H
Houbing Song
Researcher at Embry-Riddle Aeronautical University, Daytona Beach
Publications - 528
Citations - 17473
Houbing Song is an academic researcher from Embry-Riddle Aeronautical University, Daytona Beach. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 56, co-authored 425 publications receiving 11550 citations. Previous affiliations of Houbing Song include Shanxi Agricultural University & University of Virginia.
Papers
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Proceedings ArticleDOI
Elasticity Debt Analytics Exploitation for Green Mobile Cloud Computing: An Equilibrium Model
Georgios Skourletopoulos,Constandinos X. Mavromoustakis,George Mastorakis,Jordi Mongay Batalla,Houbing Song,John N. Sahalos,Evangelos Pallis +6 more
TL;DR: A novel green-driven, game theoretic approach to minimizing the elasticity debt on mobile cloud-based service level is proposed, investigating the case when a task is offloaded, scheduled and executed on a mobile cloud computing system.
Empirical Investigation of the Impact of High-Occupancy-Toll Operations on Driver Behavior
Houbing Song,Brian Lee Smith +1 more
TL;DR: In this paper, the authors conducted a visual time series analysis, price elasticity analysis, and developed a utility-based mode choice model for SOV behavior in the I-394 HOT facility.
Journal ArticleDOI
Smart assisted diagnosis solution with multi-sensor Holter
TL;DR: The proposed system is an efficient, accurate, and interactive auxiliary diagnostic and a therapeutic support tool capable of not only assisting doctors in quickly determining the most valuable information, but also of building a personalized private repository for patients.
Proceedings ArticleDOI
A New Multi-Service Token Bucket-Shaping Scheme Based on 802.11e
TL;DR: A new multi- service flow, token bucket shaping scheme in an IEEE 802.11e environment that adopts a multi-service token bucket technology on the basis of the traffic shaping scheme and can effectively improve the quality of service for video streaming in a wireless environment.
Journal ArticleDOI
A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.
TL;DR: The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.