Not every device in an M2M network will be connected directly to the internet; the Internet of Nodes is more a reality than the IoT. Each house, car, train etc. would be its own domain with a local server allowing the communication of devices within that domain.
Using a house and neighborhood as example, the local server has the intelligence to make decisions on what data is shared within the domain and which data needs to be distributed to external domains, whether those domains are houses in the neighborhood (or whatever Geo boundary is appropriate), the power company NOC, manufacturers support and efficiencies system etc.
By moving control and forwarding to Layer 7 and white listing on each of the local servers (higher security will mitigate man-in-middle attacks), connections for those external domains which need to have access to specific data, a neighborhood could make decisions and adjustments to houses within a boundary to ensure the power consumption is optimized on a community level if one house is consuming a larger amount. Decisions can be made based on activity history in each domain to turn down thermostats, turn off lights etc and giving an overall reduction in total power consumption market wide.
We do not need to know what every device is doing all the time, we only need to know when a device is not acting properly so it can be adjusted or repaired before failure. Coversant has done this successfully for a nationwide Fortune 100 HVAC company which employs 1500 service representatives to service its HVAC systems in 200,000 buildings. The problem the company wanted to solve was to support more buildings and to provide a more proactive, intelligent level of service—without adding additional service representatives—and to create a new revenue model for this higher level of service. The goal was to make the buildings run more efficiently via active monitoring of data gathered from HVAC system sensors. SoapBox was implemented to gather data from the sensors and upload it in real time to the company’s business intelligence (BI) application, which then would analyze the data and send messages back to the sensors to change settings or do something differently. Due to the implementation of this solution, the company was able to reduce outages, extend the life of equipment, and reduce electrical usage by 40 percent.
For residential application- A green-oriented company that provides property management of energy-efficient residential homes wanted to reduce their costs as an operator so they could negotiate lower bulk rates from the power companies and reduce the need for power companies to build additional power plants and infrastructure. The company deployed sensor-enabled smart meters on water heaters and HVAC systems, thermostats, and smart plugs that detect and control the “vampire effect” (draining of power) of appliances and other devices in the residences they managed. Previous to implementing SoapBox, all of the analytics of the data collected by the sensors had to be uploaded to the company’s BI application and analyzed manually. SoapBox was implemented to provide communication between the sensors and the BI tool so data could be analyzed and acted upon quickly. In the three years since the solution was implemented, several thousand homes are being monitored and optimized. They have realized 20 to 30 percent savings in energy costs per home. The company is in the process of being acquired due to its technology solution.
White paper on this is at Coversant’s website- SoapBox: A Platform to Power the Industrial Internet and SoapBox versus IBM MessageSight.