Digital Twin: sensiNact

Kenty­ou provides a digit­al twin that provides, not only the real-time inform­a­tion about the phys­ic­al envir­on­ment but also pre­dic­tion about that envir­on­ment based on the embed­ded ML and AI fea­tures.

Build­ing upon the data col­lec­ted through the Eclipse sensiN­act plat­form and ana­lysed through our AI based data ana­lyt­ics, Kenty­ou provides advanced digit­al twin cap­ab­il­it­ies.

They allow the cre­ation of vir­tu­al entit­ies that accur­ately mod­el their real-world coun­ter­part. This digit­al twin can be used for mon­it­or­ing and super­vi­sion of exist­ing asset allow­ing for an increased con­trol.

Bey­ond these super­vis­ory cap­ab­il­it­ies, the digit­al twin tool that we pro­pose offers intel­li­gent abil­it­ies for data pre-pro­cessing, decision mak­ing and semant­ic rep­res­ent­a­tion and infer­ence.

Kenty­ou has built a semant­ic lay­er on the top of sensiN­act IoT plat­form. This semant­ic lay­er encom­passes a gen­er­al-pur­pose onto­logy, which is able not only to rep­res­ent IoT data but also to provide answers on the 5-W’s ques­tions (Who did What, When, Where and Why).

The cus­tom­iz­able onto­logy behind the data mod­el enables semant­ic reas­on­ing applic­able to dif­fer­ent applic­a­tion con­text. The Digit­al Twin take bene­fit of the edge com­put­ing (there­fore address­ing the latency and pri­vacy con­cerns) and also pro­pose a dis­trib­uted AI train­ing, test­ing and col­lab­or­at­ive decision mak­ing.

The solu­tions developed by Kenty­ou have shown their full poten­tial when there is a need to com­pare and ana­lyze data com­ing from het­ero­gen­eous sources or even applic­a­tion domains. Our Digit­al Twin approach allows us to under­stand prob­lems in all their com­plex­ity to bring optim­ized solu­tions.

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