SMART STREET LIGHTING: SENSORS VS BIG DATA

Keith Henry, AMILPTechnical Sales ManagerTelensa 15 June 2017

> Sensors vs. big data

> Case study: traffic analysis

> Practical conclusions

AGENDA

Connected streetlights live in a rapidly changing data landscape

• Valuable asset: connected streetlights map where people live and work

• They are ideal hosts for sensors• Central Management Systems (CMS) connect

streetlights to the cloud• But the wider data and application environment

is changing rapidly

Wirelessnode

CMS

• These apps reflect where domain expertise resides• Typically “sense + actuate” applications• Examples includes asset management, traffic planning and

citizen engagement• There is great synergy between applications

Streetlight CMS lives among many expert domain applications

CMS

1 2 3

Big data: new platforms emerging• Connect everything• Analyse the big data• Generate “actionable

insights”• Today restricted to leaders’

dashboards• Potentially huge data comms

costs• Guaranteed ongoing data

integration costs

Big dataplatform

Sensor edge processing: distilled data

• Sensor and processor on the light pole

• Big data distilled into small and useful data

• Small data comms costs

Big dataplatform

Example application:traffic analysis

Camera-based traffic analysis via CMS

Pole-mounted camera captures video of passing traffic – lots of data. Cameras are reliable commodity hardware today.

Big dataplatform

1

Edge analytics distil useful data

Analytics software on edge processor distils useful info –traffic count, traffic mix etc. Only this small data is transmitted to the CMS. No user-id information is retained, no video is stored.

Big dataplatform

2

CMS traffic-adaptive dimming

The CMS algorithms use the traffic count data to adaptively dim streetlights when traffic is light.

Big dataplatform

3

Rich data shared with other applications

CMS passes traffic mix data with route planning and congestion data with citizen info apps. Open data interfaces.

Big dataplatform

4 4

Big data platform augmentsdata with context

Big data platform takes all of the data, combines it with egair quality and roadworks information, and displays an overall picture on the leadership dashboard.

Big dataplatform

5

©Telensa

Three application layersare emerging in smart cities

1. Sensor edge processingComputing power on the light pole with software for locally processing raw data (eg video) into distilled data

2. Domain expert appsExpert applications that automate departmental activities, such as lighting CMS, asset management, waste, etc

3. Big data platformsCentral systems that combine multiple data feeds to generate and visualisenew insights

What are the Implications for Lighting Professionals?

> Examine potential data costs: transmission costs for sensors and cameras now far outweigh the cost of the hardware

> Consider sensor edge processing to reduce data costs

> Question the justification for a big data platform if the same level of integration and insight can be gained by the integration of expert applications

> Demand open or standard API links between systems, such as Hypercat

> Be vigilant: vendors and integrators of big data platforms often underestimate the challenge and cost of data integration

©Telensa

Lighting will change the economics of smart city applications

> Perfect location: where people are

> Ideal place for sensors

> Powered host for edge processing

> Dramatically reduces the cost of data transmission and integration

©Telensa