Platt Perspective on Business and Technology

Some thought concerning a rapidly emerging internet of things 7: identifying and harnessing the power of ad hoc systems 2

Posted in business and convergent technologies, social networking and business by Timothy Platt on June 22, 2013

This is my seventh posting to a series on a rapidly emerging new level of online involvement and connectedness: the internet of things (see Ubiquitous Computing and Communications – everywhere all the time 2, postings 211 and loosely following for Parts 1-6.) This is also my second installment in this series to explicitly discuss ad hoc networks, where the inventory of functional nodes participating is constantly changing with new nodes entering in and ones that have been actively connected in dropping out (see Part 6.)

I began this discussion in Part 6 with a working example of ad hoc networks: networks of Global Positioning System (GPS) navigation devices, and how their real-time location data is collected and aggregated, and sent back to individual network-connected nodes as organized knowledge about current traffic patterns and traffic flow rates at and around their real-time current locations.

Most such devices and the networks that support them also give users the option to change and update routes at their discretion and give traffic congestion and flow data for any new route entered in. And once a route is entered in this information could at least potentially be combined with all other routing information entered in by other drivers through their connected GPS devices and with other traffic data and collectively that data could be used in anticipating and predicting possible traffic flow patterns to come.

• Ad hoc networks of things are as tightly rules based as would be the case for any fixed node system Supervisory Control and Data Acquisition (SCADA) network (as discussed in Part 4 and Part 5 of this series.)
• The rules that are in place that functionally organize an ad hoc network of this type can change overall with for example software updates to the central server computer system that organizes and processes GPS device input and sends out knowledge developed from that. And the rules and I add user options that are available and followed by specific nodes can be changed or updated through distribution of device level software or firmware updates too. But at any given time, and as a general operational rule, the system of processes that functionally define these networks and their nodes are relatively fixed, and even completely so. It is just the specific identities and numbers of nodes that are connected in, change.

And this brings me to the second basic ad hoc network of things example that I would discuss here in this series: device networks that would be used to develop more accurate, longer term next generation weather forecasts. And I begin that with the fundamentals:

• Weather forecasts are based on the coordinated analysis of large volumes of very specific types of weather data that have to be accurately and consistently gathered, brought together and analyzed. This includes data measuring, for example: temperature, dew point or relative humidity, atmospheric pressure, wind speed and direction, and of course the precise location (latitude, longitude and altitude) and time that these measurements are taken in.
• The precise weather conditions in any given location as determined by the values obtainable for these data point variables is most dependent on the precise values of these same variables in immediately adjacent locations, and certainly on a real-time, immediate basis.
• And essentially identical first step, localized data analysis and organization would be carried out for data collected from essentially any given measurement location to determine precise weather conditions there, as wider area coverage data is brought together across multiple such locations for weather modeling and predictive purposes.
• This means weather data is eminently suitable to parallel processing and similar computer analysis where data is initially analyzed for the immediate sites it is measured at, and then these results are coordinately analyzed for measurement site to measurement site interaction, and those results are further analyzed, and with more and wider ranging data and with new data as it comes in. In fact weather models and weather predictions for moving forward are essentially all based now, on computer-driven analysis of the huge collections of data collected.
• And here is where ad hoc networks of things can meaningfully and significantly enter this story. Weather and the phenomena that comprise it shift and change with time in a very nonlinear manner. Thresholds are reached where new patterns of observed behavior suddenly arise and even predominate for level of influence. The qualitative weather transition from rain to sleet to snow with temperature shifts, or from humid air to precipitation come immediately to mind here, as do the transitions to conditions that directly cause hail or microburst down-drafts.
• This type of phenomenological discontinuity arises in more normal and everyday weather patterns, and also in the development of extreme weather patterns. So accurate weather prediction, and even for basic patterns of weather coming, calls for coordinated collection and analysis of data collected from a very fine-mesh distribution of sensors. And when severe and dangerous weather patterns are a possibility, as for example when conditions are developing that could lead to tornado formation, finer detail point by point data collection becomes essential, and even lifesaving.
• There, finer meshed, more detailed data makes more accurate earlier predictions possible, and for anything like tornado formation that can mean giving people in harm’s way more warning time.

And this is where two emerging technologies can come together to meet some very specific engineering challenges and societal needs:

• The development of small scale and even micro-drones that can carry low cost, simple sensor arrays and GPS and communications capabilities, and
• The emergence of internet of things networking capabilities.

Consider highly dangerous and suddenly emerging weather patterns such as tornado formation. Tornado funnels themselves form very quickly and their exact location of formation and touch-down, where they reach the ground and cause damage, develops over very brief time-frames. But tornado activity and significant potential for it developing, form out of weather patterns that are much longer in developing, where prediction and warnings are a real possibility and where lead time in warnings could be significantly improved. So for example, most tornadoes are spawned from mesocyclone patterns that form at an altitude of several miles, in a type of thunderstorm called a supercell. And these and other tornado-generating weather patterns can be tracked for their development over a period of hours and more.

Fixed, ground-based weather data collection points are always going to relatively sparsely situated and weather balloons and standard aircraft-based systems, and weather satellites and other current resources can leave gaps from the granularity and level of detail that they can capture, real-time and certainly for highly localized events where precise timing and location cannot be predicted far in advance.

• Consider a scenario in which areas that are showing weather patterns developing that could lead to tornado formation, are swarmed with weather monitoring mini-drones that could provide real-time, high resolution data on precise weather conditions and how they are developing.
• In principle at least, these could be used to fine-detail map out the position and scale of an entire mesocyclonic disturbance and how it is evolving, tracking weather activity from near-ground level on up that develops from it.

Yes, there are practical issues and challenges that would have to be addressed here, including loss of at least some percentage of these drones, with their coming down and crashing. But the smallest micro-drones currently in development weigh as little as an ounce or so and any given sensor drone need only carry as little as one sensor and a GPS/communications link chip, and a light-weight battery. My point here is that as a matter of practical ,doable principle, this type of weather monitoring and reporting ad hoc network is possible, and it could work and to great value.

I am going to turn in my next series installment to consider intelligent networks, and how artificial intelligence and systems complexity are already blurring the line where a human user can tell when they are connected to another person or to a device. And with that, I at least begin to explicitly explain why I have included this series on networks and the internet of things, in my Social Networking and Business directory. Quite simply, when artificial intelligence and threshold systems complexity meet networks of things and they operate in an open online setting, social networking ceases to be entirely and exclusively person to person – and people involved might not know when they have crossed that line and are communicating and networking with other people or not.

Meanwhile, you can find this and related postings at Ubiquitous Computing and Communications – everywhere all the time and its continuation page, and at Social Networking and Business.

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