Real Time Analysis of Sensor Data for the Internet …people.cs.pitt.edu/.../cs3720/RT-Analysis-SensorData.pdfReal Time Analysis of Sensor Data for the Internet of Things by means

Real Time Analysis of Sensor Data for the Internet of Things

by means of Clustering and Event Processing

Work of:NUI Galway and University of Zagreb

Presented by:Rakan Alseghayer

What is IoT?§ a global infrastructure for the information society,

enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies. [www.itu.int].

§ Scale?– IDC expects the installed base of the IoT to grow to 212 billion

"things" globally by the end of 2020.”

§ Things produce Data!!

RakanAlseghayer,UniversityofPittsburgh 2

IoT’s Data§ Sensor technology is the driver of IoT

– Focus on more sensors deployed and bigger sensor NWs.§ Data produced is huge!!

– Big Data again L


IoT’s Position


Source: Gartner 2014

Problem?

Source:Gartner2014

IoT Challenges1. Current focus on sensor deployment and producing

more data.

2. Separation between ecosystems of services and IoTdata.


Goal and Approach§ Goal:

– Providing analytics and information from IoT big data

– Focus more on “unknown unknowns”

§ Approach: – The integration and usage of existing analytics

solutions along with sensor produced big data.


Outline

§ Motivation§ Contributions

§ RT interpretation based on OpenIoT§ OpenIoT use case of Zagreb city

§ Experimental Evaluation§ Conclusions


OpenIoT§ Open source platform to the open source community for

connecting physical and virtual sensors to the Cloud.

§ OpenIoT-VDK that is a ready-to-use version for academic and training purposes.


OpenIot Architicture

RakanAlseghayer,UniversityofPittsburgh 9Source:open-platforms.eu

OpenIoT components§ Extended Global Sensor Network (X-GSN):

– Middleware for deployment and programming of sensor NWs– Allow for virtual sensors (abstraction from device specifics).

§ Linked Sensor Middleware(LSM-Light):– Transforms data from virtual sensor format to Linked Data stored

in RDF (Resource description frame work) format.

§ Intelligent Server Component:– Glue the X-GSN and LSM-Light in a service based cloud

environment, and allows users to query the data.


X-GSN§ Wrappers allow the data encapsulations from device

format to X-GSN format.§ Feeds data to LSM-Light in a push based or pull based

mode


LSM-Light§ The semantic level§ Allows data querying through:

– SPARQL if data is stored already (i.e., static data)– CQELS if data is streamed through query registration ahead of

time.


Outline

§ Motivation§ Contributions

§ RT interpretation based on OpenIoT§ OpenIoT use case of Zagreb city

§ Experimental Evaluation§ Conclusions


Use Case Description§ What?

– Aims at understanding air pollution dynamics in Smart Cities by integrating data from multiple sensors

§ How?– Correlating various variables about air quality in different times

and locations.

§ Uses OpenIoT platform.


Sensors§ Custom designed, off-the-shelf components that

communicate via bluetooth.§ Electrochemical sensors to measure CO and (either

NO2 or SO2).§ Can be mounted on bikes, backpacks, or clothes.§ Sensors sends data to mobile app on smartphone that

transmit data to OpenIoT on the cloud.


Dataset§ Real sensor data measured in the city of Zagreb part of

the campaign SenseZGAir .§ 20 volunteers (sending nodes to the OpenIoT)§ 144 Km2 area covered, total distance of 758.6 km.§ 16,835 data points of 7 dimensions (NO2 or SO2).


Data Partitioning§ Time:

– Data is divided into windows of 2 hours.– non-overlapping windows.– Total of 28 windows worth of data.

§ Location:– Divided into 4 zones.– Data is spatially grouped using K-means.– Centroids are points of interests.– Voronoi-like partitions.


Location Partitioning


Z2->307points

Z1->4431points

Z3->2148pointsZ4->7019points

Correlation Measures§ Pearson Correlation Coefficient:

§ Understanding r:– r == 1 perfectly positivly correlated (green in graph)– r == -1 perfectly negatively correlated (red in graph)– r == 0 no linear correlation (grey in graph)


Correlation Analysis§ Correlation undirected graph G=(V,E) where V is

represents n variables, E represent correlation between them.

§ Visually represented, where A to F are variables§ Edges:

– Green à positive corr.– Red à negative corr.– None

§ Total of 94 graphs.§ Size of node shows reading level§ Thickness of the edge in graphs indicates the value |r|


Hot Day Results

§ Strong (-) correlation b/w Humidity and Temperature à known trend.

§ Strong (+) correlation b/w CO and SO2 à both gases produced by same source, and weakens at the last window à(traffic?).

§ CO and Humidity correlation weakens à possible effect of humidity on contaminant?


Night Results

§ Varying effects of Temp., CO, and pressure and all switch signs during this reading.

§ This is true for humidity and pressure à in low population areas, pollutant gasses are easily affected by environment conditions.

§ Note: Zone 4 was split into 2 sub-zones that showed new patterns à mobile crowd-sensed data is geographically valuable.


Window Size§ Experimented with different sizes (15 min to 12 hours).

§ Trade-off:– Too small: fast analysis, no trustful correlations.– Too large: high latency, very good correlations.


Window Size Effect


nocapturingofSO2+poorcorr.

Finegrainedcorr.,butverylonglatency

Conclusions§ Introduced an OpenIoT approach for real time sensor

data processing in the cloud.§ OpenIoT was presented in a way that showed IoT

adaptation towards satisfying analytics work on collected IoT data.

§ SenseZGAir collected data that introduced the air quality through analytics by correlating different parameters.

§ Graphical representation enables scientists and researchers to easily draw conclusions about the air quality.

25RakanAlseghayer,UniversityofPittsburgh

Questions?Thank you J

Real Time Analysis of Sensor Data for the Internet …people.cs.pitt.edu/.../cs3720/RT-Analysis-SensorData.pdfReal Time Analysis of Sensor Data for the Internet of Things by means

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