M
Min-You Wu
Researcher at Shanghai Jiao Tong University
Publications - 244
Citations - 9120
Min-You Wu is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Wireless sensor network & Wireless network. The author has an hindex of 38, co-authored 244 publications receiving 8170 citations. Previous affiliations of Min-You Wu include University of Central Florida & University of New Mexico.
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Journal ArticleDOI
Performance-effective and low-complexity task scheduling for heterogeneous computing
TL;DR: Two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time are presented, called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm.
Journal ArticleDOI
Hypertool: a programming aid for message-passing systems
Min-You Wu,Daniel D. Gajski +1 more
TL;DR: Programming assistance, automation concepts, and their application to a message-passing system program development tool called Hypertool, which performs scheduling and handles the communication primitive insertion automatically, thereby increasing productivity and eliminating synchronization errors.
Journal ArticleDOI
Towards Secure Industrial IoT: Blockchain System With Credit-Based Consensus Mechanism
TL;DR: This work proposes a credit-based proof-of-work (PoW) mechanism for IoT devices, which can guarantee system security and transaction efficiency simultaneously, and designs a data authority management method to regulate the access to sensor data.
Proceedings ArticleDOI
Task scheduling algorithms for heterogeneous processors
TL;DR: Two low-complexity efficient heuristics are presented, the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm for scheduling directed acyclic weighted task graphs (DAGs) on a bounded number of heterogeneous processors.
Journal ArticleDOI
CDC : Compressive Data Collection for Wireless Sensor Networks
TL;DR: This paper adopts a power-law decaying data model verified by real data sets and proposes a random projection-based estimation algorithm for this data model, which requires fewer compressed measurements and greatly reduces the energy consumption.