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The implications of large IoT ecosystems

TechCrunch TechCrunch 22/06/2016 Ben Dickson

The Internet of Things genie is out of the bottle and growing at an accelerating pace. According to Gartner, 6.4 billion connected things will be in use worldwide in 2016, up 30 percent from 2015. This number will soar to more than 20 billion by 2020.

Others present even higher estimates.

The opportunities in improved utility, energy-saving, efficiency and safety lying in the data gathered by such immense numbers of connected sensors and smart devices are huge and without precedent.

However, the challenges that come with the chaotic growth of IoT are also new and, in some cases, unfamiliar, and not knowing and anticipating them can slow down the process — if not halt it altogether.

Here are some of the changes we’ll face as IoT becomes more ingrained in our lives — and how the tech community is getting prepared to deal with them.

Device authentication and authorization

Identifying devices within a network is key to securing IoT ecosystems and preventing the infiltration of intruders. In current solutions, device authentication and authorization is mostly carried out through centralized cloud-based servers, which is perfectly viable in small-scale IoT networks where dozens of nodes are involved. But as ecosystems start to grow and thousands and millions of sensors and gadgets enter the fray, authentication can become a bottleneck, especially if the network loses internet connection for any amount of time.

“Most people don’t understand the notion of scale,” says Ken Tola, CEO of IoT security startup Phantom. “Effective security needs to provide a realistic mechanism to control millions of devices.” Which becomes a nightmare with current solutions. “Current options rely on internet connections which kill batteries, overwhelm the extremely fragile mesh networks onto which most IoT systems rely and fail completely when the internet goes down,” Tola explains.

According to Tola, the solution is to move much of the functionality to the edge, between devices themselves. “Working in a peer-based manner makes it much easier to handle scale,” he says. “No matter how big a system is, when authentication/authorization takes place between devices, it can happen simultaneously across millions of devices without requiring internet access, heavy network loads or any other burdensome features.”

The increased number of IoT devices can quickly turn into a management nightmare.

Tola’s startup has worked on a lightweight, scalable solution for M2M (machine-to-machine) connections with minimal internet connectivity required. Phantom is an invisible (thus the name) smart security layer that sits between a device and any connected network; it is able to securely identify two devices in a peer-based relationship, authorize the levels and types of communication and secure the conversations between those two devices.

Local networks are secured through policies stored in nodes. Policies can be distributed from the cloud or delivered through Bluetooth or direct USB sticks. Strong hash chaining techniques ensure the safe transmission of new policies. “Leveraging this local validation system, we can finally provide truly effective security at scale, and securely control millions of devices,” says Tola.

Wireless communications

At its core, the Internet of Things is the extension of worldwide web connectivity from our computers to devices and sensors that surround us. And for the most part, that connectivity has to be wireless, especially in the personal area network, i.e. the ensemble of wearable, portable and implanted devices we carry on our person.

Most IoT devices rely on radio frequency (RF) technology such as Bluetooth, ZigBee and Wi-Fi for communications. Otherwise known as far-field transmission, RF is great when communicating over long distances, but becomes problematic when applied to short-range, isolated IoT ecosystems, like the wireless personal area network.

“Link and network security become increasingly difficult as the number of any RF devices increases,” says Dr. Michael Abrams, CEO of FreeLinc, a research and development corporation. “Handshake and encryption protocols are projected into free space, and the requirement for decreasing power consumption in Wearables translates to less room for encryption protocols. These issues are clearly reflected by Bluetooth’s increasingly poor reliability and security record.”

Already, RF-based devices are shutting each other down due to interference, a situation that will grow worse when the IoT industry grows by the billions.

One solution would be “to get the FCC to allot additional bandwidth — such as 5GHz for Wi-Fi, but with trillions of projected devices to eventually connect, the problems will recur,” says Dr. Abrams.

Another problem in RF-based wireless communications is power consumption, which becomes a growing issue as more devices are added to IoT space, especially as many are powered by batteries and will be deployed in unattended environments.

An alternative, says Abrams, is to substitute Near Field Magnetic Induction (NFMI) for RF. NFMI uses the modulations of magnetic fields to transfer data wirelessly between two points. Its main strength is its attenuation. It decays a thousand times faster than RF signals, which eliminates much of the interference and security issues that are attributed to technologies such as Bluetooth.

The transformations overcoming the Internet of Things will have huge implications.

“NFMI solves many of these problems,” Abrams says. “It creates a wireless ‘bubble’ around the user, in which multiple devices can connect, and outside the signals cannot be seen. Security protocols exist only within the bubble and are not projected into free space. The rapid signal decay allows the same frequency to be used just a short distance away, practically eliminating the possibility of spectrum contention.”

NFMI is based on the same principle as Near Field Communication (NFC), which is found in all new-generation smartphones, but it extends the reading distance from 1-4 inches up to 9 feet and offers a 400 Kbps data transfer rate.

NFMI has been used in hearing aids, pacemakers and mission critical communications for more than a decade. Abrams believes it will prove its worth in a new way in the age of IoT.

Device and traffic administration

The increased number of IoT devices can quickly turn into a management nightmare. In order to make the best use of the increased utility provided by the millions of smart meters, parking and lighting sensors, traffic controls, crowd movement detection sensors and many other gadgets that are scattered across smart homes and cities, you need to be able to efficiently control their traffic and functionality. Administration, integration and connectivity should be as simple as possible and require the least amount in human intervention.

“Until now the IoT networks were not management or capacity constrained,” says Yegor Popov, co-founder and CEO of WAVIoT, an IoT networking startup. “However, one of the biggest challenges in IoT networks today is the number of different smart devices and sensors transmitting and receiving data at the same time. This challenge needs to be addressed in order to keep the network under control.”

Popov specifically alludes to Low-Power Wide Area Networks (LPWAN), where huge numbers of devices will soon share the same network in cities. Increased numbers of nodes will require real-time re-allocation of resources in order to improve efficiency and prevent interference.

“Based on analysts’ predictions, by 2023 we will connect to the internet an additional 40 billion devices,” says Marat Zaripov, WAVIoT’s CMO. “15 billion of those are estimated to be LPWAN. It is 6 million new LPWAN devices per day. And if these predictions are even remotely correct, then network management will become one of the key issues.”

“A human cannot control a billion nodes connected in a wide-area,” Popov explains. He plans on solving the problem through a technology that has become the buzzword in many industries: artificial intelligence.

WAVIoT is using machine learning for the development of a dynamic, automated network management framework. Their proprietary algorithm, dubbed Albert, provides real-time distributed system control and self-management capabilities for huge long-range IoT networks consisting of billions of smart devices and sprawling across millions of square miles. The system uses trained neural networks and Bayesian methods to optimize the interaction of nodes and IoT gateways on the network.

Popov calls it “a decentralized living organism which can adapt itself based on machine learning algorithms to provide optimal operation for the entire network.”

Albert will meet its first challenge in a real-life project in Sofia, the Bulgarian capital, where it will handle more than a million different smart city devices.

Embracing the change overcoming IoT

In many ways, the transformations overcoming the Internet of Things will have huge implications. Many of the technologies we use will evolve and adapt to support the needs of an increasingly connected world. Others that don’t will be buried under the weight of billions of connected devices and replaced by new technologies that are ready to take on the challenges introduced by the explosion of IoT. Scalability will be the key to winning in this game of survival.

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