AI in Smart Cities

Elena Fersman

Director, Machine Intelligence and Automation ResearchAdjunct Professor, The Royal Institute of Technology

Image: Ericsson headquarters, Kista, Sweden

Ericsson at a glance

Enabling the full value of connectivity for service providers

By the numbers:— 180+ countries— 201 BSEK in sales— 100,700 employees— 45,000 patents

Business areas:— Networks— Digital services— Managed services— Technology and new

businesses

Sara Mazur | © Ericsson 2018 | 2018-04-17

Data Lake

Cortana Lucida Mika Amelia Alexa Siri Autin

MSDP = Multicast Source Discovery ProtocolMSDP = Multiservice Delivery Platform

MSDP = Managed Services Delivery Platform

What’s MSDP?

Ericsson Product Catalogue, CPI Store, Wikipedia, and your calendar

What are your sources?

Judging from the site profile learned over time, and the current site behavior, the maintenance

will be needed in Q3 2019.

Tell me when site X is likely to require a preventative maintenance visit?

I changed parameter configuration in the 5G network nodes. I also sent offers to subscribers

with high risk of churn.

What actions did you do for Operator A’s network last month?

Because we need to stay competitive towards our enterprise customers while keeping our

subscribers happy.

Why?

The value is in data

Cortana Lucida Mika Amelia Alexa Siri Autin

From raw data to action

Data and its processing

What’s in the data lake?

Web pages700,000,000

Videos40,000,000

Radio sessions (RAB)120,000,000

Handovers (HSDSCH-CC)300,000,000

Internet sessions (PDP)66,000,000

Sum data10→100 TB/day

Real-time data rate100,000→1,000,000events/second

+200 more typesof events

ONE DAY IN THE LIFE OF A MEDIUM SIZED NETWORK (~10M CUSTOMERS)

20102000

29 billion devices

5 billion people

1 billion places

2022

1G 2G 4G 5G3G

5G – a foundation for digitalization

Telephony and mobile broadband

A digital infrastructure for

industrial and societal

transformation

cutting the cord, adding mobility

going digital

adding video & data mobile broadband

1990

Evolution to 5G will see increase in network complexity

2G

3G

4G

5G

1990s 2000s 2010s 2020s

Complexity

Network function virtualization

Vast differences in terminal capability

Significant variation in traffic demand

Completely new & varied use cases

Dealing with opex and network performance in this environment will go beyond the reach of humans

Multiple coexisting technologies

Machine Learning Machine Reasoning

Driven by massive amounts of datamanaged by data science

Driven by facts and knowledgemanaged by logic

Generic approach

Business objectives

Reasoning and Formal Methods

Semantics and context

Raw data feeds

Available assets

presentation application

Technologies and Components Examples

Applied Context

AI+Formal

Real World Model

Observation and Control

Things and Places

Networks

& Devices

AI + Classical

IoT Resource

Management

Sensors & Actuators

Data Analytics

Ab

straction

and

Seman

tics

Processed data feedsAnalytics

Data capture, Actuator commands, …

Stream analytics, Pattern recognition, Anomaly detection

Semantic annotation, Entity of Interest modeling, …

Knowledge management, verification and synthesis, reasoning, workflow optimization

Business objectives“Optimize grid utilization”

AI and Formal Methods“phase drift is X - increasing price by Y for area Z would reduce consumption 5%, stabilizing grid” alternatively “increase hydro

power by 2%”

Semantics and contextSmart meter for user A @ high reactive power outtake

Substation Norra 2 IED @ outage

Raw data feedsSensor 5 = 3.4 kW

Sensor 8 = 0 kWValve 2 = 12̊

Available assetsSmart meter, 10kV Distribution Substation, Energy Service

Interface (ESI), Photovoltaic/Hydro, Electrical vehicle

Processed data feedsD/M/Y pattern and average of Sensor 5 is: Y (curve)

Anomaly detected in Sensor 8

Intelligent Warehouse Logistics

SCOTT Research Project

Intelligent warehouse logistics - self aware warehouse• Self-aware: “a system that gathers and

maintains information about its currentstate and environment, resons about itsbehavior, and adapts itself if necessary”

• Ingredients for decentralizedautomation and optimization:• Reusable components• Safety between humans and robots in

collaborative scenarios• Increase trust between humans and

machines in order to improve efficiency

Collaborative Robotics

• Robots are aware of other robots and humans counterparts

• Each robot has individual safety zones: safe, warning and critical

• Safety zones can be dynamic; based on robot`scurrent state (e.g. velocity, position,… ) and geometry (e.g. model, arm`s workspace,…)

• Main challenges: • Automated planning and execution for mission

fulfillment• Automated decisions at run-time• Ensuring safety for warehouse exhibiting

collaborative robots

Long Term Research Challengeshttps://www.ericsson.com/en/white-papers/machine-intelligence

Real-time

• 5G: ultra-low latency

• Network/Cloud edge use cases

• Algorithms & Frameworks

Beyond Games and Simulations

• Reinforcement Learning

• Safe exploration

• Simulators vs. live systems

Centralized & Distributed

• From data center to network edge

• Distributed learning and inference

• Model lifecycle management

Human-Machine symbiosis

• Human Intelligence Augmentation

• Evolved Human-Machine interaction

• Frameworks, Requirements

Knowledge bases & Reasoning

• Robust learning of complex models

• Flexibility in decision making

• Declarative + Procedural

Meta Expertise

Common Knowledge

Domain-Specific Knowledge

Use Case-Specific Knowledge

Responsible Machine Intelligence

• Safety

• Trust

• Security

Agent

System

S A

R

Data-driven and data-centric research — Dealing with

heterogeneity though semantics

— Right data at right time and place

— Keeping the global state together

— The value is in data

Key findings

Mix of AI approaches and techniques — ML meets Reasoning— Declarative meets

Procedural— Collaborative

Intelligence— Ensuring safety and

trustworthiness

www.ericsson.com/research