期刊：
IEEE INTERNET OF THINGS JOURNAL,2020年7(3):2247-2262 ISSN：2327-4662
通讯作者：
Qin, Hua
作者机构：
[Qin, Hua; Xiao, Xiang; Cao, Buwen; He, Jianxin; Chen, Weihong] Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.;[Peng, Yang] Univ Washington Bothell, Div Comp & Software Syst, Bothell, WA 98011 USA.
通讯机构：
[Qin, Hua] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.
关键词：
Cross interface;energy-efficiency;gateway;Internet of Things (IoT)
摘要：
Featured with high bandwidth, high reliability, and native IP compatibility, WiFi has been recommended for a wide range of Internet-of-Things (IoT) applications. However, WiFi is inherently energy-hungry and it may impose high energy consumption on not only IoT devices but also gateways. To reduce gateway's WiFi energy consumption, many energy-efficient solutions for WiFi tethering services can be applied. However, these solutions mainly target the energy optimization of downlink data traffic in WLANs, and they are not suitable for the uplink data traffic of delivering massive IoT data from device to gateway (D2G), which is more common in IoT. When a gateway is powered by batteries, the high energy consumption caused by D2G communications may deplete the gateway quickly and renders the whole system dysfunctional as a result. Toward achieving energy-efficient D2G communications, we propose an innovative Green IoT Gateway (GIG) scheme, which aims at minimizing gateway energy consumption while ensuring the specific delay requirements of devices via cross-interface collaboration. Through utilizing the coexisting low-power ZigBee radios, GIG dynamically schedules the wakeup behaviors of high-power WiFi radios for energy-efficient and delay-bounded D2G communications. GIG has been implemented and evaluated in a prototype system, and the experiment results show that, under the moderate uplink data traffic and delay requirements, the energy consumption of GIG is 38.5% and 12.7% lower than those of a state-of-the-art WiFi tethering scheme and a simple version of the GIG scheme, respectively. Moreover, a great reduction of energy consumption at the device side can also be observed.
期刊：
IPSJ Transactions on Bioinformatics,2019年12:1-8 ISSN：1882-6679
作者机构：
[Yang Y.; Song Y.] Department of Information Technology, Hunan Women's University, China;[Cao B.] College of Information and Electronic Engineering, Hunan City University, China
期刊：
IEEE INTERNET OF THINGS JOURNAL,2019年6(1):718-733 ISSN：2327-4662
通讯作者：
Qin, Hua
作者机构：
[Qin, Hua; Cao, Buwen; Zeng, Min; Chen, Weihong] Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.;[Li, Jessica; Peng, Yang] Univ Washington, Div Comp & Software Syst, Bothell, WA 98011 USA.
通讯机构：
[Qin, Hua] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.
关键词：
Dual radio;energy efficiency;Internet of Things (IoT);pipeline
摘要：
The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11's standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.
关键词：
Computational methods;Feature;Machine learning;Network;Similarity measure;Synergistic drug combinations.
摘要：
Synergistic drug combinations play an important role in the treatment of complex diseases. The identification of effective drug combination is vital to further reduce the side effects and improve therapeutic efficiency. In previous years, in vitro method has been the main route to discover synergistic drug combinations. However, many limitations of time and resource consumption lie within the in vitro method. Therefore, with the rapid development of computational models and the explosive growth of large and phenotypic data, computational methods for discovering synergistic drug combinations are an efficient and promising tool and contribute to precision medicine. It is the key of computational methods how to construct the computational model. Different computational strategies generate different performance. In this review, the recent advancements in computational methods for predicting effective drug combination are concluded from multiple aspects. First, various datasets utilized to discover synergistic drug combinations are summarized. Second, we discussed feature-based approaches and partitioned these methods into two classes including feature-based methods in terms of similarity measure, and feature-based methods in terms of machine learning. Third, we discussed network-based approaches for uncovering synergistic drug combinations. Finally, we analyzed and prospected computational methods for predicting effective drug combinations.
摘要：
Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.
