Exploring power-voltage relationship for distributed peak demand flattening in microgrids

Huang, Zhichuan; Corrigan, David; Zhu, Ting; Luo, Hongyao; Zhan, Xiaoxiong; Gü, Yü

doi:10.1145/2735960.2735968

Cited by 6 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of data infrastructures and data management methods, it is foreseeable that sensing in the future will step into every corner of this world, for example, smart grids [70][71][72]. Then more security and privacy problems will arise.…”

Section: Future Research Directionsmentioning

confidence: 99%

Emergent Technologies in Big Data Sensing: A Survey

Zhu

Xiao

Zhang

et al. 2015

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

show abstract

Section: Future Research Directionsmentioning

confidence: 99%