Introduction to the Internet of Things (IoT) & Machine-to-Machine (M2M) Industry

To put the massive scale and potential of Internet of Things (IoT) technology in perspective, consider the following: In a world of roughly 5.2 billion people with access to the internet (about two-thirds of the world’s population), about six times as many (29.3 billion) objects will be connected to the internet by 2023, according to projections by network device firm Cisco. Over the very long term, trillions of objects, points of geography, buildings, devices, appliances, vehicles, etc. may eventually be communicating with each other and with major computer systems via networks, fixed or wireless, over the IoT. Spending on IoT-specific hardware, software and services worldwide was estimated at $262.5 billion for 2023 by Plunkett Research.

Selected Technologies that Have Deep Synergies with the IoT & Data Analytics

Artificial Intelligence (AI)

Autonomous Vehicles and Intelligent Transportation

Robotics and Automation

Cellular Telephone Networks and Smartphones

Sensors of Most Types

Smart Meters and Energy Systems

Wi-Fi and Wireless Networks

RFID

Predictive Analytics

Satellite Communications

Big Data and Data Mining

Cloud Computing

Machine to Machine (M2M) Communications

Source: Plunkett Research, Ltd.

How the IoT Works: Simply put, IoT is a method of enabling objects to communicate with each other. Most aspects of this process can also be described as machine-to-machine (M2M) communications. A network of some sort is always involved, thus the use of the descriptor “internet of things.”

The IoT “Network of Networks”

Eventually, IoT will become a massive, all-encompassing “network of networks.” That is, IoT networks that will be based within individual home Wi-Fi networks, individual business locations, plus the networks within organizations and systems in education, health care, transportation, the environment, the supply chain, the industrial base, government, etc. will interconnect and communicate with each other in myriad ways.

The global technology sector has long recognized what is called the “network effect.” This phrase evolved to define the fact that the value of a network increases exponentially as the number of users (people, devices, sensors) on the network grows. For example, an early, developmental network that enabled only a few people to email each other was of limited value. On the other hand, once the global email network grew to encompass billions of users, it was of extraordinary value. That’s the track that IoT is on today—a limited number of devices in the network that will, over time, grow to hundreds of billions of devices, thereby creating extraordinary value and efficiency.

Imagine a modern cargo ship, a behemoth that may transport 18,000 cargo containers plus crew members over long distances. The vast number of complex on-board machines and systems are both mission-critical and subject to potential failures. Consequently, the use of digital, M2M communications via an on-board network is a logical way to keep the ship operating smoothly while ensuring safety and efficiency. Sensors can gather multiple points of data on the massive engines that power the ship—temperature, oil condition, fuel use and vibration. Such sensors might detect vibrations that indicate a potential bearing failure that could lead to an engine shut down. Preventative maintenance could be planned.

Other sensors might determine that the air conditioning system is having difficulty maintaining the correct temperature in the crew’s quarters. Yet another sensing system might detect a leak in a fuel bunker. Continuous gathering and analyzing of such data can not only boost safety but also lead to savings of massive amounts of time, fuel and expense. Crew members on board can take appropriate preventive actions in many cases. Other situations may require dockside repairs. At the same time, imagine this ship communicating its location, speed and direction 24/7 to other ships nearby for safer passage. This occurs today via AIS (Automatic Identification Systems).

Now, imagine the same types of sensors applied to a manufacturing plant, a refinery, an airport, a commercial airliner, a hotel or a shopping mall. IoT will be utilized in all types of facilities and systems.

Finally, imagine the remote gathering of such data from an entire fleet of cruise ships, aircraft, refineries or manufacturing facilities. Highly advanced computers at company headquarters can analyze massive amounts of data coming in from thousands of remote facilities at once, apply AI to the data and make predictions (“predictive analytics”) about changes needed or future equipment problems. Analysis of IoT data can show what challenges loom, what supplies are needed, which items of equipment need repair or replacement, or which theoretical new ideas will prove to be effective once they are applied to real-life situations.

The larger the pool of data, the more observable the patterns and the better the accuracy and outcomes of the process. IoT data is limited only by the number of sensors that are embedded in a system or structure, and the price and capabilities of such sensors are improving constantly.

IoT isn’t only about industrial or transportation systems. It can also be applied to highly personal situations. For example, watch-like devices, worn on the wrist, can gather a wearer’s information such as heart rate, exercise and sleep. The information can be transmitted to systems in the cloud where the wearer’s health can be monitored 24/7. M2M systems in sophisticated parking lots are already alerting drivers to the locations of empty parking places. “Smart meters” that monitor electricity usage at homes are sending usage data to electric utilities’ headquarters utilizing IoT. In fact, this was the first widespread home application of IoT.

At a 2019 National Hockey League game, chip-maker Intel attached sensors to pucks as well as to players’ uniforms. This enabled viewers with smartphones to watch enhanced real-time stats, such as how fast a puck is traveling or how long it took a player to move from one end of the rink to the other. Eventually, advanced sensors may become standard at major league games, enabling fans to enjoy a radically altered viewing experience.

One of the more promising advancements is the synergy between M2M communications and a technology known as “machine learning” (one of the keys to AI). In 2014, Google spent nearly $600 million to acquire UK-based DeepMind, an intensive machine learning research group. The main point is that software can be trained by being constantly fed data, queried as to its meaning, and then receiving feedback a machine’s decisions. It is essentially training a machine to respond correctly to data of a given nature or to data within a given set of circumstances. This makes M2M the vital link in many types of machine learning efforts, because M2M is the first step in gathering many types of data.

Industry Sectors with Significant Near-Term Benefits from AI, IoT and M2M

Health Care

= Remote gathering and monitoring of patients’ vital signs

Agriculture

= Remote gathering of field conditions, eventually even down to the square meter basis, for monitoring of moisture/irrigation, sunlight and nutrients

Transportation

= Remote gathering of and feeding data to transportation planners, control systems, AI platforms and predictive analytics systems

= Utilizing RFID and similar systems plus M2M to track containers and shipments

= Creating M2M Intelligent Transportation Systems (ITS)

= Providing traffic flow management

= Enabling self-driving cars and trucks—instantaneous analysis, over IoT, of real-time data is vital to self-driving vehicles

= Optimizing operations for aircraft, truck fleets, railroads and ships—monitoring engine conditions and fuel usage, and predicting needed maintenance or possible failures

= Remote monitoring of vibration, stress and other conditions in bridges and other infrastructure

Energy Efficiency and Production/Environmental Controls

= Remote gathering of and feeding environmental and energy production/usage data to control systems, AI platforms and predictive analytics systems

= Monitoring a wide variety of local conditions in order to create technologically advanced “smart cities” and green buildings

= Improving energy efficiency in air conditioning, lighting and other systems

= Improving operations and outcomes at all types of energy production operations, from selecting better sites for drilling oil wells to gaining optimum output from windmills and refineries

= Enhancing air and water quality monitoring and control; creating water usage efficiencies

Manufacturing

= Remote gathering of and feeding plant, equipment and operations data to control systems, AI platforms and predictive analytics systems

= Monitoring of manufacturing equipment conditions such as temperature, vibration, energy efficiency, materials input and product output

= Reducing plant downtime and operating costs

= Increasing efficient use of materials and personnel

= Optimizing actions of robotic equipment

Supply Chain

= Utilizing RFID and similar systems for remote gathering of and feeding data to control systems, AI platforms and predictive analytics systems

= Monitoring changes in inventory

= Optimizing the timing of orders and shipping

= Reducing inventory wastage and delays

Source: Plunkett Research Ltd.

SPOTLIGHT: Internet Protocol Version 6 (IPv6)

Internet Protocol Version 6 (IPv6) is the latest generation of IP standard. Individual IP addresses are key to the operation of the internet, as they are required for each device, smartphone, tablet, computer, sensor, etc. that is connected to the internet. IPv6 is intended to first work with, and eventually replace, IPv4. Version 6 will enable a vastly larger number of devices to each utilize one internet address (an IP address) at one time. Specifically, it will allow for 340 trillion, trillion, trillion addresses. It is vital to the implementation of the immense number of sensors that will eventually be connected to IoT.

The rapid growth of cloud computing at reasonable cost has been among the biggest accelerators to the development of IoT. The rapidly growing need of AI systems for data provides another big boost. Today, massive investments in research, development and applications of IoT, AI and machine learning are being made by government and industry on a global scale. For example, in 2018 Samsung announced it would invest more than $22 billion over the following three years in development of advanced technologies, including AI.

Elsewhere, the semiconductor industry is especially focused on creating advanced chips capable of delivering on the full potential of IoT and AI. An ever-accelerating amount of data to be gathered by IoT and then filtered and analyzed via machine learning and AI will require ever more powerful chips that can operate at blinding speed. Intel, AMD and other leading chip makers are in a race to create the industry’s best semiconductors for AI computing

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