Z
Zhijie Shi
Researcher at University of Connecticut
Publications - 68
Citations - 2861
Zhijie Shi is an academic researcher from University of Connecticut. The author has contributed to research in topics: Underwater acoustic communication & Wireless sensor network. The author has an hindex of 26, co-authored 68 publications receiving 2563 citations. Previous affiliations of Zhijie Shi include Princeton University.
Papers
More filters
Journal ArticleDOI
Scalable Localization with Mobility Prediction for Underwater Sensor Networks
TL;DR: By utilizing the predictable mobility patterns of underwater objects, a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks is proposed, and results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.
Proceedings ArticleDOI
Aqua-Sim: An NS-2 based simulator for underwater sensor networks
Peng Xie,Zhong Zhou,Zheng Peng,Hai Yan,Tiansi Hu,Jun-Hong Cui,Zhijie Shi,Yunsi Fei,Shengli Zhou +8 more
TL;DR: A network simulator based on NS-2, Aqua-Sim, which effectively simulates the attenuation of underwater acoustic channels and the collision behaviors in long delay acoustic networks and provides a rich set of basic and advanced protocols.
Proceedings ArticleDOI
Bit permutation instructions for accelerating software cryptography
Zhijie Shi,Ruby B. Lee +1 more
TL;DR: Two instructions, PPERM3R and GRP, are proposed for efficient software implementation of arbitrary permutations and a systematic method for determining the instruction sequence for performing an arbitrary permutation is described.
Journal ArticleDOI
Efficient permutation instructions for fast software cryptography
TL;DR: Four new instructions each offer a solution to the problem of efficient, bit-level software permutations, and are described in detail in this monograph on permutations in software.
Journal ArticleDOI
Adaptive Modulation and Coding for Underwater Acoustic OFDM
TL;DR: The effective signal-to-noise ratio (SNR) computed after channel estimation and channel decoding is proposed as a new performance metric for mode switching, which is shown to predict the system performance more consistently than the input SNR and the pilot SNR.