Sensors: implications for wireless connectivity & video communications

Quick summary

• Sensor technology is complex, diverse, fascinating & fast-evolving.
• There are dozens of sensor types & technologies.
• Nobody believes the 20-50bn devices forecasts, especially if they are based on assumptions that 1 sensor = 1 device
• Some sensors improve the capabilities of already-connected devices, like phones or (increasingly) cars.
• Some sensors enable creation of new forms of connected device & application.
• Most sensors connect first via one or two tiers of local gateways, sub-systems or controllers, rather than directly connect to the Internet / cloud individually
• While the amount of sensor-generated data is growing hugely, not all of this needs real-time collection and analysis, and so network needs are less-extreme.
• Many industrial sensors use niche or unfamiliar forms of connectivity.
• Genuine real-time controls often need sensors linked to "closed-loop" systems, rather than using Internet connections / cloud.
• WiFi & short-range wireless technologies like Bluetooth & ZigBee are growing in importance. There is limited concern about using unlicensed spectrum
• LoRa radios (sometimes but not always with LoRaWAN protocols) are growing in importance rapidly
• Cellular connectivity is important for certain (especially standalone, remote/mobile & costly) sensor types, or sensor-rich complex objects like vehicles. 
• The US seems more keen on LTE Cat-1 / Cat-M than NB-IoT for sensor-based standalone devices. Europe and Asia seem more oriented towards NB-IoT
• There are no obvious & practical sensor use-cases that need 5G, but it will likely improve the performance / economics / reach of some 4G applications.
• Camera / image sensors are becoming hugely important and diverse. These are increasingly linked to either AI systems (machine vision) or new forms of IoT-linked communication applications
• "Ordinary" video sensors/modules are being supplemented by 3D, depth-sensing, emotion-sensing, 360degs, infra-red, microscopy and other next-gen capabilities.
• AI and analytics will sometimes be performed on the sensor or controller/gateway itself, and sometimes in the cloud. This may reduce the need for realtime data transmission, but increase the need for batch transfer of larger files.
• Conclusion: sensors are central to IoT and evolving fast, but the impact on network connectivity - especially new cellular 4G and 5G variants - is diffuse and non-linear.

Narrative
 
A couple of weeks ago I went to Sensors Expo 2017 in San Jose. This topic is slightly outside my normal beat, but fits with my ongoing interest in "telcofuturism", especially around the intersection of IoT, networks and AI. It also dovetails well with recent writing I've done on edge computing (link & link), a webinar [this week] and paper on IoT+video for client Dialogic (link), and an upcoming report I'll be writing on LPWAN for my Future of the Network research stream at STL Partners (link).

First things first: listening to some of the conference speeches, and then walking around the show floor, made me realise just how little I actually knew about sensors, and how they fit into the rest of the IoT industry. I suspect a lot of people in telecoms - or more broadly in wireless networking and equipment - don't really understand the space that well either.

For a start, there's a bewildering array of sensor types and technologies - from tiny silicon accelerometers that can be built into a chip (based on MEMS - micro-electromechanical systems), right up to sensors woven into large-scale fabrics, that can be used to make tarpaulins or tents which know when someone tries to cut them. There's all manner of detectors for gases, proximity, light, pressure, force, airflow, air quality, humidity, torque, electrical current, vibration, magnetic fields, temperature, distance, and so forth.

Secondly, a lot of sensors have historically been part of "closed-loop" systems, without much in the way of "fully-connected" computing, permanent data collection, networking, cloud platforms or analysis. 

An easy example to think about is an old-fashioned thermostat for a heating system. It senses temperature - and switches a boiler or radiator on or off accordingly - without "compute" or networking resource. This has been reinvented by Nest and others. Plenty of other sensors just interact with "real-time" systems - for example older cars' airbags, or motion-detection alarms which switch on lights.

In industry, a lot of sensors hook into the "real-time control" systems, whether that's for industrial production machinery, quality control, aircraft avionics or whatever. These often use fixed connectivity, with a bewildering array of network and interface types. It's not just TCP/IP or familiar wireless technologies. If you haven't come across things like Modbus or Profibus, or terms like RS485 physical connections, you perhaps don't realise the huge complexity and unfamiliarity of some of these systems. This is not telco territory.

This is important, as it brings in an entire new realm to think about. From a telco perspective, we're comfortable talking about the touch-points of networks and IT. We are don't often talk about OT or "operational technology". A lot of people seem to naively believe that we can hook up a sensor or a robot or a piece of industrial machinery straight to a 4G/5G/WiFi connection, then via Internet or VPN to a cloud application to control it, and that's all there is to it. 

In fact, there may well be one, two or three layers of other technology involved first, notably PLC units (programmable logic controllers) as well as local gateways. A lot of this is the intranet-of-things, not the Internet-of-things - and may well not even be using IP as most people in networking and telecoms normally think about it.

In other words, there's a lot more optionality around ISO layers - there are a broad range of sector-specific or proporietary protocols, that control sensors or IoT devices over a particular "physical layer". That contrasts with most users' (and telco-world observers') day-to-day expectations of "IP everywhere" and using HTTP and TCP/IP and similar protocols over ethernet, WiFi, 4G or whatever. The sensor world is much more fragmented than that.

These are some of the specific themes I noted at the event:

• Despite the protocol zoo I've discussed, WiFi is everywhere nonetheless. Pretty much all the sensor types have WiFi connectivity options somewhere, unless they're ultra-low power. There's quite a bit of Bluetooth and ZigBee / other varieties of IEEE 802.15.4 for short-range access too.
• Almost nobody seems bothered about the vagaries of unlicensed spectrum, apart from a few seriously mission-critical, time-critical applications, in which case they'll probably use fixed connections if they can. Bear in mind that a lot of sensors are actually fairly time-insensitive so temporary interference or congestion doesn't matter much. Temperatures usually only change over seconds / minutes, not milliseconds, for example. Bear in mind though, that this is for sensing (ie gathering data) not actuating (doing stuff, eg controlling machines or robots).
• Most sensors send small bursts of data - either at set intervals, or when something changes. There are exceptions (notably camera / image sensors)
• I saw a fair amount of talk about 5G (and also 4G and NB-IoT) but comparatively little action. Unlike Europe, the US seems more interested in LTE Cat-1 and Cat-M rather than NB-IoT. Cat-M can support VoLTE, which makes it interesting for applications like elder/child-trackers, wearable and building security. NB-IoT seems fairly well-suited to things like parking meters, environmental sensors, energy metering etc. where each unit is comparatively standalone, and needs to link to cloud/external resources like payments.
• There's also lot of interest in LoRa, both as a public network service (Senet was prominently involved), and also as privately-owned infrastructure. I think we're going to see a lot of private LoRa embedded into medium-area sensor networks. Imagine 100 moisture sensors for a farm, connected back to a central gateway on top of the barn, and then on to a wide-area connection (fixed or mobile) and a cloud-based application. The 100 sensors don't need a wireless "service" - they'll be owned by the farmer, or else perhaps the connectivity will be offered as a part of a broader "managed irrigation service" by the software company.
• There's an interest in wireless connectivity to reduce regulatory burdens for some sensors. For example, to connect a temperature sensor in an area of an oil refinery with explosion risks, to a server in another building, requires all manner of paperwork and certification. The trenching, ducting and physical wire between them needs approval, inspection and so on. It's much simpler to do it with wireless transmitters and receivers.
• A lot of the extra sensors getting connected are going to be bundled with existing sensors. Rather than just a vibration sensor, the unit might also include temperature and pressure sensors in integrated form. That probably adds quite a lot to the IoT billions number-count, without needing separate network links.
• A lot of sensors will get built into already-connected objects. Cars and aircraft will continue to add cameras, material stress sensors, chemical analysis probes for exhaust gases, air/fluid flow sensors, battery sensors of numerous types, more accelerometers and so on. This means more data being collected, and perhaps more ways to justify always-on connections because of new use-cases - but it also means a greater need for onboard processing and "bulk" transfers of data in batches.
• Safety considerations often come ahead of security, and a long way ahead of performance. A factory robot needs sensors to avoid killing humans first. Production quality, data for machine learning and efficiency come further down the list. That means that connecting devices and sensors via wider-range networks might make theoretical or economic sense - but it'll need to be seen through a safety lens (and often sector-specific regulation) first. Taking things away from realtime connections and control systems, into a non-deterministic IP or wireless domain, will need careful review.
• Discussion of sensor security issues is multi-layer, and encouragingly pervasive. Plenty of discussions around data integrity, network protection, even device authenticity and counterfeiting.
• Imaging sensors (cameras and variants of them) are rapidly proliferating in terms of both capabilities and reach into new device categories. 3D depth-sensing cameras are expected on phones soon, for example for facial recognition. 360-degree video is rapidly growing, for example with drones. Vehicles will use cameras not just for awareness of surrounding, but also to identify drivers or check for attentiveness and concentration. Rooms or public-spaces will use cameras to count occupancy numbers or footfall data. New video endpoints will link into UC and collaboration systems "Sensed video" will need greater network capacity in many instances. [I am doing a webinar with Dialogic about IoT+video on July 13th - sign up here: link]
• Microphones are sensors too, and are also getting smarter and more capable. Expect future audio devices to be aware of directionality, correct for environmental issues such as wind noise, recognise audio events as triggers - and even do their own voice recognition in the sensor itself.
• Textile and fabric sensors are really cool - anything from smart tarpaulins for trucks to stop theft, through to bandages which can measure moisture and temperature changes, to signal a need for medical attention. 
• There's a lot of modularity being built into sensors - they can work with multiple different network types depending on the use-case, and evolve over time. A vibration sensor module might be configurable to ship with WiFi, BLE, LoRa, NB-IoT, ZigBee and various combinations. I spoke to Advantech and Murata and TE Connectivity, among others, who talked about this.
• Not many people seemed to have thought about SIMs/eSIMs much, at a sensor level. The expectation is that they will be added by solution integrators, eg vehicle manufacturers or energy-meter suppliers, as needed.
• AI will have a range of impacts both positive and negative from a connectivity standpoint. The need for collecting and pooling large volumes of data from sensors will increase the need for network transport... but conversely, smarter endpoints might process the data locally more effectively, with just occasional bulk uploads to help train a central system.

Overall - this has really helped to solidify some of my thinking about IoT, connectivity, the implications for LPWAN and also future 4G/5G coverage and spectrum requirements. I'd recommend readers in the mainstream telecom sector to drop in to any similar events for a day or two - it's a good way to frame your understanding of the broader IoT space and recognise that "sensors" are diverse and have varying impacts on network needs.

5G vs. AI

Last week, I was in Mainz in Germany, at a European telecom regulator's workshop about spectrum and technology evolution for future 5G networks. (link). It was a very formal event, with most people from government agencies, technology standards bodies and telcos, broadcasters and the like. Some industry verticals such as energy, rail and automotive were also represented. I was one of the few analysts there - and there were no journalists, I think.

This week, I've been in San Francisco, at a very different style of event, about Artificial Intelligence. (link). It was a multi-streamed conference, with a small expo area, a press office, lively panel sessions - and a selection of Silicon Valley's finest, from VCs to Google to Uber to innovation outposts of GE and Airbus, as well as countless software startups and enterprise IT folk.

I was there as part of my TelcoFuturism research effort (link) where I'm looking at the impact and opportunities of technologies such as AI, blockchain, drones, AR/VR, robotics and quantum computing on the telecoms industry. I was interested to see both internal applications of AI in running telcos' networks and IT systems, and also in terms of scope for new services and driving connectivity.

It's that last thing that struck me most. There is a huge gulf between the expectations of the 5G community (which talks endlessly of self-driving cars and robots using ultra-high performance mobile networks, or "massive Iot" networks of sensors and actuators) and the AI and robotics community (which doesn't).

I asked quite a lot of people developing both AI software (which is a huge diversity from deep-learning, to image-processing, to personal assistants and bots) and hardware and applications (autonomous vehicles, GPUs etc) how important networks were to their innovations.

The general answer: not that much. They want as much processing done on the device itself as possible, not controlled remotely or from the cloud, especially where anything safety-critical is involved. A speaker from Nvidia showed a board that is essentially a vehiclular supercomputer, using inputs from cameras, engine monitoring, LIDAR and all sorts of other sensors to work out what to do. A self-driving car is not going to ask the cloud for permission to brake in an emergency. There is a recognition that networks are not ubiquitous or completely reliable, so they need to act independently - autonomous means autonomous. This also means much lower latencies.

Other companies are working on facial/emotional recognition systems that can be embedded in smartphones, or even directly in camera hardware, without the need for an OS - or sending data to/from the network all the time. The speaker from GE said that aircraft engines may generate terabytes of data during a flight - but have enough onboard intelligence to do analytics, optimisation and even self-maintenance in flight. That doesn't mean they won't also transmit telemetry data via satellite (or maybe air-to-ground 5G in future), but that likely won't be for realtime control.

The line from Nvidia's website (link) that should be read carefully by 5G advocates is this: 

"With a unified architecture, deep neural networks can be trained on a system in the data centre and deployed in the car"

However, that is not to say there is no requirement for connectivity. There will be a lot of data flowing around, generated by sensors or user/device behaviour, fed back to a machine-learning system and analytics function to help develop, train and improve future algorithms and models. But that doesn't need to be realtime - it can wait until the car gets home, or the handset dips back into 4G/5G/WiFi coverage. Vehicle-to-vehicle data flows will be useful in helping build a better picture of the context, but that is a secondary consideration at the moment, and also may well not involve cellular connections.

There will also be a need for non-critical information to use the network, such as mapping and navigation data for vehicles, entertainment for passengers, or advertising overlays for an AR headset. In an IoT context, the irrigation data from one farm's sensors will implicitly be helping train the AI system used to manage other locations' (and maybe even other industries') systems.

I think there is a gulf in understanding between telecoms and AI communities. I don't think many of the 5G standards and verticals discussions factor in the rise in GPUs at the edge/in devices, for a lot of "heavy lifting". It often won't need to be done in the cloud, or even mobile edge computing nodes. Some of the VCs seemed to get "connectivity" a bit better, but even some of those seemed unrealistic about 5G timelines, deployment and capabilities.

Clearly there will still be many needs for huge volumes of 4G/5G Internet connectivity from smartphones, streaming video for various applications and a lot of genuine IoT requirements. There is definitely an ongoing business model for enhanced mobile broadband. (Sidenote for another post: Home WiFi is also going to be mesh and AI-enabled by companies like Google and Amazon).

So... I think that some of the expected critical IoT and massive IoT uses for 5G are being overstated. There may well be a need for more mobile uplink data to help train deep-learning systems and other analytics tools. But that often doesn't need to be realtime. While they might need software updates from the cloud, a lot of endpoints will be smart enough to make their own decisions and analysis without relying on he network.

I also think that in the 3-5yr timeframe for mobile and IoT 5G deployments to have broad coverage, AI technology (both software and hardware) will have progressed far beyond even where it is today. There are so many branches of AI, from deep-learning to image recognition to bots - and these have much tighter couplings with the enterprise IT systems and end-devices, than the network. 

Meanwhile, the telecoms industry is looking forward to exciting 3-year processes to define "agenda items" in interminable regulatory committee stages, and regional sub-committees, before the next ITU World Radio Congress in 2019, to debate 28GHz vs. 32GHz bands, or work out how to "harmonise" 700MHz for 5G against incumbent desires of broadcasters and others.

At the moment, in the new strategic battleground of Networks vs. AI, I suspect that Moore's Law and deep-learning mostly favours the robots.

This post is from Disruptive Analysis' new TelcoFuturism research programme. This looks at strategical implications of intersections between the telecom/network industry and other adjacent trends. If you are interested in more detail about this, or to arrange an advisory briefing or keynote speaking engagement, please contact information AT disruptive-analysis DOT com.

eSIMs are over-hyped for consumer products

The last couple of weeks - especially with MWC - have seen lots of noise around embedded SIMs (eSIMs). In particular, the GSMA announced its remote provisioning standard (link). 

While interesting and a step in the right direction, I think the industry is over-hyping the potential of eSIMs.

eSIMs are still physical SIMs, but they are built-into devices as fixed hardware components (basically an extra chip soldered-in), rather than as traditional removable cards. They can be remotely-programmed to support different operators' profiles, or switch between them.

This development gets around some of the more awkward practicalities of physical SIM cards in non-phone devices:

• Physical space & design constraints needed for SIM slot & removable tray
• Vulnerability to vibration and dust by having a tray/slot
• Need to get SIM cards into the devices' normal distribution channels & retail stores
• Potential need for user to source a SIM separately in a different purchase
• Difficulty for user to swap operators (especially if device is locked to a particular network)

These were some of the problems which stopped widespread adoption of cellular radios and SIM cards in laptops, most tablets and other devices. (I wrote about this a lot in 2006-2008, eg here & here)

In that sense, eSIM is definitely a step forward. More use-cases become practical for cellular connectivity, just at the time when M2M/IoT is finally taking off. However, it would be wrong to assume this means that 4G-connected consumer devices will become the norm. While some categories (eg cars) are widely adopting cellular radios, others (eg wearables, home electrical appliances) are not.

The problem? Cost.

A 4G radio module and a SIM/eSIM remains a significant extra component on the per-unit BoM (bill of materials) cost for a manufacturer, plus the costs of extra design, engineering and testing in creating a cellular version of a product, amortised over the volume sold.

Today, normal 4G modules for devices cost perhaps $20-30, with SIM, battery & other components added to that. 

New versions of LTE are being designed to reduce costs, by cutting down some of the functions of "full" LTE. The target price for a new Cat-1 LTE module, optimised for M2M, is about $15. It's reasonable to imagine that Cat-1 (or Cat-M, its successor) will get to the $10 range over the next couple of years.

In parallel to this, when the new 3GPP NB-IoT low-power standard starts to ship (maybe mid-late 2017, being optimistic) the price should be more like $5-10, with an intention (I'm guessing 2018) to get that below $5. Add on an extra amount for the eSIM licence and design/test costs - probably a few dollars more. Then add on whatever is needed in terms of extra battery, software and so forth.

In other words even in two years' time, adding cellular to a consumer device will still cost the manufacturer at least $10 and perhaps $20 depending on the power/transmit speed needed, number of frequency bands, fallback to 3G, voice support and so on. While that's better than today, it's still significant for a manufacturer to wear.

Now $10 does not seem like much - or even $30 - until you consider the underlying costs of the devices they're supposed to be built into.

• A $20,000 car might have a 13% gross margin, or $2600, before amortising the R&D and sales & marketing costs
• A high-end laptop might have a $100/unit gross margin, and a low-end one maybe $30
• FitBit (the largest wearables company) makes 46% margin on $87 average selling price, so about $40/unit
• A $300 washing machine might have 17% margin, so $50/unit
• A $30 toaster probably has a $5 profit margin

So in other words, adding a cellular module now, and also in the mid-term future, is a large % of gross margin for most consumer devices, irrespective of whether it uses SIM or eSIM.

Nobody is going to add a $10 extra cost to a toaster which has only a $5 margin, unless they can charge an extra $10 (or preferably $20) for it. And if only perhaps 10% of people actually (a) care enough to want a connected toaster, and (b) are willing to pay the extra cash upfront, then the product will become uncompetitive. Instead, the manufacturer could make two versions - normal & connected - sold at different prices. But that adds complexity in manufacturing, adds inventory costs, and there's no guarantee that retailers will stock both anyway. 

There's also no realistic way for cellular operators to subsidise the new mToasters down to the normal price, unless they sell them in their own stores, or find a way to reward the manufacturers with a sign-up bounty or rev-share once they get activated.

Result - the cellular Connected Toaster market is a non-starter, unless someone works out a way to print adverts on toast in shades of brown, and creates a new business model. And even then, you could probably do it more cheaply and easily with a WiFi Toaster.

Now obviously that's an extreme example - but it is designed to make the point that if (radio module+SIM) is a big % of the underlying device gross margin, and take-up rate is likely to be low, then the concept is not viable. In particular, if a device doesn't already come in (successful and well-used) WiFi-connected versions, it is unlikely to succeed in cellular variants, unless it has wheels or legs, plus high margin and a possible new revenue stream.

A $2000 specialist mountain bike might get a cellular radio built-in. A $20 bike sold in a developing country will not. 

In other words, while eSIM is a helpful advance for some types of connected IoT device, it's not a huge game-changer which will mean every home appliance, and every wearable product, will adopt 4G. Where it is realistic is in categories such as:

• Expensive items such as cars, which can wear the extra BoM cost of the radio module and SIM/eSIM easily, and which may be re-couped by extra revenue streams to the manufacturer such as warranty sales
• New categories of devices which need always-on wide area connectivity to function - eg "lost and found" tags or wearables for wayward pets and children, or realtime heart-rate monitors with emergency alert capability for cardiac patients (notwithstanding insurance liability costs).
• Special "connected" premium-priced versions of products that normally just rely on WiFi or other short-range wireless (eg most wearables)
• Separately-sold accessories, eg aftermarket security "trackers" for bicycles
• Existing SIM-connected devices where the physical SIM creates extra complexities (eg some tablets, some industrial machiner - and maybe, finally, mainstream laptops etc)

This also means that the vast bulk of upcoming IoT devices (the quasi-mythical 10bn, 20bn, 50bn figures) will not support cellular, certainly by 2020, and perhaps even by 2025, unless 5G module prices get below $1, which seems unlikely. The majority will either use WiFi, cheaper (& non-SIM) LPWAN technologies, or maybe aggregated locally via a cellular gateway.

Cellular is definitely a player in IoT, but it will certainly not be ubiquitous, eSIM or not. There needs to be a specific reason and use-case for its inclusion - it is too expensive to be added in by a manufacturer just as an extra feature, except on very expensive/profitable products.

Disruptive Analysis has conducted research projects & internal advisory workshops on SIMs/eSIMs for tier-1 mobile operators, vendors and investors in the recent past, as well as writing about the Apple and Google SIMs. In addition, I've written about IoT Networking & LPWAN technologies for STL Research, as part of its Future of the Network research stream. (details here) Please contact information AT disruptive-analysis DOT com for more details.