Scalable framework for monitoring machine-to-machine (M2M) devices

Patent metadata

Field	Value
Publication number	US-9806902-B2
Application number	US-201414220300-A
Country	US
Kind code	B2
Filing date	Mar 20, 2014
Priority date	Mar 20, 2014
Publication date	Oct 31, 2017
Grant date	Oct 31, 2017

Abstract

A device receives information associated with machine-to-machine (M2M) devices connected to a host server device via a network. The information associated with the M2M devices include one or more of device information associated with components of the M2M devices, application information generated by the M2M devices, or network information associated with interactions of the M2M devices, with the network, when the M2M devices provide the application information to the host server device via the network. The device performs an analysis of the information associated with the M2M devices via one or more analytics techniques, and generates analysis information based on the analysis of the information associated with the M2M devices. The device provides the analysis information for display by the host server device.

First claim

What is claimed is:

1. A method, comprising:
receiving, by a device, information associated with a first set of machine-to-machine (M2M) devices connected to a host server device via a network,
the device being different from the host server device, and
the information associated with the first set of M2M devices including one or more of:
device information associated with components of the first set of M2M devices, or
application information generated by the first set of M2M devices;

determining, by the device, normal behavior patterns, associated with the first set of M2M devices, over time;
performing, by the device, a first analysis of the information associated with the first set of M2M devices via one or more analytics techniques,
the first analysis, of the information associated with the first set of M2M devices, including:
a comparison of the information associated with the first set of M2M devices and the normal behavior patterns, and
anomaly detection to identify one or more of an item, an event, or an observation that does not conform to the normal behavior patterns based on the comparison, and

the first analysis, of the information associated with the first set of M2M devices, being performed in at least one of:
near real time,
real time, or
batch time;

generating, by the device, analysis information based on the first analysis of the information associated with the first set of M2M devices,
the analysis information including a comparison of the first analysis, of the information associated with the first set of M2M devices, and a second analysis of information associated with a second set of M2M devices,
the first set of M2M devices being different from the second set of M2M devices;

generating, by the device, a dashboard user interface that includes the analysis information and anomaly information; and
providing, by the device and for display, the dashboard user interface to the host server device.

2. The method of claim 1 , further comprising:
providing one or more notifications associated with the analysis information to one or more other devices associated with the host server device.

3. The method of claim 2 , where the one or more notifications include information associated with one or more M2M devices, of the first set of M2M devices, identified as being anomalous based on the first analysis of the information associated with the first set of M2M devices.

4. The method of claim 1 , where the one or more analytics techniques include one or more of:
an anomaly detection technique to identify one or more anomalous M2M devices, of the first set of M2M devices, based on the information associated with the first set of M2M devices,
a trending technique to identify one or more trends for the first set of M2M devices based on the information associated with the first set of M2M devices,
a prediction technique to predict one or more behaviors of the first set of M2M devices based on the information associated with the first set of M2M devices, or
a segmentation technique to group one or more M2M devices, of the first set of M2M devices, into groups, based on the information associated with the first set of M2M devices.

5. The method of claim 1 , where the analysis information includes one or more of:
information associated with one or more anomalies identified in the device information or the application information,
information associated with one or more trends identified in the device information or the application information,
information associated with one or more comparisons of the device information or the application information, associated with the first set of M2M devices, and device information or application information associated with other M2M devices, or
information associated with one or more predictions determined based on the device information or the application information.

6. The method of claim 1 , further comprising:
customizing the dashboard user interface for a particular user by including analysis information and anomaly information for one or more M2M devices, of the first set of M2M devices, associated with the particular user.

7. The method of claim 1 , where the analysis information includes one or more of:
information associated with a data usage level of one or more of the M2M devices of the first set of M2M devices,
information associated with a total session number for one or more of the M2M devices of the first set of M2M devices, or
information associated with a number of bad disks being utilized by one or more of the M2M devices of the first set of M2M devices.

8. A device, comprising:
one or more processors, at least partially implemented in hardware, to:
receive information associated with a first set of machine-to-machine (M2M) devices connected to a host server device via a network,
the device being different from the host server device, and
the information associated with the first set of M2M devices including one or more of:
device information associated with components of the first set of M2M devices, or
application information generated by the first set of M2M devices;

determine normal behavior patterns, associated with the first set of M2M devices, over time;
perform a first analysis of the information associated with the first set of M2M devices via one or more analytics techniques,
the first analysis, of the information associated with the first set of M2M devices, including:
a comparison of the information associated with the first set of M2M devices and the normal behavior patterns, and
anomaly detection to identify one or more of an item, an event, or an observation that does not conform to the normal behavior patterns based on the comparison, and

the first analysis, of the information associated with the first set of M2M devices, being performed in at least one of:
near real time,
real time, or
batch time;

generate analysis information based on the first analysis of the information associated with the first set of M2M devices,
the analysis information including a comparison of the first analysis, of the information associated with the first set of M2M devices, and a second analysis of information associated with a second set of M2M devices,
the first set of M2M devices being different from the second set of M2M devices;

generate a dashboard user interface that includes the analysis information and anomaly information;
store the analysis information; and
provide, for display, the dashboard user interface to the host server device.

9. The device of claim 8 , where the one or more processors are further to:
provide at least one notification associated with the analysis information to at least one other device, associated with the host server device, via an email message, a text message, or a voicemail message.

10. The device of claim 9 , where the at least one notification includes information associated with one or more M2M devices, of the first set of M2M devices, identified as being anomalous based on the first analysis of the information associated with the first set of M2M devices.

11. The device of claim 8 , where the one or more analytics techniques include one or more of:
an anomaly detection technique to identify one or more anomalous M2M devices, of the first set of M2M devices, based on the information associated with the first set of M2M devices,
a trending technique to identify one or more trends for the first set of M2M devices based on the information associated with the first set of M2M devices,
a prediction technique to predict one or more behaviors of the first set of M2M devices based on the information associated wi

Assignees

• Verizon Patent & Licensing Inc

Inventors

• Srivastava Ashok N
• Pamarthy Kalyan
• Das Santanu
• Chen Yunzhu
• Le An
• Sun Xuepeng

Classifications

• H04L12/2825 Primary

  Reporting to a device located outside the home and the home network (access arrangements H04L12/2856; for remote control or remote monitoring of applications H04L67/025; telephonic communication systems adapted for combination with telemetering systems H04M11/002) · CPC title