A Smart City-Smart Bay Project: Establishing an integrated water monitoring system for decision

support in Dublin Bay

Fiona Regan, Timothy Sullivan, Ciprian Briciu, Helen Cooney, Dian Zhang*, Edel O’Connor*, Noel O’Connor*, Alan Smeaton*

Marine and Environmental Sensing Technology Hub (MESTECH), National Centre for Sensor Research

Dublin City University

*CLARITY Centre for Sensor Web Technologies, Dublin City University Dublin, Ireland

Project  Ra+onale  Design,  deployment  and  integra2on  of  an  autonomous  real-­‐2me  

mul2modal  sensing  network  for  improved  decision  making  

Research  Objec+ves    •  Improve  Water  quality  monitoring  

•  Improve  discrete  sampling  regimes    •  Iden+fy  and  Improve  detec+on  of  Security  threats  

•  Iden2fy  threats  to  health  (microbial  and  other  pollutants)  

•  Enhanced  Signal  processing:  Develop  surrogate  measurements  

•  Produce  Baseline  datasets  on  water  quality  

     Introduc+on    Ra+onale    Study  site    Methods    Instrumenta+on    Data  analysis    Results    Conclusions  

Current  and  future  Network  Distribu+on  by  2014  

River  Liffey  

Dublin  Bay  

Dublin  City  Centre  

2  km  

Pilot  Sites:  Malahide  and  Poolbeg  Estuaries  

     Introduc+on    Ra+onale    Study  site    Methods    Instrumenta+on    Data  analysis    Results    Conclusions  

In-­‐situ  sensors  

•  Mul+-­‐parameter  sondes  equipped  with  real-­‐+me  telemetry  systems    •  IP66-­‐Rated  outdoor  network  camera    •  Ini+al  systems  deployed  in  October  2010  -­‐  August  2013:  •  Circa  2.5  million  images  have  been  collected  •  Circa  500,000    individual  sensor  measurements  

     Introduc+on    Ra+onale    Study  site    Methods    Instrumenta+on    Data  analysis    Results    Conclusions  

     Introduc+on    Ra+onale    Study  Site    Methods    Instrumenta+on    Data  analysis    Results    Conclusions  

Duc+ng  of  marina  structure  

220V  power  supply  

Commercial  telemetry  solu+on  box  

 Data  Analy+cs  

•  Machine  learning  objec+ves:  automated  detec+on  and  trajectory  of  vessels  

•  Automated  Turbidity  event  detec+on  –  pixel-­‐based  adap+ve  segmenter  method  

•  Salinity  predic+on  using  mul+ple  data  sources  (+de,  flow,  weather  data)  using  regression  tree  approach  

•  Shipping  ac+vity  +  turbidity:  predic+on  of  sampling  +mes  and  microbial  contamina+on  –  separa+ng  natural  events  from  anthropogenic  events  

•  Water  level  predic+on  

•  Security  Threats:  Unauthorized  shipping  

     Introduc+on    Ra+onale    Methods    Study  Site    Instrumenta+on    Data  analysis    Results    Conclusions  

1 Aug2 Aug

3 Aug4 Aug

5 Aug6 Aug

7 Aug

0

5

10

15

20

25

30

Turbidity 2 m Turbidity 4 m

Turb

idity

(NTU

)

Date 2012

Detec+ng  and  automa+ng  turbidity  event  detec+on        Introduc+on    Ra+onale    Methods    Study  Site    Instrumenta+on    Data  analysis    Results    Conclusions  

Conclusions  

•  An  extensive  network  of  both  in-­‐situ  aqua+c  sensors  and  visual  sensing  systems  have  been  and  are  in  process  of  deployment  in  Dublin  Bay  

 •  The  network  has  already  had  demonstrable  impact  on  monitoring  and  

understanding  dynamic  processes  in  Dublin  Bay  

•  Incorpora+on  of  visual  sensing  nodes  into  the  network  has  proven  advantageous  

•  Machine  learning  and  increased  compu+ng  power  has  aided  in  data  analysis  –  future  work  will  emphasize  data  analy+cs    

 •  Challenges  remain:  Increased  spa+al  coverage,  Biofouling!,  Cost,  

Transla2on  of  data  into  knowledge  

     

Thank  You!  Ques+ons?  

Contacts:  +m.sullivan@dcu.ie;  fiona.regan@dcu.ie