Dr. Zheng Peng
City College of New York
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
23Years (2004-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications2 September 2010 in IEEE Transactions on Mobile Computing
Journal Article
ZZZhong Zhou
Zhong Zhou
Zheng PengJCJun-Hong Cui
Jun-Hong Cui
ZSZhijie Shi
Zhijie Shi
ABAmvrossios Bagtzoglou
Amvrossios Bagtzoglou
Due to harsh aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on the predicted mobility pattern. Anchor nodes with known locations in the network will control the localization process in order to balance the trade-off between localization accuracy, localization coverage, and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.
11 March 2014 in IEEE Transactions on Emerging Topics in Computing
Journal Article
YLYu Luo
Yu Luo
LPLina Pu
Lina Pu
MZMichael Zuba
Michael Zuba
Zheng PengJCJun-Hong Cui
Jun-Hong Cui
In oceans, both the natural acoustic systems (such as marine mammals) and artificial acoustic systems [like underwater acoustic networks (UANs) and sonar users] use acoustic signal for communication, echolocation, sensing, and detection. This makes the channel spectrum heavily shared by various underwater acoustic systems. Nevertheless, the precious spectrum resource is still underutilized temporally and spatially in underwater environments. To efficiently utilize the spectrum while avoiding harmful interference with other acoustic systems, a smart UAN should be aware of the surrounding environment and reconfigure their operation parameters. Unfortunately, existing UAN designs have mainly focused on the single network scenario, and very few studies have considered the presence of nearby acoustic activities. In this paper, we advocate cognitive acoustic as a promising technique to develop an environment-friendly UAN with high spectrum utilization. However, underwater cognitive acoustic networks (UCANs) also pose grand challenges due to the unique features of underwater channel and acoustic systems. In this paper, we comprehensively investigate these unique characteristics and their impact on the UCAN design. Finally, possible solutions to tackle such challenges are advocated.
1 October 2009
Conference Paper
PXPeng Xie
Peng Xie
ZZZhong Zhou
Zhong Zhou
Zheng PengHYHai Yan
Hai Yan
THTiansi Hu
Tiansi Hu
SZShengli Zhou
Shengli Zhou
+3
14 February 2012 in IEEE Transactions on Parallel and Distributed Systems
Journal Article
JLJun Liu
Jun Liu
ZZZhong Zhou
Zhong Zhou
Zheng PengJCJun-Hong Cui
Jun-Hong Cui
MZMichael Zuba
Michael Zuba
LFLance Fiondella
Lance Fiondella
Time synchronization is an important requirement for many services provided by distributed networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless Sensor Networks (WSNs). However, none of them can be directly applied to Underwater Sensor Networks (UWSNs). A synchronization algorithm for UWSNs must consider additional factors such as long propagation delays from the use of acoustic communication and sensor node mobility. These unique challenges make the accuracy of synchronization procedures for UWSNs even more critical. Time synchronization solutions specifically designed for UWSNs are needed to satisfy these new requirements. This paper proposes Mobi-Sync, a novel time synchronization scheme for mobile underwater sensor networks. Mobi-Sync distinguishes itself from previous approaches for terrestrial WSN by considering spatial correlation among the mobility patterns of neighboring UWSNs nodes. This enables Mobi-Sync to accurately estimate the long dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency.
15 January 2013 in IEEE Transactions on Mobile Computing
Journal Article
JLJun Liu
Jun Liu
ZWZhaohui Wang
Zhaohui Wang
MZMichael Zuba
Michael Zuba
Zheng PengJCJun-Hong Cui
Jun-Hong Cui
SZShengli Zhou
Shengli Zhou
Time synchronization plays a critical role in distributed network systems. In this paper, we investigate the time synchronization problem in the context of underwater sensor networks (UWSNs). Although many time-synchronization protocols have been proposed for terrestrial wireless sensor networks, none of them can be directly applied to UWSNs. This is because most of these protocols do not consider long propagation delays and sensor node mobility, which are important attributes in UWSNs. In addition, UWSNs usually have high requirements in energy efficiency. To solve these new challenges, innovative time synchronization solutions are demanded. In this paper, we propose a pairwise, cross-layer, time-synchronization scheme for mobile underwater sensor networks, called DA-Sync. The scheme proposes a framework to estimate the doppler shift caused by mobility, more precisely through accounting the impact of the skew. To refine the relative velocity estimation, and consequently to enhance the synchronization accuracy, the Kalman filter is employed. Further, the clock skew and offset are calibrated by two runs of linear regression. Simulation results show that DA-Sync outperforms the existing synchronization schemes in both accuracy and energy efficiency.
5 August 2010 in IEEE/ACM Transactions on Networking
Journal Article
Due to the long propagation delay and high error rate of acoustic channels, it is very challenging to provide reliable data transfer for time-critical applications in an energy-efficient way. On the one hand, traditional retransmission upon failure usually introduces very large end-to-end delay and is thus not proper for time-critical services. On the other hand, common approaches without retransmission consume lots of energy. In this paper, we propose a new multipath power-control transmission (MPT) scheme, which can guarantee certain end-to-end packet error rate while achieving a good balance between the overall energy efficiency and the end-to-end packet delay. MPT smartly combines power control with multipath routing and packet combining at the destination. With carefully designed power-control strategies, MPT consumes much less energy than the conventional one-path transmission scheme without retransmission. Besides, since no hop-by-hop retransmission is allowed, MPT introduces much shorter delays than the traditional one-path scheme with retransmission. We conduct extensive simulations to evaluate the performance of MPT. Our results show that MPT is highly energy-efficient with low end-to-end packet delays.
About Me
Zheng Peng received a Bachelor's degree in Control Theory and a Bachelor's degree in Computer Science from Zhejiang University, China, in 2002. He obtained a Master's degree in Computer Science from the University of Electrical Science and Technology of China (UESTC) in 2005, and later his Ph.D. from the University of Connecticut, USA. Dr. Peng is currently a Professor at the Shenzhen Institute for Advanced Study, UESTC. Prior to his current position, Dr. Peng was a faculty member of the City College of New York and the University of Connecticut.
His research interests include the design, modeling, development, and performance evaluation of wireless sensor networks, embedded/distributed systems, and their applications in unique environments. He has authored/co-authored over 60 referred journal and conference papers on a variety of topics in underwater wireless networks. In addition to theoretical work, he led the efforts in designing and implementing software/hardware for underwater networks. Further, Dr. Peng participated in a number of field experiments in leading roles, obtaining hands-on and real-world experience from North America to Asia. Moreover, Zheng provides active services to the research communities. He served at multiple international conferences as an organizing committee member, technical program committee member, and/or session chair. He has also worked with several international journals as an associate editor, guest editor, and/or reviewer.