M2M moves into service sectors with proactive measures that will enrich daily lives of millionsby OMA | Tuesday, November 12, 2013
M2M moves into service sectors with proactive measures that will enrich daily lives of millions
Global Telecoms Business, Eshwar Pittampalli, September/October 2013
The market for machine-to-machine deployments and services is here, with projections pointing to the explosive growth of connected devices into hundreds of billions if not trillions. It is clear that all service sectors from consumer devices and healthcare, to transportation, retail and public safety will be impacted in a positive way with the explosion of M2M deployments.
End users will benefit, but so will the enterprises supporting these service sectors—along with all the associated value chain players in the ecosystem. Enterprises are trying to connect end devices to people and places in order to either improve the productivity of their employees or provide services to end users. This will improve how people live, work, play, shop and commute while raising their top-line revenue.
It is interesting to look at how M2M deployments with the help of data analytics are going to proactively and effectively improve the way we live and enrich our daily lives.
To illustrate how M2M deployment is going to help the consumer services industry, consider a simple vending machine. It is not unusual for the machine to relay the status of inventory of the merchandise and send alert messages ahead of time so that it can be replenished before it goes empty.
It does not take away the frustration when a person deposits the required money and presses the selection button of choice and the machine does not dispense the item chosen. Sometimes you see the item you’ve selected dangling at the edge of its spiral arm and another turn of the arm will certainly drop the item in the collection bin at the bottom of the machine.
With the M2M-equipped smart vending machine, it is possible for the machine to realise that the selected item is not dispensed and hence provide an additional turn to dispense it.
One of the most important advantages of such a deployment is that similar data is collected from millions of such machines. The analysis of this data could lead to intelligence that provides certain preventive actions that could be taken to raise the customer experience level.
Remote patient monitoring not only helps the patient to return home sooner but also reduces the burden on the healthcare system—while still being able to monitor the patient’s vital statistics on a regular basis.
But more importantly, monitoring the vital statistics from the patient who doesn’t necessarily need clinical care in a hospital but does need occasional checks like their blood pressure for example, will reduce future emergency room visits, post-discharge visits, and hospital re-admissions.
On the other hand, data collected can also notify care givers of potentially life-threatening issues before the patient even begins to feel bad—and can notify the patient or home healthcare workers that action needs to be taken.
These positive outcomes not only reduce the burden on the healthcare system, but proactively enrich the lives of the patient and the relatives.
In today’s world of automation and just-in-time processes, it is important that all the critical machinery in an assembly line is well tuned and working. Any critical machinery breakdown that is a part of a conveyor belt assembly line will create havoc and result in the degradation of the efficiency of the plant.
Based on monitoring the vital characteristics of a machine from its normal operation to its abnormal operation, it can be detected ahead of any breakdown. This gives time for maintenance on the machine during idle time, with the anticipated problem being fixed before it becomes an issue.
In such cases with the help of M2M systems it is possible, in association with knowledge and data collected from the machines, to foresee a potential mishap of a machine and save the plant operations and down time of the assembly line.
The use of smart grid applications will make utility company operations more efficient, resulting in fewer power outages and supply interruptions and the reduced need for additional fossil fuel hungry power stations.
With the knowledge gained from the usage pattern of the residents on an electric grid, it is possible to broadcast time of usage based tariffs that are lower than prime time or peak usage of electricity. This proactive notification of peak versus off-peak usage data not only helps prevent power outages, but provides direct economic benefit for the end user.
The most important advantage of this M2M system and the knowledge gained from the data analytics is to push the need for new deployment of a costly fossil fuel system into the future, while maintaining positive customer experience.
The current technology of weather forecasting depends upon satellite photographic imagery. Further, the accuracy of the photographic analysis is dependent of the data analytics of the images acquired by the satellites. It is not too uncommon that in several cases the images and their analysis do not provide accurate forecasting information.
We have often seen a severe forecasted natural disaster turning into a moderate or minimally impacting one. On the other hand a moderate forecasted disaster can turn into a natural disaster—leading to the loss of billions of dollars’ worth of physical assets in addition to loss of many precious lives.
The use of M2M weather sensors and the data analysis of changing weather conditions could provide more precise and accurate information about the current state of weather, resulting in the saving of human lives. The floating sensors are placed in the middle of the ocean hundreds of miles offshore and transmit data on weather conditions in a peer to peer mode to the monitoring station inland.
By deploying the M2M sensors it is possible to get the weather conditions and associated information in real-time to predict upcoming deadly natural disasters before they reach land, giving people adequate time to go to safe neighborhoods and take shelter accordingly.
Emergency services have demanding requirements for feedback of onsite conditions at an incident, so that the ambulance or fire fighters can react in a safe way to protect the individuals who are in the midst of the disaster and themselves.
For example, based on the prevailing weather conditions such as wind speed and direction, humidity and ambient temperatures in and around the affected area, the data analytics engine can predict and forecast dangerous areas from where the firemen should not evacuate ahead of time preventing any loss of lives. For ambulance personnel and hospitals the vital signs data collected from the rescued individuals can provide their health conditions in real-time to the hospital emergency units, who can be ready proactively with the right equipment and personnel to respond to the needs of the incoming disaster victims.
These examples illustrate that the vast network of sensors that will be deployed to support M2M applications. Accurate data collection and analysis is extremely important in realizing the potential of the internet of things.
Once these networks are deployed they not only guide us in how we live, work, commute, take care of our health and the health of our environment, but they also proactively warn us about upcoming natural disasters ahead of time so that we can get prepared to face them and finally help raise the end user experience in customer service deployments.