IOT Analytics and business assurance. Ericsson-wedo perspectives October 2017

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1 IOT Analytics and business assurance Ericsson-wedo perspectives October 2017

2 agenda IOT FRAMEWORK AND ARCHITECTURE USE CASES Automotive / Transport / Fleet Management Smart Energy / Utilities Connected Water / SCEF BLOCKCHAIN BASED IOT ASSURANCE KEY DRIVERS OF BUSINESS ASSURANCE IN IOT

Connections (billion) Everything that benefits from a connection will be connected 50 Our vision - 50 billion connected devices 40 30 15

3 Connections (billion) Everything that benefits from a connection will be connected 50 Our vision - 50 billion connected devices years - 26 billion connected devices years - 5 billion connected people years - 1 billion connected places

4 WHERE Are the Actions?

Application Service Providers Application Service Providers Direct Go-to-market Service

White Labeling Technology To Service Providers Connectivity Wholesale Provider

5 Carriers: Where is the Money? Share of overall revenue 6-9% 9-12% 10-20% 60-65% 100% Vertical Applications & Services Application Service Providers Application Service Providers Direct Go-to-market Service Enablement Limited Data plans Flexible Pricing, SLA, Self-service Joint Go-to-market White Labeling Technology To Service Providers Connectivity Wholesale Provider Connectivity Provider Telco Reseller Powered By Telco Vertical Service Provider Ericsson IoT Transformation Services Confidential Ericsson AB 2016 Page 5

6 Data Sources Flow & Use Case Designer Use Case Library Ericsson Role in IoT Transformation Partner: From Foundation to Value Creation & Monetization Vertical Industry Use Case Library AUTOMOTIVE IoT CEM SMART ENERGY SMARTCITY ITS Configurable Exposure REST APIs Core Platform In Memory Cache Dynamic Rule Engine AI & ML toolbox Service Models COTS Data Model Library Automotive Utilities Semantic DB (Knowledge Graph).. Automotive Transport Event Stream Processing Engine Online/Offline Store Incidents Generator Real Time Bus Correlation Correlation (EDR) Profiler Shipping ITS Smart Cities IoT CEM Action Logic Action Orchestrator Automation Actions Library Automotive Utilities Smart Mediation / Ingestion Shipping SmartCities Generic Adaption Flexible Data Source Ingestion Adaptors Library Predictive Main. Root Cause An. PLC Modems LTE CANBUS Probe Device Events Logs xdr CRM Charging FM TT PM M2M Social Media APIs End-to-End Data Source Actuator (systems)

7 IOT analytics applications UTIITIES ITS & AUTOMOTIVE PUBLIC SAFETY SHIPPING SMARTCITIES Central Proactive Operations Centre Performance Management IoT Experience Centre Smart Meter Management Fleet / Asset Management Proactive City Maintenance Communication Supervision Customer Experience Vessel / Asset Management Connected Urban Transport Configuration Management Proactive Outage Management Predictive Maintenance Connected Service Booking Traffic Optimization Intelligent Public Transport Predictive Maintenance Quality of Shipment Conditions Citizen Experience Management Sustainable Energy & Waste Management Fraud & Revenue Protection Proactive Road Friction Warning Preemptive Security Management Maritime Traffic Management City Mobility Smart Grid Monitoring & Management Connected Urban Transport Proactive Hazard Identification Revenue Optimization Proactive Crime & Prevention

8 Connected car / fleet mgmt

9 Solution overview External Systems Operational Control Center Ericsson Connected Fleet Driver Apps Vehicles are fitted with an OBU (On-Board Unit) The OBU can be line-fitted by the OEM or retro-fitted by a workshop or driver The OBU sits inside the vehicle and collects data that is sent to Ericsson Connected Fleet Ericsson Connected Fleet manages vehicle and external data The solution runs in a public cloud Operations handled by Ericsson Analytics and reports

10 Vehicle Modem (3G, 4G) Telematic Unit & CAN 3rd Party APIs Functional Data Flows 1. Connectivity (Accessibility, Retainability, Integrity, Mobility, ) 2. Telematics Data (Engine, Oil, Break, Driving ) Data Connectivity Accessibility KPIs Integrity KPIs Availability KPIs Mobility KPIs Telematic Data Engine Indicators Multimedia Indicators Application Indicators Vehicle Indicators 3. Traffic Analysis (Fleet management, city planning, ) Context-specific Factors Content/Media Traffic Smart City Retail etc..

11 $ Provides Devices $ Provides Service Key players in the eco-system Ericsson Provides Service $ Service Operator Provides Service and devices $$$ Business Owner (Transport co.) Billing Settlement Device Provider Truck Service Ericsson Safe City Solutions Commercial in Confidence March 2017 Page 11

BA drivers in the connected car eco-system PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of

12 BA drivers in the connected car eco-system PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of Things (IoT) device deployment: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance. Analysis of IoT data can help our customers financial and marketing departments learn their customer s habits and needs so they can more efficiently manage their resources Suited to a wide range of IoT use cases including automotive, smart metering, smart home and connected Point-of-Sale applications, the solution provides instant network status and analysis. In addition, it immediately highlights issues by ensuring that a rich array of data is readily accessible With new IoT products being introduced in the market every day, the underlying technology is still going through a considerable amount of startup-level experimentation. WeDo Technologies FMS addresses Validate Payments Detect / Deter Fraud Improve Operations IOT Data can help validate payments made in a Connected Vehicle Eco-System IOT Validation can deter and catch fraudulent transactions and behavior Business Assurance teams and drive IOT architecture towards improving operational efficiency and reducing assurance recovery costs

13 SMART ENERGY / smart city

14 Energy UTILITY USE CASES Energy Aggregation Loading visualization Line Loss Fraud Detection Operation Optimization Network Assurance

15 Utilities Power & Fraud Analytics Energy aggregation Big volume data aggregation and explosion for utility operation efficiency improvement. Loading visualization Loading visualization and grid operation visualization providing optimal network monitoring and planning. Line Loss Power supplying efficiency improvement with line loss deep dive analysis. Hybrid Fraud Detection Revenue protection from Fraud detection and anti-theft analysis with machine learning.

Utility Communication Quality & Fraud Detection KEY HIGHLIGHTS: New way of monitoring customer experience instead of network to reduce and prioritize the right issues: Know the Impact How many

16 Utility Communication Quality & Fraud Detection KEY HIGHLIGHTS: New way of monitoring customer experience instead of network to reduce and prioritize the right issues: Know the Impact How many households are impacted, what symptom are they having and where? Common Factors Who is impacted? What do they have in common? Improve the Impacted What are the Most Probable Causes? What is the Next Best Action to resolve issue? Integrated with Workforce Management systems KEY USE CASES: Highly applicable in Smart Cities scenarios Revenue Assurance See the experience from the customer perspective by detecting symptoms and monitor in real-time Identify Smart meter issues related to connectivity. Identify network cause through isolating the cause down to a cell and single radio event and smart meter Identify potential energy fraud

17 BA drivers in the Smart energy eco-system PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of Things (IoT) device deployment: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance. Analysis of IoT data can help our customers financial and marketing departments learn their customer s habits and needs so they can more efficiently manage their resources Suited to a wide range of IoT use cases including automotive, smart metering, smart home and connected Point-of-Sale applications, the solution provides instant network status and analysis. In addition, it immediately highlights issues by ensuring that a rich array of data is readily accessible With new IoT products being introduced in the market every day, the underlying technology is still going through a considerable amount of startup-level experimentation. WeDo Technologies FMS addresses Detect / Deter Fraud Ensure Quality Validate Payments IOT Validation can deter and catch fraudulent transactions and behavior in a Smart Energy eco-system IOT devices can ensure quality and loss prevention at all points of the end to end flow from source to destination IOT Data can help validate payments made in a Connected Eco-System

18 Overview SCEF definition Service Capability Exposure Function (SCEF) defined in 3GPP ts and ts23.401, is a 3GPP network element to: Provides a means to securely expose the services and capabilities provided by 3GPP network interfaces. Provides a means for the discovery of the exposed service capabilities. E.g. API portal Provides access to network capabilities through homogenous network application programming interfaces (e.g. Network API) defined by OMA, GSMA, and possibly others. Abstracts the services from the underlying 3GPP network interfaces and protocols. Virtualized, cloud optimized, reference NFV-I is Openstack/KVM Authentication & Authorization Ericsson SCEF Solution Partner Integration Policy Enforcement Assurance Abstraction Accounting Access SLA Enforcement Northbound APIs (REST, SOAP, Native/Custom) Service Capability Creation Southbound Protocols (T6a, Ns, Nt, S6t, Tsms, Sh, Rx )

Connected Water Challenge & Requirement Measurement and management of water for cleanliness on a perpetual basis is expensive, time

seniors or devices Battery life is very important, normally need 5-10 years Similar challenges & requirements at many I&S domains e.

support NIDD, secure QoS etc SCS as IoT platform type solution to support device integration, device & data management, real-time

19 Connected Water Challenge & Requirement Measurement and management of water for cleanliness on a perpetual basis is expensive, time consuming, and inefficient Hundreds of locations monitored perpetually Easily deployable and continual monitoring of all connected seniors or devices Battery life is very important, normally need 5-10 years Similar challenges & requirements at many I&S domains e.g. environment monitoring, bike sharing services Possible solution based on 3GPP SCEF as network capability exposure node e.g. support NIDD, secure QoS etc SCS as IoT platform type solution to support device integration, device & data management, real-time process for AS AS focus on IoT app and service NO need to take care of wireless technology LPWA Power Saving Mode to support long battery time

21 BA drivers in A connected water eco-system PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of Things (IoT) device deployment: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance. Analysis of IoT data can help our customers financial and marketing departments learn their customer s habits and needs so they can more efficiently manage their resources Suited to a wide range of IoT use cases including automotive, smart metering, smart home and connected Point-of-Sale applications, the solution provides instant network status and analysis. In addition, it immediately highlights issues by ensuring that a rich array of data is readily accessible With new IoT products being introduced in the market every day, the underlying technology is still going through a considerable amount of startup-level experimentation. WeDo Technologies FMS addresses Validate Payments Detect / Deter Fraud Improve Operations IOT Data can help validate payments made in a Connected Vehicle Eco-System IOT Validation can deter and catch fraudulent transactions and behavior Business Assurance teams and drive IOT architecture towards improving operational efficiency and reducing assurance recovery costs

blockchain assurance - overview Alice pays Bob $500 : : : Alice pays Bob $500 : : : Proof of work Alice agrees on a transaction with Bob The transaction is signed and a block is created to represent

22 blockchain assurance - overview Alice pays Bob $500 : : : Alice pays Bob $500 : : : Proof of work Alice agrees on a transaction with Bob The transaction is signed and a block is created to represent the transaction The block is broadcast and validated by the blockchain network based on a cryptographic hash function The block is added to the chain providing a permanent nonrepudiable and transparent record of the transaction IOT and Blockchains Blockchains can replace expensive operations overhead currently in place to establish trustful transactions IOT devices can participate in such Blockchain assurance networks that will allow the trustful transactions with lower operational costs

23 INDUSTRIALIZED BLOCKCHAIN service package Configuration view blockchain Access to Ericsson Industrial Blockchain API exposed functionality available to Application Developers Provides DATA INTEGRITY that can be used for multiple use cases, from securing firmware updates to verify sensor readouts Available today: Access to Ericsson Data Centric Security offering Supported APIs: Transaction signature storage and extension Transaction signature verification Access through DCS Middleware Gateway

NEXT GENERATION RA&FM SYSTEM COLLECT MONITOR NOTIFY

Management Engines Advanced Fraud Detection (AFD)

Workbench Smart Data Blueprints Adaptive Case

24 NEXT GENERATION RA&FM SYSTEM COLLECT MONITOR NOTIFY DISCOVER ACT Smart Data Stream (ETL & CEP) Fraud Management Engines Advanced Fraud Detection (AFD) KPI Designer Business Sensors Investigation Workbench Smart Data Blueprints Adaptive Case Management Balanced Scorecards Link Analysis Web Portal Dashboard & Reporting

25 1. IOT ANALYTICS

BUSINESS ASSURANCE CHALLENGES ADRESSED PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of

Analysis of IoT data can help our customers financial and marketing departments learn their customer s habits and needs so they can more efficiently manage their resources Suited to a wide range of

26 BUSINESS ASSURANCE CHALLENGES ADRESSED PROVISIONING COMPLEXITY COST AND MARGIN ASSURANCE SLA AND QoS FRAUD AND SECURITY Four fundamental device management requirements exist for any Internet of Things (IoT) device deployment: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance. Analysis of IoT data can help our customers financial and marketing departments learn their customer s habits and needs so they can more efficiently manage their resources Suited to a wide range of IoT use cases including automotive, smart metering, smart home and connected Point-of-Sale applications, the solution provides instant network status and analysis. In addition, it immediately highlights issues by ensuring that a rich array of data is readily accessible With new IoT products being introduced in the market every day, the underlying technology is still going through a considerable amount of startup-level experimentation. WeDo Technologies FMS addresses

27 BUSINESS ASSURANCE CHALLENGES ADRESSED

28 RAID BUSINESS ASSURANCE ACCELERATES CASE ANALYSIS LINK ANALYSIS DASHBOARDS & REPORTS FRAUD ENGINES FRAUD DETECTION RULES CASE MANAGEMENT R A I D F M S E M P O W E R S Y O U T O V I S U A L I Z E D A T A, C H E C K D E T A I L S A N D M A N A G E Y O U R F R A U D C A S E S

RAID BUSINESS ASSURANCE HYBRID APPROACH LEARN Analysts leverage AFD findings and create / improve rules through Rules Optimizer CREATE CORRECT IMPROVE Resolved cases feed the

29 RAID BUSINESS ASSURANCE HYBRID APPROACH LEARN Analysts leverage AFD findings and create / improve rules through Rules Optimizer CREATE CORRECT IMPROVE Resolved cases feed the AFD models to enhance pattern search through Machine Learning ADVANCED FRAUD DETECTION Designed to identify unknown fraud types RULES LIBRARY Targeting known fraud scenarios LEARN

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