an implementation of fog computing attributes in an iot … · 2014-11-19 · an implementation of...

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