Tag: m2m

Yesterday Chris Brogan had an interesting blog post sharing his take on the state of LBS and what it will need to take it to the next level.  As a noted and respected voice in the areas of new media communication and social networking, Chris points out what he considers to be current limitation of LBS technology application and also identifies some things he thinks would add value to the the LBS offering to consumers.  For the record, I would agree that LBS is in its infancy, that its value to the average consumer is pretty limited.  Recent studies have show that the uptake of LBS applications is limited to a small, keen segment of the population but others suggest it is growing. Having said that, I also believe there is great potential for growth in LBS application development.

Key value adds today:

  • Proximity.  Identify your location to business, provide you with real time updates on information such as local traffic and weather.
  • Navigation.  Plan your route, obtain real time directions.

Some new interesting developments:

  • Geofencing.  An extension of proximity capability to define a region of interest around your current location or some fixed point. Applications might be to monitor the movement of a known object (like your kids or a pet?), identify businesses within some limit of my current location (barbers within three blocks).  In his post Chris Brogan refers to this as an identity register.
  • M2M. Machine to machine technologies are emerging in a wide array of b2b markets it will be interesting to see how effectively these can be extended to a consumer market.

Some things that would take LBS to the next level:

Chris Brogan also mentioned temporary groups and commerce capability as important enhancements to the LBS experience.

From my perspective I see analytics as being another important enhancement both from a business and consumer perspective.

Challenging issues:

LBS applications are dependent on content.  To the extent that it is available, applications with flourish or remain marginal.  For instance, if I want to know the barbers in a three block radius of my current location, how many of the existing barbers are actually discoverable?  Obviously those that are, will benefit from the application but if I perceive the information content presented to me is incomplete my confidence in the LBS application will lag.

The other side of the content coin is information privacy.  An issue not limited to the world of LBS applications, the question of protecting information a user considers private (such as current or past location) is an important one. The idea of temporary groups may be one way of addressing privacy concerns.

Those are a few of my thoughts.   Let me know what you think.

So what do you know about machine to machine (M2M) technologies?  Not so much you say.  But I suspect you may have heard of Smart Grids.  These are a form of ‘smart infrastructure’ that employs M2M technology.

A basic definition of M2M is the suite of technologies that support wired or wireless communication between machines allowing for the exchange of information about such factors as location, temperature, device status, etc.

Simple M2M Architecture (courtesy ETSI)

Smart Grids are an example of an M2M technology implementation where infrastructure within a utility grid are instrumented with sensors that can monitor a wide variety of information such as energy consumption, power outages, etc.  These sensors are networked into a communication network that allows the sensed information to be fed to a central system where events can be monitored and various responses affected.

M2M technologies are emerging as a significant technology growth area with applications being realized in many areas.

From a geospatial perspective, M2M presents many opportunities.  At CeBit Australia in August 2010 David Hocking, CEO of the Spatial Industries Business Association boldly stated “No smart infrastructure is smart unless it’s geo-enabled.  Spatial data is the glue for Smart Infrastructure.”

Whether the centrality of geospatial information is as great as that or not, there are clear opportunities for the inclusion of geospatial technologies within the M2M technology mix for many applications.

Many of the classic benefits of geographic information systems can be exploited when M2M systems account for and collect geospatial data.  These include:

Asset visualization – the ability to view network devices being monitored in an appropriately scaled spatial context is beneficial in quickly assessing network status, problems, patterns, etc.

Asset management – geoanalytical tools can be utilized to understand the dynamic nature of the network as assets by assessing qualities like grouping, patterns, movement, etc.

Spatially structured dashboard views of key network parameters can provide both alerts and contextual information about network asset behavior.

Correlation with ancillary information – one of the strengths of GIS technologies is the ability to correlate various types of data on the basis of location.  For instance it might be relevant in an M2M utility grid application to be able to visualize the network against weather information to better understand causes of network disruption.

M2M network performance analysis – spatial measurement tools can be utilized to visualize and better understand network behavior.  These can be summative (evaluating past performance) or real time in nature.

Resource allocation and deployment – network management planning – whether for regular maintenance or emergency response, can be better planned and coordinated with a spatial reference.

While the integration of geospatial technologies with M2M infrastructure has the potential to add value to the overall network, there are issues that need to be addressed in order to achieve maximum benefit.  These include:

The degree to which network monitoring and analysis needs to be dynamic in nature?

Issues around data management:

Where is it stored?

Who owns the data?

Is the data complete?

Do the various data layers have compatible levels of accuracy?

Has the data been cleaned and structured so that it can be interfaced with other data layers?

How is the data maintained?

System connectivity:

How will systems talk to each other?

Can data from disparate systems be reliably accessed?

While there may be challenges to integrating M2M and geospatial technologies, there are also clear benefits that can enhance the value of M2M networks.