Co-authored with Daniel Eckert
While the Internet of Things (IoT) accounts for approximately 1.9B devices today, it is expected to be over 9B devices by 2018—roughly equal to the number of smartphones, smart TVs, tablets, wearable computers and PCs combined. But, for the IoT to scale beyond early adopters– it must overcome specific challenges within three main categories: technology, privacy/security, and measurement. Following are 12 hurdles that are hampering the growth of the IoT:
1) Basic Infrastructure Needed--IoT technology is still being explored and the required infrastructure must be developed before it can gain widespread adoption. The cost of sensors also needs to shrink for usage to expand into mid-market companies.
2) No Standards – Interconnectivity between platforms is now only starting to emerge. (i.e. I want to turn my lights on when I walk in the house and turn down the temp, turn on some music, and lock all my doors – that’s 4 different ecosystems, from 4 different manufacturers.) Competing protocols will create demand for bridge devices.
3) Security Immaturity – Many products are built by smaller companies and/or in open source environments that do not have the resources or time to implement the proper security models. A recent study shows that 70% of IoT devices are vulnerable to hacking. No IoT-specific security framework exists yet; however, the PCI Data Security Standard may find applicability with IoT or the National Institute of Standards and Technology (NIST) Risk Management Guide for ITS.
4) Physical Security Tampering: IoT endpoints are often physically accessible by the very people who would want to meddle with their results: customers interfering with their smart meter, for example, to reduce their energy bill or re-enable a terminated supply.
5) Privacy Pitfalls: Similar to Big Data, privacy risks will arise as the endpoints within the IoT collect and aggregate fragments of data that relate to their service. The collation of multiple points of data can swiftly become personal information as events are reviewed in the context of location, time, recurrence, etc.
6) Data Islands – If you thought Big Data was big, you haven’t see anything yet. The real value of the IoT is when you overlay data from different things – but right now you can’t because devices are operating on different platforms (see #2) – Consider that the connected house generates 200MB of data a day and it’s all contained within data silos.
7) Information, but Not Insights – All the data processed will create information, eventually intelligence – but we aren’t there yet. Big Data tools will be used to collect, store, analyze and distribute these large data sets to generate valuable insights, create new products and services, optimize scenarios and so on. Sensing data timely and accurately is only half of the battle. Data needs to be funneled into existing back-end systems, fused with other data sources, analytics and mobile devices and be made available to partners, customers and employees.
8) Power Consumption and Batteries– 50 billion things are expected to be connected to the Internet by 2020 – how will all of it be powered? Battery life and consumption of energy to power sensors and actuators needs to be managed more effectively. Energy used for wireless network communications often dominates overall power consumption at the endpoint, so wireless protocols and technologies optimized for low data rates and low power consumption are important. Three categories of wireless networking technologies are either available or under development that are better suited for IoT, including Personal area networks, longer-range sensors and mesh networks and application-specific networks.
9) New platforms with New Languages/Technology—Many companies lack the skills to capitalize on the IoT. IoT requires a loosely coupled, modular software environment based on Application Programming Interfaces (APIs) to enable endpoint data collection and interaction. Emerging Web platforms using RESTful APIs can be used to simplify programming, deliver event-driven processes in real time, provide a common set of patterns and abstractions and enable scale. New IDEs, search engines and APIs are emerging to facilitate rapid prototyping and development of IoT applications.
10) Enterprise Network Incompatibility— Many IoT devices aren’t manageable as part of the enterprise network/infrastructure. Enterprise-class network management will need to extend into the IoT-connected endpoints to understand basic availability/uptime of the devices as well as manage software and security updates. While we don’t need the same level of management access as we do to more sophisticated servers, we do need basic, reliable ways to observe, manage and troubleshoot. Right now we have to deal with manual and runaway updates. Either there’s limited or no automated software updates or automatic updates with no way to stop them.
11) Device Overload: Another issue is scale. Enterprises are used to managing networks of hundreds or thousands of devices. The IoT has the potential to increase these numbers exponentially. So the ways we currently use to monitor and manage will need to be revisited.
12) New communications and Data Architecture– As a means of preserving power consumption and driving down overall cost, IoT endpoints are often limited in storage and processing capabilities. Endpoints that push raw data to the cloud allow for additional processing as well as richer analytics by aggregating data across several endpoints. In the cloud a context computer can combine endpoint data with data from other services via APIs to smartly update, reconfigure and expand the capabilities of IoT devices.
The IoT will be a multi-trillion industry by 2020. For that forecast to materialize, some of that money will flow in the pockets of entrepreneurs who can clear the hurdles that threaten to rob the IoT from reaching its full potential.
Image shared by madhan r