The document discusses the future of edge computing in mobility systems, focusing on its applications in smart battery swap and charge stations, and emphasizing real-time data processing for enhanced operations and user experiences. It highlights the importance of security, cost management, and the balance between local data management and cloud reliance to ensure performance and safety. Additionally, it addresses challenges related to skills, costs, and data communication required to build effective edge computing platforms.

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