Internet of Things and other things

In the article The Cognitive Net Is Coming published in the recent IEEE Spectrum, the author (Antonio Liotta from the Eindhoven University of Technology) states:
Perhaps as early as the end of this decade, our refrigerators will e-mail us grocery lists. Our doctors will update our prescriptions using data beamed from tiny monitors attached to our bodies. And our alarm clocks will tell our curtains when to open and our coffeemakers when to start the morning brew. (I would say, coffeemaker and curtains should be smart enough - if they are not yet - to know when to start brewing or when to open. Rather than be hard programmed by time, they should sense when these functions are needed).

By 2020, according to forecasts from Cisco Systems, the global Internet will consist of 50 billion ­connected tags, televisions, cars, kitchen appliances, surveillance cameras, smartphones, utility meters, and ­what not. This is the Internet of Things, and what an idyllic ­concept it is.

But here’s the harsh reality he says: Without a radical overhaul to its underpinnings, such a massive, variable network will likely create more problems than it proposes to solve. The reason? Today’s Internet just isn’t equipped to manage the kind of traffic that billions more nodes and diverse applications will surely bring.

Then the author proceeds to devise a more intelligent (cognitive) Internet protocol which would presumably solve the problems of today's global networks. Not going into the details of the cognitive protocol we may find some useful (even though not necessarily totally new) idea to endow every connected computer/processor equipped device with the ability to route data. Given the ­computational capabilities of today’s consumer ­devices, there’s no reason for neighboring smart gadgets to communicate over the core network. They could instead use any available wireless technology, such as Wi-Fi or Bluetooth, to spontaneously form “mesh networks.” This would make it possible for any terminal that taps into the access network—tablet, television, thermostat, tractor, toaster, toothbrush, you name it—to relay data packets on behalf of any other terminal.
By off-loading local traffic from the Internet, mesh networks would free up bandwidth for long-distance services, such as IPTV, that would otherwise require costly infrastructure upgrades. These networks would also add routing pathways that bypass bottlenecks.
To handle data and terminals of many different kinds, it is suggested that the routers (including the terminals themselves) use methods for building and selecting data pathways borrowed from a complex network that already exists in nature: the human autonomic nervous system.

Comparing a complex system to a human body is a popular metaphor. The human body system controls breathing, digestion, blood circulation, body heat, the killing of pathogens, and many other bodily functions. It does all of this, as the name suggests, autonomously—without our direction or even our awareness. Most crucially, the autonomic nervous system can detect disturbances and make adjustments before these disruptions turn into life-threatening problems.
In fact, the parts of the brain that control this process rely on a multitude of inputs from many subsystems, including taste, smell, memory, blood flow, hormone levels, muscle activity, and immune responses. Does the food contain harmful bacteria that must be killed or purged? Does the body need to conserve blood and fuel for more important tasks, such as running from an enemy? By coordinating many different organs and functions at once, the autonomic system keeps the body running smoothly.
By contrast, many current systems (the Internet included) address a disturbance, such as a spike in traffic or a failed node, only after it starts causing trouble. Routers, servers, and computer terminals all try to fix the problem separately, rather than work together. This often just makes the problem worse rather than to fix it.

One idea, proposed by IBM, is the Monitor-Analyze-Plan-Execute (MAPE) loop, or more simply, the knowledge cycle. Algorithms that follow this architecture must perform four main tasks:
First, they monitor a router’s environment, such as its battery level, its memory capacity, the type of traffic it’s seeing, the number of nodes it’s connected to, and the bandwidth of those connections.
Then the knowledge algorithms analyze all that data. They use statistical techniques to determine whether the inputs are typical and, if they aren’t, whether the router can handle them.
Next, they plan a response to any potential problem, such as an incoming video stream that’s too large. For instance, they may figure the best plan is to ask the video server to lower the stream’s bit rate. Or they may find it’s better to break up the stream and work with other nodes to spread the data over many different pathways.
Lastly, they execute the plan. The execution commands may modify the routing tables, tweak the queuing methods, reduce transmission power, or select a different transmission channel, among many possible actions.

An integrated building energy technology advanced system architecture (ASPA) can utilize some of these principles above.  For example, above mentioned curtains can react on the level of light and open when needed, but also close when the temperature in the room is raising due to excessive sunlight approaching the limit when air-conditioning needs to be turned on.  In the hydronic systems, why do we need to maintain the maximum level of the temperature in the storage tank during the period of low or no demand for hot water - only to waste energy to the heat loss? Instead, the system can monitor the pattern of daily use and predict the demand, reducing the energy use to the minimum. Outside temperature follows the pattern but can be of course a subject of significant variations. A combination of  knowledge-based algorithm with a feedback loop including adaptive capability would control the air circulation. An irradiation sensor can tell the solar thermal collector if it needs to adjust the flow rate in the system in order to either increase efficiency or prevent overheating.
     
An evolution of this system architecture, Advanced Sustainable Control Energy Network Technology, will be key to keeping the system in check. Not only will it help prevent individual components from failing, but by monitoring data from neighboring nodes and relaying commands, it will also create feedback loops within the local network. In turn, these local loops swap information with other local networks, thereby propagating useful information across the Sustainable Network.