IoT Part 3: What are the barriers to the Internet of Things?

The vision of the IoT that captures futurists is one where everything is interconnected. However there are some major obstacles to this vision being realised – privacy, security and transferring decision-making to machines.

A visual metaphor for IoT barriers

The current stink over the US National Security Agency’s gathering of ‘meta-data’ from telco’s highlights the potency of the data privacy issue in terms of both hazard and outrage. People should be concerned about who has their personal information and what they do with it. But the outrage over what the U.S. Government is doing should be a prompt for people to consider who else has their personal information and under what terms. Furthermore, there needs to be greater awareness of the value of this private information, as huge businesses have evolved to take advantage of valuable data that people are providing effectively for free. Yep, that’s a multi-faceted rant only partly related to the issue at hand.

This is background to the real issue for the IoT, in that much of the data that is of value must be gathered within privacy constraints. Locational data for private individuals is both highly valuable and highly sensitive – this is why many smartphone applications will prompt users to acknowledge when their location is to be shared. For sensors which do not have a smartphone-like interaction with users, this acknowledgement is difficult to obtain, effectively preventing a more widespread rollout OR promoting less-scrupulous behaviour in terms of monitoring without permission. As an outcome, there are constraints on IoT businesses in terms of:

  • What data they can capture without infringing upon people’s privacy
  • Undertaking the challenge in gaining people’s (conscious) endorsement for use of their data
  • Putting their business at risk of people’s outrage once they suspect their data is being captured or used against their wishes (regardless of whether this is the case)

Security is a similar issue but from the data gatherer’s perspective – what people/entities are going to gain access to their systems and what are they going to do when they get there? As an outcome, the IoT utopia of total connectivity is (rightly) impaired by protections applied to various data interactions.

As an example of the parallel nature of the privacy/security issues, consider the issue of home security. The perfect IoT solution may be a keyless security system that simply recognises the rightful occupants as they pass in an out of the residence, likely via their smartphone. This system may use a smartphone app that would monitor the movement of the residents – in a worse-case scenario this information may be used by organised crime to either gain access to unoccupied residences or even to facilitate an insurance fraud.

Verizon already offer a remote home security solution linked to an individual’s smartphone, which brings us to the next constraint – to what extent are people comfortable with handing control over to the machines?

For systems with national security implications this has long been a challenge that is dealt with by retaining ultimate control at human level. In the doomsday scenario, we simply can’t have the future of the planet being decided by a machine code “if/then” statement. By extension, individuals, companies and governments are unlikely to cede ultimate control over to machines – predominantly due to the perceived or real risks outlined above relating to privacy and security.

The outcome from this is a proliferation of standards and operating systems that are unique to each industry sector/application, that address issues specific to these sectors/applications, resulting in major obstacles to streamlined communication between devices, particularly at the total system level.

As the practical level I find that this translates to sector-specific solutions that may be addressed through integration hubs, but more often stop far short of the IoT vision that some would have you believe.

There’s still plenty of opportunity in the space however, which will be the subject of my next IoT post.

Advertisements

IoT Part 2: So why the interest in the Internet of Things?

This is a fairly simple post as there are three main drivers for the sudden interest in the IoT – in no particular order:

  1. Sensor module costs
  2. Wireless networks
  3. Technology trends
A microsensor with coin for scale  this is a titan compared to the sensors that can be injected into your body for medical monitoring (credit: beob8er, Flickr)

A microsensor with coin for scale – this is a titan compared to the sensors that can be injected into your body for medical monitoring (credit: beob8er, Flickr)

Sensor modules are the electrical circuitry that incorporates the transducers that sense the information and the communications chips that send it.  Other solid-state components that perform various processing, decision-making and response functions can also be added in as required, but it’s the cost reductions of the sensors and comm’s chips that are having the most effect on the IoT market.  McKinsey recently stated that sensor/actuator module costs had dropped 80 – 90% in the last 5 years, while the Financial Times recently said that sensor/comm’s modules had dropped from 50 to 15 Euro over the last 4 years.

Wireless networks have become ubiquitous and low cost.  The coverage of fast 3 and 4G networks has supported the phenomenal uptake of smartphones, and is a key enabler for the IoT.  The volume of data generated by all these connected devices may ultimately mean that bandwidth becomes a key IoT constraint – speaking from personal experience as someone who has instrumented a fleet of 40 cars to capture around 20 data fields at 5 second intervals over months at a time, the costs associated with this can still be prohibitive even if the situation is improving.

Technology trends towards cloud computing and big data analytics (amongst others) are also key IoT enablers.  The ability host data and process it away from connected devices significantly reduces costs and lifts system performance to useful or highly profitable levels.  And the ability to handle all this data and make sense of it is also becoming more widespread.  The importance of big data analytics is clear when it is considered that a Boeing 787 aircraft generates around half a terabyte of data for every long-haul flight (Financial Times 2013).

Next post – the key challenges.

IoT Part 1: What is the Internet of Things?

This post is the first in a series exploring a technology buzz phrase – the Internet of Things.

Through this series I’ll present answers to the obvious questions about the Internet of Things marketplace, following which I’m hoping to figure out how to upload a document that pulls all the separate posts together for people to download as required.

The Internet of Things, or IoT, has been getting a bit of press lately – from the hacker level who are tinkering with sensor boards and networking devices in their home, to the biggest corporations who are spruiking mind-boggling figures regarding the IoT market potential.

The most basic definition of the IoT is the networking of physical objects through the use of sensor/comm’s modules.  This definition can be expanded to include much greater levels of integration between networked devices, so that the various turns of phrase shown below describe the varying degrees to which objects can capture, communicate and respond to information.

The various terms being used to describe the 'Internet of Things'

The various terms being used to describe the ‘Internet of Things’

The variation between definitions often reflects how far this networking of objects has progressed along the stepwise path towards the final step that is the ultimate IoT vision:

  1. Connecting devices to the network (‘Smart’ devices)
  2. Making those devices talk to each other (if/then linkages)
  3. System-level interaction between devices (control strategies using analytics that draw on multiple data sources – historical, real-time and projected)
I’ve developed this understanding based upon a review of definitions I’ve collated from various information sources below:
  • ‘Smart’ devices defined by the presence of microprocessors (1)
  • Billions of connections that will encompass every aspect of our lives (1)
  • The interaction and exchange of data between machines and objects (1)
  • Smart machines interacting and communicating with other machines, objects, environments and infrastructures resulting in volumes of data generated and processing of that data into useful actions that can “command and control” things and make life easier for human beings (1)
  • Objects/things that are connected to the Internet, anything, anytime, anywhere (2)
  • Sensors and devices embedded into everyday objects/things that are connected to Internet through wired and wireless networks (2)
  • Networks of low cost sensors and actuators for data collection, monitoring, decision making, and process optimisation (3)
  • The use of sensors, actuators, and data communications technology built into physical objects – from roadways to pacemakers – that enable those objects to be tracked, coordinated or controlled across a data network or the Internet (3)
  1. What the Internet of Things (IoT) needs to become a reality, Karimi, K. and Atkinson, G., Freescale and ARM, Sept 2012 (pdf)
  2. Global Internet of Things (IoT) & Machine-To-Machine (M2M) Communications Market worth $290.0 Billion by 2017, Markets and Markets, 4 Sept 2012
  3. Disruptive technologies: Advances that will transform life, business and the global economy, McKinsey Global Report, May 2013 (pdf)
My next IoT post will examine the IoT enablers and/or challenges.