Christer Åhlund

ICT Architectures for Smart Cities/Regions

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

A smart city in Europe

•  “In Smart Cities, digital technologies translate into better public services for citizens, better use of resources and less impact on the environment.”

A smart city in Europe

•  “In Smart Cities, digital technologies translate into better public services for citizens, better use of resources and less impact on the environment.”

•  This is a need for rural areas as well –  That is, better public services, better use of resources

and less impact on the environment.”

What does smart mean

•  In this context it can be interpreted as –  “simplifying” –  “more efficient” –  “more informed” –  “controllable” –  ”adaptable” –  etc.

Smart from a social perspective

•  A society that listens to and understands the people living there.

•  Make use of the trends in engagement of citizens –  Crowdsourcing –  Social innovations –  Research among citizens

Smart from a technical perspective

•  Retrieval of information about states and activities in cities and regions to be used to manages environments in a sustainable way (ecological, social and economical)

•  Requires gathering of data using sensors. Sensed data is then processed to extract information, to be informed of and manage objects

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

ICT as an enabler for smart cities and regions

User interaction

Sensorer Gateway

LTE/5G

Wi Fi

LAN Internet

Server

AAA

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

Sensing

•  Requirements –  Sensors need to be identified so that we can trust the

source –  Sensor communication needs to be secured

•  Solutions –  Sensors need to authenticate –  Encrypt sensor communication

Sensing

•  Sensor communication solutions in this context should be able to handle: –  Fixed sensors –  Mobile sensors –  Delay tolerant networking –  Opportunistic communication

Sensing

•  Authentication •  Encryption

Selection of gateway

•  For performance considerations we are combining information about the RSSI, round trip time, and delay.

•  A policy value caclulated –  PV=wRSSI*RSSIn+wRTT/RTTn+wJitter/Jittern

Some sensor authentication and communication results

•  Delays experienced, time to handle authentication and battery consumption

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

Communication and processing decisions

•  Where to store and process information, we need to consider: –  Cloud computing performances –  Communication capacity

Communication and processing decisions

•  Monitoring communication and cloud platform performance

2. Network probing

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

Example of a service in a Smart City/Region

•  A Bayesian Approach for Forecasting Heat Load in a District Heating System (DHS)

Forecasting Heat Load in a DHS •  District Heating System(DHS)

–  Heat production side –  Distribution network –  Heat consumption side

•  DHS optimize energy production by reusing waste energy with CHP(Combined Heat and Power) plants

•  CHP plants have efficiency of 65% – 90%

DHS Plant

Heated Water

Cooled Water

Residential and Commercial Buildings

CHP plant of Skellefteå Kraft

Forecasting Heat Load in a DHS

•  Using a Bayesian statistical approach to develop heat load forecasting models

•  Identify the influence of several parameters on the heat load forecast

•  Identify the parameters with the most influence on the heat load forecast

Forecasting Heat Load in a DHS •  We assume conditional independence between the parameters

influencing the heat load forecast •  Naive Bayes has been shown to perform well even if there is

dependency among parameters •  t = current time •  h = horizon •  HL(t) = Heat load •  HL(t+h) = Heat load •  forecast •  Tout=Outdoor temp •  Ts = Supply temp •  Tr = Return temp

•  Tdelta = Ts – Tr

•  m = flow rate •  Dw = day of week

•  Hd = hour of day

Forecasting Heat Load in a DHS

•  Two ways to learn Bayesian Network –  Continuous variables –  Discrete variables

•  Two techniques used for

discretization: –  EWD(Equal width discretization) –  K-means clustering

Histogram of Heat Load of Building C during Winter Season

Forecasting Heat Load in a DHS

Equal Width Discretization •  Sort continuous values from min to

max •  Divide the sorted continuous range

into k intervals of equal width .

•  We choose k = 5 for all parameters

K-means clustering •  Divide dataset into k clusters (discrete

states) •  All parameters are discretized using k =5 •  Algorithm

–  k data points randomly selected as centroids

–  Every data point assigned to a centroid (Euclidean distance)

–  Re-compute centroids for each cluster

–  Repeat previous step until centroid of each cluster is fixed is fixed

K-means clusters

Proposed Model

Forecasting Heat Load in a DHS

Winter season-average accuracy of 81.23 % for all 3 buildings for HL(t+1)

Forecasting Heat Load in a DHS

Summer season-average accuracy of 76.74 % for HL(t+1)

Outline

•  Introduction •  A Smart City/Region ICT architecture •  Sensor communication •  Network and Cloud monitoring for

Communication and Processing decisions •  A Smart City service – Forecasting Heat-load •  Awareness of Smart City Information •  Summary

How to visualise information in a Smart City/Region

•  Multi-modal interaction to gain access to information

•  To be made aware of situation augmented reality might be an attractive future approach

•  Hololens!

Summary

•  ICT solutions provide “smartness” through –  Gathering of data via sensing –  Adaptable communication –  Data analytics for information retrieval –  Information access through multi-modal interfaces

•  Few deployments in place so far, often with limited to specific area

•  A timely research area

Some papers •  D. Granslund, C. Åhlund, P. Holmlund, “EAP-Swift: An Efficient Authentication and Key

Generation Mechanism for Resource Constrained WSNs,” To be published in Hindawi International Journal of Distributed Sensor Networks, 2015.

•  D. Granlund, P. Holmlund and C. Åhlund, “Opportunistic Mobility Support for Resource Constrained Sensor Devices in Smart Cities,” Sensors 2015, Vol. 15, no. 3, pp 5112-5135.

•  K. Mitra, S. Saguna, C. Åhlund, “A Mobile Cloud Computing System for Emergency Management,” IEEE Cloud Computing, vol. 1, issue 4, pp. 33-38, 2014.

•  K. Mitra, S. Saguna, C. Åhlund, “A Mobility Management System for Mobile Cloud Computing,” to be printed in proceedings of the IEEE Wireless Communications and Networking Conference (WCNC) conference, 2015.

•  Ss. Idowu, S.Saguna, C. Åhlund. & O. Schelén, “Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach, ” IEEE International Conference on Smart Grid Communications (SmartGridComm 2014), pp. 554 – 559, 2014.

•  S. Idowu, C. Åhlund, O. Schelén, O, “Machine learning in district heating system energy optimization,” in proceedings of IEEE International Conference on Pervasive Computing and Communications, 2014, pp. 224-227

Questions