an implementation of fog computing attributes in an iot … · 2014-11-19 · an implementation of...
TRANSCRIPT
An Implementation of Fog Computing Attributes in an IoT Environment
Ranjit Deshpande CTO K2 Inc.
Introduction
• Ranjit Deshpande – CTO K2 Inc.
• K2 Inc.’s end-to-end IoT platform – Transforms Sensor Data into Predictive Insights
– Data Classification at the network level allows an efficient, scalable Cloud Analytics engine
– Predicts operational efficiencies and business opportunities
– Facilitates the building of secured, flexible and modular applications
Index
• Sensor Networks – Challenges – Software Stack
• Elements of Fog Computing – Example of an end-to-end IoT Architecture – Characteristics of IoT Data – Data Collection – Data Organization – In-network Processing – Data Transmission – Security – Management and Control
Sensor Networks
• Sensor Nodes – Low Power (battery operated) – Typically wireless – Distributed – Resource-constrained – Disparate – Prone to failures
• Sensor Networks – Self-organizing – Self-healing – Robust – Cross-platform, standards-based – Secure
Controller
Sensor Network Challenges
• Software optimization
• Resource constraints
• Power consumption
• Environmental
– RF interference
• Robustness
• Reliability of data
• Power management
• Frequency hopping
• Mesh networking
• Advanced data collection and validation
Software Stack
End-to-End IoT Architecture
Characteristics of IoT Data
• How is the Data Structured?
• Drives the Cost of WAN & storage
• Speed of Data Processing and Consumption
• Very large volume of data
Quantum Latency
Structure Cost
Data Collection
• Policy set by the cloud-based via the Controller
• Push Model – Sensor pushes data to controller – Configurable interval
• Pull Model – Controller requests data from the
sensor – Model-based – Query-based
• Policy affects power consumption
Data Organization
• Data is collected from heterogeneous sources
• Sensor data is often unstructured
• IoT Controller creates order from chaos
– Maintains data model for sensors
– Validates accuracy of data
– Organizes and structures data
In-Network Processing
• Rules-based processing
– Rules can be set by a Cloud-based controller
– Reduced latency for local actions
• Model-based processing
– Controller builds a model of sensor data
– Deviations from model are treated as triggers
• Advanced machine-learning algorithms provide predictive insights into the data
Data Transport
• Secure, end-to-end communications – Link-layer security for sensor nodes – TLS for Controller-to-Cloud communications
• Compression and aggregation – Rules-based aggregation – Compression to reduce bandwidth
• Prioritization and classification – Policy-based prioritization of sensor network events – Control upstream traffic – Crucial for applications requiring low-latencies
Data Aggregation and Classification
• Aggregation
– Coupled with compression can reduce upstream traffic
– Can be controlled via the Rules Engine
– Can be used to batch-transfer data
– Optimize for payload size
• Classification
– Pre-classified data can reduce processing load for the Cloud
– Prioritized events can lower latency for critical events
– Facilitates SLA’s for individual customers in a Public Cloud
Q0 Q1 Q2 Qn
…
P0 P1 Pn …
C Payload
Security
• Sensor Authentication – Pre-shard keys are not secure – Controller can authenticate sensor using x.509 certificates
• Controller – Controller and Cloud perform mutual authentication using
x.509 certificates – Well-established, industry-standard mechanisms
• Link-Layer – Most standardized protocols provide link-layer security
(For e.g. 802.15.4) – Pre-shared keys are not secure
• Transport – All traffic between the Controller and Cloud is encrypted
using TLS
Management and Control
• Sensor Nodes – Exclude rogue sensors from joining the network
– Throttle sensor data volume
– Firmware upgrades
• Controller – Functions as a management gateway for Sensor Nodes
– Enforces local security policy
– Can be managed via existing standards (SNMP, TR-069, REST, etc.)
– Configures sensor network topology: Star vs. Mesh
– Limited logs and alarms
Summary
• Deploying and managing sensor networks requires intelligent local processing
• Building, Scaling and Managing IOT solutions requires a distributed architecture with Fog Computing attributes
• Data conditioning, filtering and classification is crucial
• In-network processing of events is essential for many applications
• Security needs to be a design consideration, not an after-thought