An Overview of the Current Low Power Wide Area Network Market

Alexander Gluhak
September 12, 2017

 

Transforming disruptive technology developments from early hype to commercial reality is often a bumpy ride, which also the Internet of Things (IoT) is currently experiencing. The first success stories from early adopters have given rise to a huge diversification of the underlying technology base driven by a gold rush of aspiring entrepreneurs. However, larger companies are also joining the race to cut out a big slice of the promised Trillion Dollar IoT revenue cake1.

Emerging IoT connectivity technologies such as Low Power Wide Area Networks (LPWAN) are expected to connect more than half of the IoT devices on our planet but this technology market is still immature and highly fragmented. Clear technology winners are yet to emerge from an intensive phase of upcoming market consolidation, which makes it a gamble to commit to a particular technology choice. This article explores emerging LPWAN technology ecosystem and discusses the main market drivers for the adoption of these technologies.

Low Power Wide Area Networks

Data services provided by cellular networks of mobile operators have been the dominant form of long-range wireless connectivity for IoT devices. These so-called machine to machine (M2M) connections are delivered on top of 2G, 3G or 4G network technologies. However, limitations such as higher costs and energy consumption as well as the inability to provide deep indoor coverage make them not suitable for many IoT applications.
Low power wide area networks (LPWANs) promise to bring simple, more affordable and energy efficient connectivity with high link budgets. This makes them more suitable for IoT applications that require increased device autonomy and offer limited maintenance opportunities, where device and connectivity costs have to be low and where information from hard to reach areas have to be gathered. A LPWAN network can connect many end devices via a single base station over a long distance, however the trade-offs are slower data rates and small data payloads and limited number of message deliveries. Current options on the market can be roughly divided into technologies using unlicensed and licensed spectrum:

  • LPWAN technologies operating in unlicensed band often utilise the sub-GHz ISM band due to improved propagation characteristics in particular for built environments. Sigfox2 and LoRaWAN3 are currently the most widely deployed unlicensed LPWAN technologies. Other contenders include proprietary technologies such as Telensa’s Ultra Narrow Band (UNB)4 , NWave5 or RPMA by Ingenu6 . Another more recent noteworthy open standard is Weightless-P7 .
  • The licensed LPWAN variants have been developed by the mobile industry in response to the increasing demand for IoT connectivity and the early success of unlicensed LPWAN technologies and potential market threat they are posing to existing mobile network operators and vendors. The main contenders are two LTE based variants Narrow Band IoT (NB-IoT8 ) and CatM19 and a 2G based variant referred to as Enhanced Coverage GSM (EC-GSM-IoT10 ). Also worth noting are current research efforts for Massive Internet of Things (MIoT) under the 5G umbrella. They will be able to offer highly reliable low latency communication to many device end points. However, their impact on the current market is currently very limited due to the immaturity of these technologies.

Although LPWAN technologies offer similar service properties, they vary in terms of technical capabilities such as uplink and downlink data rates, capacity, quality of service support or link budget. For more details the reader is referred to another overview article11 or to the technical specifications of the individual standards.

Ecosystem diversity

One of the most interesting aspects in the current LPWAN arena are the differences in ecosystem approaches that are taken by existing LPWAN technology vendors, which in turn have influence on possible business models that stakeholders can realise along the value chain. The figure below shows a high level view of the LPWAN value chain. Chips and modules provide the enabling layer of LPWAN communication capabilities for both end devices and base stations.

Figure 1: An overview of the LPWAN value chain. Figure 1: An overview of the LPWAN value chain.

A network operator provides LPWAN network infrastructure, which leverages both base stations and network software.

IoT devices are typically sensing and actuation end points or connected products, which utilise LPWAN networks to exchange information with IoT services. Often, IoT services utilise IoT application enablement platforms, which simplify the development of service by providing data and device management services and building blocks for rapid application development.

Most of the LPWAN technology ecosystems are open on the chip/module and IoT device side as well as IoT service side to ensure suitable end to end solutions to emerge for end users on the market. However, the approaches differ in how networks HW/SW and operations are managed and made accessible to other stakeholders.

SigFox controls tightly the network value chain by providing exclusive access to its network technology to a single operator per country in a franchise model. Telensa uses its own UNB technology to enable smart streetlight solutions for their customers, but does not make its technology available to a wider ecosystem. NWave is the sole supplier of both network and connectivity modules, but offers these to application partners to jointly deliver end-to-end services. Ingenu operates various private network across different countries and is now creating a public network in the US. It works with a module vendor to make its technology available to application partners.

LoRaWAN and 3GPP LPWAN variants are examples of a complete open ecosystem play where different network HW/SW vendors and network operators can freely compete. They offer choice from a diverse set of network HW/SW vendors and enable both public and private network operation. In reality licensed LPWAN variants provide a high barrier of entry for both network vendors and operators due to the high reliance on LTE network legacy and spectrum. The play will be dominated by incumbent network operators and telecom equipment vendors. LoraWAN in contrast provides opportunities for not only for traditional operators and infrastructure vendors but also for a variety of new market entrants. Some companies run LoRaWAN networks as private corporate networks comparable to WiFi, others open up their private networks to other users to become public network operators. An example of the latter is SENET12 in the US. The Things Network13 is an example of a crowd-sourced LoRaWAN community network.

Drivers for LPWAN technology adoption

A variety of factors influence the choice of market adopters for a specific LPWAN technology and its ecosystem. These apply both to solution vendors who base their products on a specific LPWAN technology and end users who utilise LPWAN based solutions to tackle their business challenges. The main drivers for LPWAN technology adoption include:

  • Technology match. How well does the LPWAN technology support application demands? Various factors play a key role here. The strongest differentiators of existing technologies are reliability, capacity and maximum uplink / downlink data rates as well as energy consumption. Licensed technologies such as NB-IoT and CAT-M1 have the upper hand when delivering reliable network services, and are able to serve more devices per base station and provide higher peak data rates. Unlicensed technologies such as LoRaWAN and SIgfox are more suited for IoT applications with lower communication and energy demands. A big drawback of Sigfox is the limited down link capacity making over the air updates for software lifecycle management very difficult.
  • Network availability. The rollout of commercial services beyond pilot stage often requires the existence of LPWAN networks to support operation of significant scale.  
  • At the moment, Sigfox is the network with the largest geographic coverage due to its head start compared to other technologies. However, the LoRaWAN ecosystem seems to be quickly catching up with national public network rollouts ongoing in over 30 countries. LoRaWAN also provides a good viable option for private network deployments. NB-IoT and CAT-M1 have been followers on the market and were fast-tracked last year through the 3GPP standards pipeline. Public network rollouts are only at the beginning with US operators seeming to favour CAT-M1 while European operators focus on NB-IoT. An advantage of Sigfox is the global footprint as different national deployments appear as single network to their customers. For the other technologies roaming is needed to support customer device moving to different countries. Differences in spectrum regulation across countries also make it difficult to have a device work on the same transceiver module.
  • Module costs. Viable IoT business cases often require IoT device costs to be kept low, in particular where a large number of IoT devices are to be deployed to enable an IoT service. Connectivity module costs are a major contributing factor to it. LPWAN module costs are typically dictated by the transceiver complexity and drop with sufficient increasing market demand. Sigfox modules currently offer the most competitive price point and are approaching the $1 mark. LoRaWAN slowly follows the trend but are yet to achieve such economies of scale. The NB-IoT and LTE-M module ecosystem is still embryonic, but even if they catch up in terms of scale, costs are unlikely to go below the $5 mark due to the increased transceiver complexity. A strategy for use cases that leave little margin for IoT device hardware is to offer the devices for free as part of a monthly or annual service subscription.
  • Ecosystem confidence. Choosing the right technology ecosystem can be risky in an immature market where winners still have to be determined and technologies can disappear overnight. SIGFOX currently represents the largest LPWAN solution ecosystem and network deployment and has received over $300Mio in investments while generating only a modest $13.5 annual revenue in 2016.  The LoRaWAN alliance boasts boast now more than 500 members and a constantly growing ecosystems of solutions. The strongest ecosystem however is behind the licensed LPWAN technologies such as NB-IoT and CAT-M1. Although slightly less mature at the moment, it will eventually come strong and with large scaling potential. All other LPWAN technology ecosystems are currently either nascent or less well developed. One strategy for LPWAN solution providers to navigate this uncertainty is by developing products that can be easily ported to different LPWAN technology options. Innovative LPWAN module companies such as Pycom14 provide a range of pin-compatible modules for different LPWAN technologies that can be easily swapped out during the production process, without the need for new firmware development.
  • Ecosystem openness. A final important driver for large scale adoption is the openness of a technology ecosystem. When looking at the history of wireless technology, open standards and value chains are essential to build successful innovation ecosystems. Open standards typically win over proprietary standards and closed ecosystems are less likely to enable sufficient critical mass necessary for economies of scale.  The most successfully deployed wireless standards such as Bluetooth, Wifi, Zigbee and the mobile standards such as GSM, UMTS and LTE are all based on open standards. They enable the necessary permissionless innovation to experiment freely with new technology solutions and business models.

Conclusions

After an initial hype, the LPWAN market is at a crossroads as clear technology winners are yet to emerge from an intensive phase of upcoming market consolidation. The current diversity of technology options prevents the emergence of economies of scale that are needed to drive down the price points of IoT products and solutions. Ecosystem around Sigfox, LoRaWAN and the licensed LTE variants have currently the largest chance in breaking the deadlock as they best align with the market drivers of adoption. While there is space for some of these technologies to co-exist and serve different markets demands, only few of the above discussed technologies will survive to dominate the LPWAN market by the end of this decade.


 

alex gluhakAlex Gluhak is Head of Technology and IoT Lead at the Digital Catapult, where he is responsible for interventions to help UK companies grow faster using emerging digital technologies. For the past 15 years Alex has actively contributed to the research of mobile computing and IoT technologies at companies such as Intel Labs and Ericsson. He leads Things Connected (http://thingsconnected.net/), a  UK innovation support programme for products and services based on Low Power Wide Area Networks and the development of decentralized IoT data market places in the H2020 SynchroniCity project (http://iot-synchronicity.eu).

 

 

Comments

2018-04-21 @ 5:33 AM by Ahmad, Hamza

Which LPWAN technology do you see as most impactful and why? 

TagItSmart – SmartTags for Unlocking Business Potential

Stylianos Georgoulas, Srdjan Krco and
Rob van Kranenburg
September 12, 2017

 

IoT (Internet of Things) is about connecting objects, things and devices and combining them with a set of novel services. The IoT market is unstoppably progressing, introducing a lot of changes across industries, both from the technological and business perspectives. The vision of the IoT did not change much from the beginning, but the reach that current technology brings to the table is still limited due to the certain limitations (i.e. technological limitation or for the practical reasons such as prices of the tags in some mass market scenarios) in different application of IoT.

Optimization of the whole value chain is providing many opportunities for improvements leveraging IoT technologies, in particular if information about the products is available and shareable. Many related industries are going to be affected by this, as packaging and insurance companies, among others, are requested to be more transparent to consumers, while consumers (78%) prefer brands that create unique and personalised content and are more interested in building a relationship with these companies [1].

Consumer packed goods (CPG) companies can prepare themselves for a range of possible futures by harnessing technology, reinventing brands, and exploring new business models [2]. The following five potential “undercurrents” that may impact the consumer product industry in 2020 are identified:

  • unfulfilled economic recovery for core consumer segments;
  • health, wellness and responsibility as the new basis of brand loyalty;
  • pervasive digitalization of the path to purchase;
  • proliferation of customization and personalization; and
  • continued resource shortages and commodity price volatility.

An important aspect to take into account is the need for a service economy around IoT [3]. The interconnection of products will promote ecosystems of new online services; therefore, new business models based on this network capital are gaining momentum. The new value chains will increasingly organize itself as networks around consumers, offering a multiplicity of channels and interfaces across all value-add processes and business entities [4]. This makes consumer the one in charge, whose decisions affect the whole value-chain. Sharing information throughout the whole lifecycle of products and reactivity to context information are key in the short term.

The TagItSmart1 project sets out to address the trends highlighted above by redefining the way we think of everyday mass-market objects not normally considered as part of an IoT ecosystem. These new smarter objects will dynamically change their status in response to a variety of factors and will be seamlessly tracked during their lifecycle. This will change the way users-to-things interactions are viewed. Combining the power of functional inks with the pervasiveness of digital (e.g. QR-codes, quick response codes) and electronic (e.g. NFC tags, near field communication) markers, a huge number of objects will be equipped with cheap sensing capabilities (SmartTags) thus being able to capture new contextual information. Beside this, the ubiquitous presence of smartphones with their cameras and NFC readers will create the perfect bridge between everyday users and their objects.

TagItSmart has been developing a toolbox of functionalities (enablers) to manage the lifecycle of SmartTags; from their customized creation so they can react appropriately in response to exposure conditions of interest, to their scanning and decoding using even “off the shelf” smartphones, the processing and secure storage of carried information for on the fly recommendations as well as further data analysis. In addition, components and UIs that allow developers to introduce new services evolving around SmartTags that are then easily selected and executed by interested parties in an automated way and without requiring further technical knowledge from their side, are provided. This service platform is already being used to support diverse scenarios in the Smart Retail, Manufacturing, Brand Protection, Smart Health and Recycling domains, allowing condition tracking of mass-market products throughout their lifecycle, from the moment they are produced and tagged, during their useful life, up until they are eventually recycled/disposed of.

Conclusions

We believe that SmartTags, combined with this service platform, will create a completely new flow of crowdsourced information, which can pave the way for novel and previously infeasible services and business models, improved experience and revenue streams for stakeholders in the value chain; from manufacturers to logistics, retailers and all the way to consumers at home and recycling sites, empowering further the circular economy. The fact that we have already been approached by several companies interested in collaboration, the goal being using project outcomes to drive their digital transformation and creating novel services and benefits to their users, is live proof of the potential in the project and a guide of more things to come and stay in the long term.

References

[1] Consumer Goods Forum, ‘Rethinking the Value Chain: new realities in collaborative business’, 14 December 2015. [Online]. Available: https://www.uk.capgemini.com/resources/rethinking-the-value-chain-new-realities-in-collaborative-business.

[2] P. Conroy, K. Porter, R. Nanda, B. Renner, A. Narula, ‘Consumer product trends: Navigating 2020’, 25 June 2015. [Online]. Available: http://dupress.com/articles/consumer-product-trends-2020/.

[3] C. Links, ‘Consumers want Smart Systems Not Smart Things’, GreenPeak, 8 January 2016. [Online]. Available: http://www.sensorsmag.com/networking-communications/consumers-want-smart-systems-not-smart-things-20558.

[4] Consumer Goods Forum, ‘Rethinking the Value Chain: new realities in collaborative business’, 14 December 2015. [Online]. Available: https://www.uk.capgemini.com/resources/rethinking-the-value-chain-new-realities-in-collaborative-business.


1 http://tagitsmart.eu/

 

 

stylianos georgoulasStylianos Georgoulas is a Senior Research Fellow at University of Surrey, UK. He received a Diploma in Electrical and Computer Engineering from University of Patras, Greece in 2001 and his PhD degree from the University of Surrey in 2007. He has worked in a variety of EU and UK funded research projects such as the FP6 ENTHRONE, EPSRC MVCE Core 5 Flexible Networks, FP7 4WARD, FP7 UniverSelf and FP7 iCore projects. Currently he is the project manager of the H2020 iKaaS project and technical manager of the H2020 TagItSmart project. His research interests are in the area autonomic service management and energy-efficient traffic engineering, formal verification and stability control, applied in a range of environments ranging from fixed to wireless, cloud and IoT environments having authored and co-authored a number of papers in these areas.

srdan krcoSrđan Krčo is a co-founder and CEO of DunavNET, a company designing turnkey IOT solutions. He has over 20 years of experience, working with large multinational companies and driving international collaborative research and innovation projects. Currently, he is coordinating the research and innovation H2020 project TagItSmart, creating enablers for IoT of mass-market goods. Srdjan is a Board member of the International IoT Forum and is actively participating in IoT-EPI (IoT European Platforms Initiative) and AIOTI (Alliance for IoT Innovation) activities. In 2007, Srdjan won the Innovation Engineer of the Year award in Ireland. He has published over 15 patents and more than 70 papers at international conferences and journals and is a frequent speaker at international events addressing IoT and its applications. Srdjan is a Senior IEEE member. He received Microsoft’s MVP award for 2017/18 for the work done in the IoT domain.

rob van kranenburgRob van Kranenburg (1964) wrote The Internet of Things. A critique of ambient technology and the all-seeing network of RFID, Network Notebooks 02, Institute of Network Cultures. He is co-founder of bricolabs and the founder of Council. Together with Christian Nold he published Situated Technologies Pamphlets 8: The Internet of People for a Post-Oil World. He worked as Community Manager at the EU Project Sociotal, and currently as Ecosystem Manager of the Horizon 2020 Project TagItSmart. Rob is co-editor of Enabling Things to Talk Designing IoT solutions with the IoT Architectural Reference Model, Springer Open Access. Rob is in the SmartCitiesWorld Advisory Board. He chairs AC04 - IoT Hyper-connected Society of the IERC, The European Research Cluster on the Internet of Things. He is a member of The IoT Asia 2017 International Advisory Panel (IAP).

 

An Architecture for IoT Analytics and (Real-time) Alerting

Michele Stecca and Sergio Fraccon
September 12, 2017

 

Internet of Things (IoT) applications poses many challenges in different research fields like electronics, telecommunication, computer science, statistics, etc. In this article, we focus our attention on the so-called “IoT Analytics” that can be defined as the set of approaches and tools used to extract value from IoT data.

Of course, the meaning of the word “value” may vary, depending on the specific application domain (e.g., optimization of a production line to save time and money, increasing revenues thanks to new services, new business models, etc.). After describing one of the use cases that helped us to define the architecture depicted in Figure 1, we will discuss some of the technologies that can be used to actually implement a platform for IoT Analytics whose many advantages are:

  • increase data retention, thanks to the usage of scalable systems like Hadoop, Cassandra, etc. In such a way the whole set of data coming from physical devices is available to data scientists and data analysts;
  • generate detailed reports about equipment usage, customer habits, etc. starting from collected data;
  • carry out in-depth analyses on gathered data to predict future failures, utilization peaks, etc.;
  • apply the advanced models defined by data scientists in conjunction with streaming technologies like Apache Spark Streaming, Apache Flink, etc. This feature enables the implementation of (near-real time) altering systems.

Use case: Industry 4.0

We propose a general architecture for IoT Analytics that can be used both in consumer as well as industrial scenarios but, in this section, we describe how it can be used in the Industry 4.0 domain (a.k.a., Industrial IoT - IIoT) to detect events of interest, anomalies, etc. on a production line. In order to do so, the following approaches can be used:

  • apply static rules defined by looking at what happened in the past. In this case, analyzing data about how the production line performed in the past can help human operators to define static rules (e.g., “if machine B stops for more than 3 minutes and machine C for more than 5 minutes, then machine A is probably broken”) that are able to detect the events of interest. This approach is effective in many cases, but it is not optimal because it is able to describe only the simple “if-then” logic. Moreover it is not adaptive in case of changes;
  • Apply Artificial Intelligence/Machine Learning (AI/ML) techniques to identify the events of interest. The usage of advanced analytics techniques allows the identification of more complex logics that goes beyond the simple “if-then” approach. Furthermore, AI/ML enables the identification of patterns that were unknown to the operator and can self-adapt as conditions changes. AI/ML models are generated off-line (i.e., on the historical data gathered in the past) but, once defined, they can be applied in near-real time to respond quickly. In particular, we deployed our models in a centralized system where the application of AI/ML models to incoming data flows takes place in near real-time thanks to the presence of a streaming framework (i.e., Apache Spark Streaming). A generic discussion about where such models should be deployed (i.e., Edge vs. Fog vs. Cloud Computing) is out of the scope of this article, as we focus on the solution that we deployed in real world scenarios.

Architecture and Technologies

In this section, we describe the architecture that we defined for IoT use cases and some of the technologies that we used in real world deployments.

Figure 1: The architecture for IoT Analytics.Figure 1: The architecture for IoT Analytics.
Since we are focusing on Analytics, we are not going to cover hardware, networks links, protocols, etc. because we consider these aspects for granted. The entry point to the platform is a stream of data provided by means of Message Queue (MQ) systems, like MQTT (Message Queue Telemetry Transport) or Apache Kafka (depending of the specific application requirements, other technologies like HTTP may be used).

The data received by the Message Queue system is forwarded to the Streaming Framework, which is a system optimized for the processing of data flows. This component is supposed to perform two main actions, namely:

  • ETL (Extract Transform Load) operations: data coming from the physical devices need to be pre-processed to make them suitable for storage (see letter A in Figure 1). Typical operations performed by the Streaming frameworks are data filtering, data enrichment, format changes, aggregations (e.g., aggregate data according to a specified time window), etc.
  • Apply AI/ML models on the fly: the algorithms (e.g., classification, regression, anomaly detection, forecasting, etc.) defined off-line by data scientists are deployed on the streaming framework to be applied as soon as new data reach the platform. Depending on the AI/ML result, this component may decide to fire alters in (near) real time (see letter B in Figure 1). Keeping in mind that the platform aims at generating value from the data, we can state that automated generation of (near) real time alerts is the first concrete example of “value generation”.
After being received and pre-processed, data have to be stored on scalable Storage systems to support report creation, analyses, AI/ML algorithm definition, searches, etc. It is important to choose the most suitable technology to fulfil the specific application requirements in terms of performance and volumes (possible choices are discussed below).

The rightmost component of Figure 1 is the IoT Analytics tool, i.e., a software typically used by analysts and data scientists to process (e.g., to define an AI/ML model) and visualize (e.g., to create a report) data. The insights that can be obtained from IoT data by means of IoT Analytics tools represents another example of “value generation”.

Regarding the technological landscape, for each component showed in Figure 1 a lot of different tools, both open source as well as proprietary, are available. Due to the lack of standardization and to the limited maturity of the IoT field, we remark the fact that nowadays many IoT deployments are tailored-made. Taking into account that every new project may require different approaches depending on the specific situation, it is important to be familiar with a multitude of technologies.

  • Message Queue: Apache Kafka[1] is a distributed MQ system that is widely used, but it is very common to use also MQTT-based frameworks like Mosquitto[2].
  • Streaming framework: Apache Spark Streaming[3] is the Spark component for distributed processing of data flows. This is usually deployed in conjunction with Spark SQL for ETL and Spark MLlib for real-time Machine Learning.
  • Storage: even though we draw just one block in the architecture, it is common to store data on multiple systems at the same time. For example, HDFS (Hadoop Distributed FileSystem[4]) is a common choice for long-term data memorization where it is possible to execute massive analyses (e.g., Apache Parquet[5] is a very optimized format for SQL queries). Elasticsearch[6] is a distributed search engine providing efficient search capabilities and analytics. It is possible to deploy both these frameworks to get the best of the two worlds (i.e., fast searches and massive computations).
  • IoT Analytics: this is a very crowded field, where we can find ad-hoc solutions, traditional business intelligence tools adapted for IoT/Big Data, and Big Data-native solutions. For instance, we developed our own end-to-end Big Data-native solution called Doolytic[7], which is very flexible because it can be used by both data scientists (through the Notebook Computing integration) and by not-skilled users (through a user-friendly interface).
Conclusions and future work

In this article, we described out approach to IoT Analytics. It is still too early to understand how a “standard” architecture for IoT Analytics will look like, but we think that capabilities like streaming, real-time alerting, real-time reporting, scalable storage, etc. represent important building blocks in the IoT systems of tomorrow. In the future, we will investigate the introduction of Deep Learning frameworks, like Tensorflow[8] to deploy more advanced algorithms (e.g., autoencoders for anomaly detection) and/or the usage of Elasticsearch built-in Machine Learning capabilities.

[1] https://kafka.apache.org/

[2] https://mosquitto.org/

[3] https://spark.apache.org/streaming/

[4] http://hadoop.apache.org/

[5] https://parquet.apache.org/

[6] https://www.elastic.co/products/elasticsearch/

[7] http://doolytic.com/

[8] https://www.tensorflow.org/
 

 

michele steccaMichele Stecca received his master’s degree in Software Engineering from the University of Padova and his Ph.D. from the University of Genova. He worked as a researcher at the ICSI in Berkeley, CA before joining Horsa Group as IoT & Big Data Consultant. He has participated in important projects co-financed by the EU in these areas for Telefonica, Telecom Italia, FIAT, Atos and Siemens (in the IoT field he has been involved in the EU FP7 collaborative project iCore www.iot-icore.eu). Dr. Stecca has authored numerous articles for international industry publications, presented at many international events and has served as an Adjunct Professor at the Universities of Genova and Padova. For more information see: https://sites.google.com/site/steccami/

 

sergio fracconSergio Fraccon is an MBA IT Leader with 20 years experience in National and Multinational companies on both side demand and offering, focused into the business process realignment to the company strategy, using IT as enabler. Thanks to these experiences he has developed international skill and experience of management of international projects and team in different country and different industry (from Manufacturing and logistics, to Fashion). He is today Director of Business Analytics Data Strategy Unit at Horsa Group, taking care of Data Intensive environment that has analytics pervasive not only as Decision Support System but already integrated in the daily operational process. His main goal is to spread technologies like Big Data, Machine Learning and Artificial Intelligence in practice and solutions for all kinds of company size, making it accessible not only to the big organization but also to the mid-sized company.

 

Comments

2017-09-15 @ 4:10 PM by Strnadl, Christoph

I am not sure if I miss something but I don't see how this architecture is different from the well-known Lambda architecture. We have been applying this architecture for years now, but surely it helps to be re-inforced that it actually works in practice.

Secondly, in practically all our IoT projects, very soon clients discover they need a back-channel from the real-time analytics component (which, incidentally, is not even shown in the diagram) back to the device e.g, to change some device parameters. Unfortunately, however, this omission practically stymies the application of this architecture to (I am exaggerating) trivial use cases where you get some alerts on real-time data BUT no action at the device level is needed. IN MANY projects -- especially if IoT maturity grows -- however, getting back to the devices is where very large business benefits are buried.

Additionally, you need a channel from the real-time analytics to the corporate/enterprise IT systems as well -- unless you want to limit alerting and actions to dashboarding or user-mediated activities. This, of course, no longer is trivial and certainly needs a full-fledged integration (+ process automation) platform. However, again, the end-to-end process integration from devices to corporate IT systems (think of ERP or CRM) and back to devices is where large business values can be uncovered.

And, actually, in real world projects one needs this back channel basically as step 0 (yes, the very first step) for device management. This, of course, is not shown at all in this architecture -- which, for all practical purposes, severly limits the application of it.

So, if you limit your use case to just (real-time) analytics -- AND REALLY NOTHING MORE -- then this architecture template may indeed serve you well.

IF, however, you need ANYTHING OUTSIDE this (artifically, in my practical view) limited use case, then a FULLER ARCHITECTURE is required. And this will be the case for many (if not all) integrated real-world use cases.

By the way, these much (broader) architectures are readily available.

 

P.S. The authors also mention "fog" and "edge" computing. Unfortunately, we are seeing an increasing demand for "fog analytics" and "edge analytics" -- so even from a pure (real-time) analytical point of view, this architecture definitely is not future-proof.

Improving IoT Performance and Quality of Experience with Remote Module Services

Juan Carlos Lazcano
September 12, 2017

 

Innovation and the Internet of Things (IoT) are fueling a new industrial revolution that is quickly transforming the way we work, live and enjoy our lives. From manufacturing to transportation to farming, real time data available at your fingertips, and the ability to remotely manage assets is optimizing process, improving productivity and helping to save resources. For instance:

If you’re a farmer, economical use of water and fertilizer are essential to make tight profit margins. By installing smart agricultural sensors, food producers are able to closely monitor and manage fields based on real time weather, humidity and sunlight. At harvest time, yield is increased while the cost of crop inputs is reduced, even with challenging environmental conditions. This success is made possible by the growing network of connected sensors and objects that are able to collect and exchange data using embedded IoT technology and wireless networks.

Realizing IoT benefits in a world of technology fatigue

IHS, a market analysis firm, forecasts that the IoT market will grow from 15.4 billion installed devices in 2015 to 30.7 billion in 2020 and 75.4 billion by 2025[1]. Almost everyone is now interacting with the IoT, whether they know it or not. Both enterprises and end users want the convenience and savings the IoT promises; however, not everyone wants more technology in their lives. The pace of technological innovation is frenetic and many people don’t have the desire or bandwidth to learn how new systems work. Customers want a smooth experience, especially when managing fleets of devices deployed across geographically diverse locations. They need to trust that IoT solutions will perform as expected, providing 100 percent reliability without customer intervention or physical maintenance.

Improving performance and quality of experience for the IoT ecosystem

To ensure seamless reliability and realize the full potential of the IoT, device makers, service providers and Mobile Network Operators (MNOs) need new solutions to enhance the quality of experience for implementers and end users - a key factor in driving IoT adoption and customer retention.

The good news is new software solutions are emerging that leverage existing embedded IoT technology to allow ecosystem stakeholders to remotely monitor and manage their solutions improving connectivity and functionality. And they don’t require a complete overhaul of existing IoT hardware. Software solutions can be introduced and integrated into already deployed assets allowing remote monitoring, maintenance and troubleshooting for a whole fleet of deployed devices. The solutions are used to optimize performance from the time of installation throughout the entire lifecycle of the device.

Improved insight into how IoT solutions are using wireless networks opens the door to improved quality of service and experience for customers by identifying, reporting and, in some cases, repairing potential connectivity issues before they cause service disruption. By enabling remote monitoring and control of IoT solutions, reliability of applications can be greatly improved, lifespan of devices can be extended through timely over the air updates, and cost-savings can be realized by eliminating expensive and time-consuming maintenance visits for swarms of devices.

How does it work? Examining new solutions for managing IoT performance

IoT solutions and devices are comprised of four basic elements: sensors that collect data, a communications module and SIM that enables connectivity and sends sensor data securely over wireless networks, a cloud platform that analyzes the data, and a user interface that delivers information in easy to understand formats. Solutions are often deployed in remote locations and as fleets spread out across different regions, this creates challenges for monitoring performance. For instance, connected car solutions are installed in vehicles at the time of manufacturing, and then sold and distributed in regions around the world. These solutions operate on a variety of different wireless networks using different types of technology standards. What they all have in common are wireless connectivity modules and SIMs or Machine Identification Modules (MIMs) that play a key role in ensuring that cars stay connected and that vehicle data is shared seamlessly, no matter where they operate. It is at the IoT module level where new solutions can be introduced to improve device monitoring and enhance IoT solution performance and quality of service. 

The benefits of IoT module monitoring

If we map the solution flow in a smart car, the benefits for all stakeholders in the ecosystem become clear. The latest connected car systems include advanced telematics that constantly monitor engine performance, as well as Advanced Driver Assistance Systems (ADAS) that can take control of the vehicle in specific scenarios to improve driving outcomes. They send alerts when service visits are due, prompt improved driving behaviors to enhance safety and fuel economy and enable a suite of value added services including 3-D navigation, entertainment, mobile WiFi, concierge services and automatic eCalling when accidents occur.

By leveraging a software agent running on the solution’s connectivity module, the OEM or service provider can monitor and manage the performance of the connected car system to improve end-to-end system performance. The solution can fully automate device data collection and uploading so that no human interaction is needed for reporting. This is essential to drivers who want and need to be fully connected on the road but who have no interest in comprehending and managing another connected system.

These solutions proactively measure and analyze a range of device indicators and events in real time including: connectivity thresholds, reboot counts, radio parameters, cell IDs, radio access technology, GPS and geolocation parameters and more. This data is useless to the driver but when securely shared with the OEM and service provider, it’s key to guarantee that the connected car system behaves as expected.

To be successful, solutions must be extremely flexible so that the data collected can be defined to meet varying Service Level Agreements (SLAs), which are customized to meet the needs of different types of customers. The solutions should also include real time alerts that can be pre-defined to generate alarms and warnings for proactive issue detection allowing repairs before the customers notice an issue. The software solutions can also be embedded on the SIM or MIM of the connected car solution to offer support and monitoring that is reported directly to the Mobile Network Operator. In this way, OEMs and service providers can work together to ensure drivers and end users get the service they depend on.

Improved experience with zero touch

Unbeknownst to the driver, the software solution can detect changes in parameters caused by emerging events like heavy network traffic during rush hour drive time. As the vehicle nears a potential white zone, the solution automatically analyzes the wireless ecosystem based on quality of service findings and selects the best service provider in the area to meet the SLA. The solution would then make a seamless network switch to ensure free flowing bandwidth. And just in time too, because the car hits debris on the roadway and gets a flat tire. Before the vehicle has come to a complete stop, the vehicle’s eCall system has alerted the nearest public service answering point and within minutes, the vehicle’s concierge service indicates that help is on the way.

In this scenario, the software solution works like a scouting entity to find the best wireless network while it easily integrates collected indicators and data with any backend system. Best-in-class solutions should provide the service provider with a history of network connectivity issues and Key Quality Indicators (KQIs) per region, per operator, for specific devices and for specific SLAs. In the case of connected cars, SLAs are rigorous and penalties can be costly. Solutions that help fine-tune and improve the quality of service, ultimately improving quality of experience for the driver are essential for meeting SLAs and avoiding fines.

The technology provider, in this case the connected car OEM, uses KQI data that measures customer care elements for specific vehicles and VIPs to improve service on their end. This makes it possible to reach out to customers and receive feedback following events, which ultimately helps to build closer customer care connections that strengthen brand loyalty. With this type of solution in place, end users can happily ignore the complexities and inner workings of IoT technology while receiving first-rate service they can count on.

We are standing at the precipice of a world transformed by the Internet of Things, an interconnected world of convenience, efficiency and safety, where enterprises and people have confidence in the reliability and security of connected devices. As solutions for monitoring and managing IoT modules and MIMs make their mark on the marketplace, the entire IoT ecosystem benefits. And perhaps most importantly, customers can rely on the IoT to provide reliable and trusted services that simplify and ease the way we travel, work, and live.


[1] IoT Platforms: Enabling the Internet of Things

 

 

juan carlos lazcanoJuan Carlos Lazcano is Vice-President of M2M for North American and responsible for developing Gemalto’s M2M and security portfolio and delivering solutions to device manufacturers and system integrators. Providing strong expertise in mobile communication, Juan was formerly Vice President of Telecommunication Sales at Gemalto where he handled mobile payments accounts and innovative connectivity projects for global mobile network operators and service providers. He joined Gemalto in 1998 and has held various positions in business development and marketing in the U.S and in Latin America. Prior to Gemalto, Juan was a systems consultant and Internet service manager for Monterrey Tec and a consultant for Softtek. He received a Bachelor’s degree in electronics and a Masters degree in Management of Information Systems, both from Monterrey Tec.