Our Hyperconnected IoT World

The Internet of Things (IoT) is an emerging topic of technical, social, and economic significance. In plain English, the concept can be explained as the “extension of network connectivity and computing capability to objects, devices, sensors, and items not ordinarily considered to be computers” (Rose 3). While the concept has existed for quite some time now (ATMs are considered some of the first IoT objects, and went online as far back as 1974) and has been the subject of tech-media excitement, the growing conversation around self-driving cars, which are an example of IoT devices, has brought the Internet of Things to mainstream media.

Currently, there are 18.5 IoT devices circulating in the world.

Morgan Stanley projects 75 billion networked devices by 2020.

McKinsey Global Institute suggests that the financial impact of IoT on the global economy may be as much as $3.9 to $11.1 trillion by 2025.

The statistics paint a picture of expansive growth surrounding IoT. While to many it is still considered a futuristic idea, having the Internet control wearables, cities and our own houses are nearer than we think. We have to understand the Internet of Things, know its powers, and be aware of its pitfalls. As IoT is inching closer to our personal lives, we need to be able to control its effects on us and the world.


At the most fundamental level, the Internet of Things is modeled in four ways:

  1. Device-to-Device Communication. The device-to-device communication model is composed of multiple devices that directly connect and communicate with each other. No intermediary application server is required. A common way of doing this is through protocols like Bluetooth, Z-Wave, and ZigBee.
  2. Device-to-Cloud Communications. This model is similar to device-to-device but includes an intermediary—a cloud server of sorts—between separate devices. One example this model is employed in is Nest Lab’s Learning Thermostat IoT device. The device transmits data to a cloud database, where energy consumption data is obtained and analyzed. The cloud is also connected to a phone device, which simply relays the data and allows the user to change the temperature based on the smart thermostat’s recommendation.
  3. Device-to-Gateway. In the device-to-gateway model the IoT device is connected through an application-layer gateway to a cloud service. Think of a personal fitness tracker — the tracker itself cannot directly connect to the cloud for data aggregation and analysis; it has to rely on a smartphone app (the intermediary gateway) to link to the cloud.

  4. Back-End Data Sharing. This model refers to a communication architecture that enables users to export and analyze smart object data from a cloud service in combination with data from other sources. Instead of looking at separate devices and connecting them to the cloud, this model takes a reverse approach and disaggregates the data from the cloud to track back to the individual IoT devices.


  • Security. An important implication following the Internet of Things is the potential for significant security threats. The age of big data has increased the numbers we collect and the nature of IoT devices, which heighten the opportunity for cyber attacks, data thievery, and hacking. Naturally, the accumulation of data help us improve technology, medicine, education, politics, and more; however, there is always the possibility of such data getting into the wrong hands. Our increased level of dependence on technology and data increases the dangers which come with security threats.
  • Privacy. Data accumulated through and used by IoT devices can provide many benefits to the device’s end-user. Just think of a smart thermometer — by aggregating information on house temperatures, degree of house insulation, and electricity bills, a user can save money and heat/cool their house more environmentally. Frequently, however, this data also benefits the device’s manufacturer or supplier, who both might use it to undergo infringing customer analyses. Some might say individual data points collected about individuals are not enough to breach an individual’s privacy. Just because you know what someone pays on their electricity bills does not mean you can derive anything else about this unknown person. The real problem occurs when individual data points are combined or correlated. Oftentimes, a more invasive digital portrait is painted of the individual than can be realized from an individual IoT data stream. The aggregation of data poses the real privacy danger.
  • Social Implications. An overview of the dangers of IoT cannot be complete without touching on what the future of this technology says about our society. I am not going to lie: I am petrified by it. We are becoming walking data points. IoT has convinced us that we can quantify people and their actions. While more information can be beneficial, we need to remember that we operate in a values-based society with a moral system, not just data points. When making data-based policy and societal decisions, we must be cognisant of this and supplement quantitative evidence with qualitative contextualized information.

While threats exist, I am can’t stop being hopeful. IoT has the potential to (1) generate revenue, (2) make our lives safer and healthier, and (3) let our communities and individuals make better decisions. With understanding and the right amount of caution, we can unlock its potential for positive impact.


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