ICT Architectures for Smart Cities/Regions. Christer Åhlund

Transcription

1 ICT Architectures for Smart Cities/Regions Christer Åhlund

2 Introduction Outline 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

3 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.

4 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.

5 What does smart mean In this context it can be interpreted as simplifying more efficient more informed controllable adaptable etc.

6 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

7 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

8 Introduction Outline 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

9 ICT as an enabler for smart cities and regions User interaction LTE/5G Server Wi Fi LAN Internet Sensorer Gateway AAA

10 Introduction Outline 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

11 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

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

13 Sensing Authentication Encryption

14 Selection of gateway For performance considerations we are combining information about the RSSI, round trip time, and delay. A policy value caclulated PV=w RSSI *RSSI n +w RTT /RTT n +w Jitter /Jitter n

15 Some sensor authentication and communication results Delays experienced, time to handle authentication and battery consumption

16 Introduction Outline 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

17 Communication and processing decisions Where to store and process information, we need to consider: Cloud computing performances Communication capacity

18 Communication and processing decisions Monitoring communication and cloud platform performance 2. Network probing

19 Introduction Outline 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

20 Example of a service in a Smart City/Region A Bayesian Approach for Forecasting Heat Load in a District Heating System (DHS)

21 Forecasting Heat Load in a DHS District Heating System(DHS) Heat production side Distribution network Heat consumption side CHP plant of Skellefteå Kraft DHS optimize energy production by reusing waste energy with CHP(Combined Heat and Power) plants CHP plants have efficiency of 65% 90% Residential and Commercial Buildings Heated Water DHS Plant Cooled Water

22 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

23 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 T out =Outdoor temp T s = Supply temp T r = Return temp T delta = T s T r m = flow rate D w = day of week H d = hour of day

24 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

25 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

26 Proposed Model

27 Forecasting Heat Load in a DHS Winter season-average accuracy of % for all 3 buildings for HL (t+1)

28 Forecasting Heat Load in a DHS Summer season-average accuracy of % for HL (t+1)

29 Introduction Outline 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

30 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!

31 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

32 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, 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 K. Mitra, S. Saguna, C. Åhlund, A Mobile Cloud Computing System for Emergency Management, IEEE Cloud Computing, vol. 1, issue 4, pp , 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, 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 , 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

33 Questions