new m2m

M2M Definition

Solutions allowing communication between devices of the same type and a specific application via wired or wireless networks

Three drivers of M2M and IoT growth

Understanding physical environment, improved technology and networking, and reduced component costs

Main goal of M2M deployment

To achieve productivity gains, reduce costs, and increase safety or security

Key difference between M2M and IoT data sharing

M2M generally does not allow broad data sharing; IoT connects systems to the broader Internet

IoT blending of information sources

Combines sensor data with GIS, road databases, weather systems, and other real time data

M2M vs IoT Application Focus

M2M focuses on a single application and single device; IoT focuses on multiple applications and multiple devices

M2M vs IoT Business Model

M2M is business objective driven and mainly B2B; IoT is participatory community driven and supports B2B and B2C

M2M vs IoT System Approach

M2M is a vertical system solution; IoT is a horizontal enabler approach

M2M vs IoT Standards

M2M often uses de facto and proprietary standards; IoT uses standards and open source approaches

SENSEI Project Goal

To enable Real World integration in a future Internet

SENSEI Service Infrastructure Requirement

It should be separated from underlying communication networks and based on Internet Protocol

SENSEI Resource Separation

Separation of sensing and actuation from devices, entity centric services, and users

ETSI M2M TC Architecture Type

A horizontal system with separation of devices, gateways, and common service core

IoT A ARM Meaning

Architectural Reference Model for the Internet of Things

IoT A Main Aim

To achieve high interoperability between different IoT solutions at communication, service, and information levels

IoT Design Objective Horizontal System

Target a system of real world services that are open, service oriented, and secure

IoT Design Objective Reuse

Design for the reuse of deployed IoT resources across different application domains

IoT Design Objective Abstraction

Design different levels to hide underlying complexities and heterogeneities

IoT Design Objective Trust

Design for ensuring trust, security, and privacy

Basic IoT Device Properties

Microcontroller, power source, sensors or actuators, and communication capability

Common Microcontroller Sizes in IoT Devices

8 bit, 16 bit, and 32 bit

IoT Device Operating Systems

Main loop, event based, real time, or full featured operating system

Device Management Definition

Efficient means to perform management tasks such as provisioning and configuration

Execution Environment Definition

Manages application lifecycle and provides Application Programming Interfaces

Basic Device Type

Provides basic sensor or actuator services and requires a gateway for WAN connection

Advanced Device Type

Hosts application logic and WAN connections and often functions as a gateway

Device Management Provisioning

Initialization or activation of devices regarding configuration and enabled features

Device Management Fault Management

Enables error reporting and access to device status

Gateway Role as a Mediator

Operates between the device management server and devices when direct communication is not optimal

Gateway Role as a Proxy

Gateway acts as a device management server to the device and as a device management client to the server

Gateway Role as a Translator

Gateway represents devices and translates between different protocols such as TR 069 or CoAP

Network Definition

Created when two or more computing devices exchange data or information

Network Nodes and Links

Computing devices are nodes and they communicate over links

M2M Big Data Characteristic

Huge amounts of data are generated by capturing detailed aspects of device involved processes

M2M Heterogeneous Data

Data produced by a variety of devices differing in sampling rate and quality

M2M Temporal Data

Data that measures the environment over time

M2M Spatial Data

Data coupled to interactions in specific locations

M2M Polymorphic Data

Data that obtains different meanings depending on the semantics and process applied

M2M Data Security Risk

High risk of leaking private information and usage patterns due to detailed data capturing

Data Acquisition

Collection of data from devices via wired or wireless links using continuous, interval, or event based collection

Data Validation

Checking acquired data for correctness and meaningfulness within an operating context

Cloud On Demand Self Service

Unilateral provisioning of computing capabilities without human interaction with providers

Cloud Broad Network Access

Capabilities accessed through standard mechanisms for thin or thick client platforms

Cloud Resource Pooling

Provider resources are dynamically assigned to multiple consumers through a multi tenant model

Cloud Rapid Elasticity

Capabilities can scale rapidly outward and inward according to demand

Cloud Measured Service

Automatic control and optimization of resource use through metering capabilities

Public Cloud

Services owned or operated by third parties with costs borne by the provider

Hybrid Cloud

Combines public cloud and on premises infrastructure or applications

Private Cloud

Cloud infrastructure used exclusively by one organization

Infrastructure as a Service IaaS

Provides virtual hardware, storage, and networking provisioned over the Internet

Platform as a Service PaaS

Provides a development and deployment environment including middleware and database systems

Software as a Service SaaS

Third party hosts applications and makes them available over the Internet

IaaS Target User

Network Architects

PaaS Target User

Developers

SaaS Target User

End Users

Descriptive Analytics

Uses statistics such as means and frequencies to create KPIs for system performance

Predictive Analytics

Uses historical facts to forecast demand or perform predictive maintenance

Clustering Analytics

Identification of groups with similar characteristics for customer segmentation

Anomaly Detection

Identifying fraud or failures by checking for anomalous consumption patterns