Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality.
Microgrids (MGs) are the most affordable solution for the development of smart grid infrastructures. They are conceived to intelligently integrate the generation from Distributed Energy Resources (DERs), to improve Demand Response (DR) services, to reduce pollutant emissions and curtail power losses, assuring the continuity of services to the loads as well.
We show that fornpoints ind-dimensional Euclidean space, a dataoblivious random projection of the columns ontoO(((logk+log logn)/ε^6)log(1/ε)) dimensions is sufficient to approximate the cost of all k-means clusterings up to a multiplicative (1±ε) factor. The previous-bestupper bounds on O(logn/ε^2) given by a direct application of the Johnson-Lindenstrauss Lemma, and O(k/ε^2)given by [Cohen etal.-STOC’15]
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