Perspectives on IoT-enabled Edge Computing with specific focus on Swap and Charge Stations, Intelligent Battery Packs and Cloud-enabled Support. Additional insights on challenges and risks to be mitigated.

1. IEEE SmartTech 2018
( http://www.ieeesmarttech.com/ )
Future of
Edge Computing
in Mobility Systems
Narayanan Subramaniam [ Chief Software Architect @ ]
LinkedIn: http://www.linkedin.com/in/cnsubramaniam
@copyright: Narayanan Subramaniam

2. Context: (F)Edge Computing @
Smart Battery
Swap/Charge Stations)
SMART Network
IoT Hub,
Monitoring,
Provisioning,
Machine
Learning
Legend:
Metrics, Telemetry Data
Alerts, Audits
Software (Features, Algorithms – DevOps)
Diagnostics (Logs, Events)
Configuration, Provisioning, Billing
• DevOps based Design with Agile Development
• Security, Scale, Availability and Performance is Implicit
• Intelligent Autonomous Stations and Smart Batteries
Swap/Charge Stations)
@copyright: Narayanan Subramaniam

3. Landscape of Computing Use Cases for Mobility
Consumers and
Fleet Operators
• Personalization:
• User Experience
• Search
• Performance
• Utilization and ROI
• Collaboration
• Meetings
• Content
• Augmented Reality
• Search
• Weather
• Points of Interest
Vehicles and Mobility
Stations (e.g. Swap,
Charge)
• B2B:
• Asset Tracking,
• Operational Metrics
• Resource Optimization
• B2C
• User Experience
• Performance
• Transaction Metrics
• Autonomous Vehicles
• Safety and Security
• Availability
Energy Providers (e.g.
Renewable Sources)
• B2B:
• Dynamic Pricing
• Energy Demand
• Grid Feedback
CONTINUOUS DATA STREAMS AT VOLUME, IN VARIETY, THAT ARE EVER CHANGING
1. Who: Sensors, Embedded Computing, Personalization
2. When: (Near) Real Time, Accumulated Data
3. Why: Operations, Performance, Safety, Reliability, Availability and Data Insights (AI/ML)
@copyright: Narayanan Subramaniam

4. Why Embark on (F)Edge Computing for Mobility Solutions ?
@copyright: Narayanan Subramaniam
Near Real Time
Processing, Autonomous
Operations vs Cloud
Latency/Availability
• Operations:
• Multiple streams of sensor and
embedded based data e.g. Power
Delivery, Charging, Braking
• Thermal, Energy, Policy Management
• Metrics Aggregation and localized
Decision Algorithms, A/B Testing
• Safety and Security
• Anomaly Detection and Isolation
• Fire Detection and Suppression
• Video-analytics – theft, environment
• Availability
• Fault Patterns and Redundancy
• Security Denial of Service issues
• Personalization
• Secure content caching, offline
modes for Billing/Payment
Cost of Computing-In and
Connectivity-To the
Cloud, Vendor Lock-In
• 3-5 Year Cost Analysis:
• Storage and Archival
• Connectivity and Data Transit (APIs)
• Computing (Serverless and Server
Based) with ever increasing Data
• Technology Lock-In
• Lock-In to vendor extensions
• Data extraction and portability costs
• Delicate Balance
• Skills vs Recurring Non Core Cost
• How critical is Autonomy ?
Privacy, Data Retention,
Regulations/Compliance
• Data Retention:
• Locally Managed – push only what is
needed in the Cloud
• Privacy
• Cloud Data Storage Privacy versus
local Aggregation and/or
Anonymization/Pseudonymisation of
PII Data
• Explicit Opt-In/Out Costs
• Data Sovereignty
• Geo-location of Data At Rest

5. Reference Architecture for (F)Edge Computing
Credits: Reference from OpenFog Consortium: https://www.openfogconsortium.org/wp-
content/uploads/OpenFog_Reference_Architecture_2_09_17-FINAL.pdf

6. Protocol
Adaptation,
Model
Adaptation
Device
Addressing
and Routing
High Availability Messaging
Wireless
(Bluetooth,
Wifi, GPRS,
GPS, NFC)
Wired
(CAN2.0,
Modbus,
Ethernet,
TCP/IP, USB,
GPIO, SPI)
Cloud/IoT Interfaces (MQTT,
REST)
Database:
• Metrics
• Alarm/Alert
• Audit, Transactions
• Event/Logs
• Configuration
• Toggle, A/B
• Diagnostics
• Security
• Algorithms/Policies
Station Sensor
and
Battery Pack
Interfaces
Station Application Stack:
• SWAP and Charge
Orchestration
• Station Monitoring
• Energy Management
• Safety and Security
Management
• Charging and Thermal
Management
• Diagnostics
• Configuration and Provisioning
• SW/FW Management
• Entitlement and Billing
• UI/UX
• Containers
• Localized ML, Analytics
High Level (F)Edge Computing Architecture @
@copyright: Narayanan Subramaniam

7. Mobility (F)Edge Computing Technology Stack Candidates
Credits: Images Thanks to Open Source Community Technology and Development Organizations

8. Challenges and The Way Forward
@copyright: Narayanan Subramaniam
Skills and Cost
• Skills:
• Multidisciplinary – Agile Software
Engineering, DevOps, PC, Storage
technologies, Networking/Telecom,
Cloud, Machine Learning experience
• Full Stack, Embedded Developers,
need retraining in orthogonal skills
• Cost:
• Buy vs Build results in vendor lock-in
versus skills challenges
• Multidisciplinary skills to use or build
the (F)Edge Platform effectively in a
Buy vs. Build approach
• Cost Effective Industrial Grade
components for a range of Thermal,
Environmental, Serviceability needs
Communication
Interfaces for (Big) Data
• Data Is Key:
• Protocols like CAN and Modbus not
appropriate for Bulk Data Transfer
• Lower Cost Industrial Ethernet/Wifi
and Convertor components to
facilitate exploding Data
requirements
• Clear separation of Control,
Management and Data Plane with
Enterprise grade Security
Domain Specific Data
Standardization
• Standardized Data Models that are
Domain specific for common API level
Orchestration and off the shelf
Software components to solve
baseline Use Cases
• Data Model Extension constructs for
business differentiation and
competitive benefits
• Design for Privacy

9. Demos
@copyright: Narayanan Subramaniam

10. THANK YOU !
@copyright: Narayanan Subramaniam