Theory 2010
Toward Optimal Data Aggregation in Random Wireless Sensor Networks · INFOCOM 2007
Datacenter networks
many-to-one communication
0.112007
Toward Optimal Data Aggregation in Random Wireless Sensor Networks · INFOCOM 2007
Physical-layer communications
cooperative communication
0.012007
Toward Optimal Data Aggregation in Random Wireless Sensor Networks · INFOCOM 2007

Methods — techniques the papers use, named apart from their topics

stochastic geometry · 0.1information-theoretic scaling laws · 0.1
YearPublicationVenuePosition
2010 Order-Optimal Data Aggregation in Regular Wireless Sensor Networks
abstract
The predominate traffic patterns in a wireless sensor network are many-to-one and one-to-many communication. Hence, the performance of wireless sensor networks is characterized by the rate at which data can be disseminated from or aggregated to a data sink. In this paper, the problem of data aggregation at a single sink is considered. It is shown that a data aggregation rate of Θ(logn/n) per node is optimal and that this rate can be achieved in regular wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.
Richard J. Barton, Rong L. Zheng
IEEE Trans. Inf. Theory2
2007 Toward Optimal Data Aggregation in Random Wireless Sensor Networks
abstract
Data gathering is one of the most important services provided by wireless sensor networks (WSNs). Since the predominant traffic pattern in data gathering services is many-to-one communication, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we provide a theoretical characterization of data aggregation processes under different communication modalities in WSNs. We demonstrate that data aggregation rates of Theta(log(n)/n) and Theta(1) are optimal when operating in fading environments with power path-loss exponents that satisfy 24, respectively. Furthermore, the optimal rate can be achieved using a generalization of cooperative beam-forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in literature are shown to be suboptimal in the low-to-medium attenuation regime (for 2 < alpha < 4).
Rong L. Zheng, Richard J. Barton
INFOCOM1
2006 Cooperative Time-Reversal Communication is Order-Optimal for Data Aggregation in Wireless Sensor Networks
abstract
Data gathering is one of the most important services provided by wireless sensor networks (WSNs). The predominant traffic pattern in data gathering services is many-to-one communication. Hence, it is critical to understand the limitations of many-to-one information flows and devise efficient data aggregation protocols to support prolonged operations in WSNs. In this paper, we demonstrate that a data aggregation rate of p(logn/n) is optimal and that this rate can be achieved using a generalization of cooperative beam forming called cooperative time-reversal communication. In contrast, the non-cooperative multihop relay strategies widely adopted in the literature are shown to be suboptimal in both capacity and network lifetime
Richard J. Barton, Rong L. Zheng
ISIT2