The Internet of Things (IoT) is a form of ubiquitous computing that weaves together an interconnected fabric of embedded devices into a holistic sensory ecosystem. The next generation of machine-to-machine (M2M) computing is dramatically different compared to what we have seen for the past decade when using centralized cellular networks. IoT has some very different attributes, including:

  1. Federated architecture
  2. Peer-to-peer networks
  3. Client / Server networks
  4. Decentralized intelligence
  5. Low data rate devices
  6. Low power devices
  7. On demand computing with device sleep dwell times
  8. Real-time and near real-time communications
  9. A varied and vast array of sensors for monitoring, control, telemetry, and diagnostics

Figure 1 – IBM Infographic for IoT and Sensors

Beyond the IoT network, what is also growing exponentially is the option for new and unusual instrumentation at the end devices. Of course, we still have smart nodes that perform specific functions, like contact closures or firing off simplistic serial command strings, but we now have a huge array of microcomputers, sensors, and specialized devices that perform a myriad of applications. These new devices can be installed at the network’s edge to greatly enhance the capabilities of the applications by providing new data inputs for better decision-making.

Figure 2 – Intel IoT Gateway with Sensors

Unlike the older centralized M2M networks, we are now taking advantage of the federated aspect of the network and therefore we are seeing the application intelligence pushed out to the network’s edge too. The federated model has a layered approach that can “make and break” as required to combine and disband layers and therefore functions and features of the network. By distributing the network intelligence over the end to end network topology, we are adding fresh capabilities and supplementing the existing functions.

Figure 3 – In-Jet ApS’ IoT Federated Architecture

Centralized intelligence is still critical to IoT and is not going away, but it is being radically improved with dispersed intelligence. By concentrating portions of the application intelligence at the boundary, we can now generate derived data with perimeter devices chatting to other periphery devices thus calculating new information for superior study. This new information may be located only at the edge and may never be called for to traverse the network to the core application. It will have a finite lifespan and be generated upon demand and then be discarded once consumed.

Figure 4 – Arduino Weather Sensors

From a smart grid perspective, these new devices are falling into several categories. The number of options per category is extensive. So, here is a snapshot of some of the subcategories within the various categories of edge devices used to instrument the next generation IoT network for smart grids.

  • Motion – detection of and characteristics of motion from accelerometers
  • Location – height, proximity, alarms, Doppler positioning
  • Parameter – position, angle, distance, speed, acceleration,
  • Acoustic – microphone, lace sensor (like a guitar pick-up of line vibrations)
  • Chemical – odour detection (burning), ozone detection, smoke detector, gas detection
  • Telemetry – gyroscope, fluxgate compass, magnetic compass, inertia, altimeter, attitude
  • Values – tilt, maximum / minimum values, rate
  • Environment – temperature, humidity, barometric pressure, rainfall, dew
  • Flow – values, maximum / minimum values, rate of flow, alarms
  • Navigation – position (latitude / longitude), altitude, direction, time
  • Optical – infra-red, LED light detection, colorimeter, photodetector, scintillation detection
  • Pressure – flow, boost, pressure, barometer
  • Force – density, level, load,
  • Visual – size, colour, shape, speed, recording,

Again, the number of different kinds of sensors is large, and the aggregation of data from an assortment of sensors can derive data that is not actually sensed, but is computed from the outputs of various sensors to conclude new data. The applications available to the smart grid designer are extensive and almost unlimited. For example:

If we wish to monitor powerlines, then the key parameter to consider is current. Other important parameters can also be measured with sensors, such as: temperature of the conductor, temperature of the air around the right of way, wind speeds, sway in the transmission lines due to wind, sag in the transmission line due to temperature, voltages, electromagnetic fields, differential between conductors / phases, power on the neutral, and more. Small sensors or packages of sensors can be assembled into devices to perform these functions. The IoT networks can then deliver this data to the controller, which may be located out at the edge, regionally, or at the core of the grid.

All of these parameters can be calculated at an extremely low cost node on the transmission line. Devices may need to cost just a few dollars or nodes might cost less than $100. These nodes can be linked together with IoT network topologies.

Figure 5 – IBM MoteRunner

There are some innovative, low cost tools for developing solutions using some of the off-the-shelf do-it-yourself (DIY) platforms, such as Raspberry Pi and Arduino. These sub $100 computing platforms can be packaged in a weatherproof housing with a radio and made operational with minimal coding and development. Some DIY projects are already installed today and delivering data back to the Utility for analysis.

Powering of these sensors and DIY platforms can be done with batteries or with alternate powering sources. Energy harvesting (aka scavenging) is the process by which energy is derived from external sources (e.g. solar, thermal, wind, salinity gradients, and kinetic), captured, and stored for small, wireless, autonomous devices, like those on Utility wireless sensor networks. Scavenging ambient power drawn from the energy pulsing through the transmission lines is still in its infancy, but induction powering holds promise. With many of these new power sources, energy storage becomes a key component of the strategy. Since these sensors and DIY platforms draw so little power, the size of these power solutions in compact and manageable.

Figure 6 – ARM ecosystem

The ARM processor is the main processing platform for IoT. According to Stroud (2014), the ARM processors are a family of 32-bit microprocessors developed by Advanced RISC Machines, Ltd. in the 1980s. Today ARM processors power a wide variety of electronic devices, including mobile phones, tablets, multimedia players and more. ARM processors are based on a reduced instruction set computer (RISC) architecture, and while they do share the low-end market with processors from AMD and Intel, they aren’t designed to compete with these companies’ higher-end processors. With more than 50 billion ARM processors sold to date, it is ubiquitous with mobile and small footprint computing. There are a variety of ARM processors available to the designer and this allows the integration of several applications on the same device – main IoT applications, security, routing, analytics, and more. ARM has more than 1,000 partners integrating their processor into devices. By using the ARM processor and the ARM architecture for IoT development, this permits the use of an open, web based suite of standards.

The future is bright for IoT and for the vast assortment of sensors, processors, and devices to instrument the IoT networks is ever-expanding and the depth of functionality and the breadth of performance is stunning.

—————— MJM ——————

Michael Martin has more than 35 years of experience in broadband networks, optical fibre, wireless and digital communications technologies. He is a Senior Executive Consultant with IBM’s Global Center of Excellence for Energy and Utilities. He was previously a founding partner and President of MICAN Communications and earlier was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation. He holds three Masters level degrees, in business (MBA), communication (MA), and education (MEd). As well, he has diplomas and certifications in business, computer programming, internetworking, project management, media, photography, and communication technology.