IoT Analytics Across Edge and Cloud Platforms
IoT Analytics Across Edge and Cloud Platforms
Internet of Things (IoT) is seeing the rapid deployment of sensing, control and communication infrastructure across various application domains. These range from utility infrastructure such as smart power and transport, to consumer devices like Fitbit and Nest. This growing ability to observe and control devices in real-time to efficiently manage public or lifestyle services is motivating the need for responsive analytics, the software platforms to coordinate them, and the computing resources to execute them. While device and communication technologies were at the vanguard of IoT, the next wave of IoT innovation will be driven by data analytics and computing. To this end, distributed analytics platforms that can utilize heterogeneous computing resources, at the edge and in the Cloud, are starting to be essential.
Contemporary IoT software architectures are typically Cloud-centric or Device-centric (see Figure 1). In the former, data from a sensor is streamed to a Cloud data center, where analytics and decision making happen using current and historic data from this and other sensors, and the control signals transmitted back to the actuators at the edge. This forms the predominant interaction model at present. In a device-centric model, proprietary logic present within the device operates on the sensed observations and makes local decisions, with the firmware updated occasionally. Examples of this include stand-alone consumer devices. However, both of these are narrow architectural views.
Edge Devices as First-class Computing Platforms
An emerging model of distributed analytics is one that spans devices at the edge of the network and Cloud resources seamlessly, leveraging the relative merits of each. There are several motivations for this.
- Data sources and control sinks at the Edge. Most observation sources that drive the analytics lie at the edge of the network. These include physical sensors that measure the infrastructure, mobile devices for participatory sensing, and gateways devices that interact with local sensors and actuators. Similarly, the controls for managing the infrastructure are also at the edge. There are of course exceptions to this, such as social streams, historical data, and web service controls hosted in the Cloud.
- Network constraints between Edge and Cloud. Moving data from the edge to a remote data center incurs network latency of 100’s of milliseconds, which is unacceptable for interactive or mission-critical applications. Similarly, urban surveillance applications generate large data volumes that are bandwidth-prohibitive to completely move to the Cloud. The network connectivity may also be intermittent, causing a loss of functionality if the Cloud connectivity is lost.
- Resource Costs. Cloud resources are charged on a pay-as-you-go model. As a result, compute, storage and bandwidth add to the operational costs that the application provider pays for, or the user is charged for. Hence, there is a trade-off between the revenue or value earned by the IoT application and its running costs on the Cloud.
Advances to device and processor technologies have ensured that contemporary sensor and gateway devices have non-trivial computing power. E.g., the Raspberry Pi device that is popular as IoT gateways has a 64-bit ARM processor with 4 cores, each of which offers a third of the computing power as an Intel Xeon processor core on a Cloud Virtual Machine (VM). Further, these captive devices are part of the one-time capital expenditure and their maintenance is ensured. Lastly, they are co-located with the sensors and actuators, either on-board or in the local network. Consequently, there exist significant benefits if edge devices can be leveraged as active platforms for analytics, besides Cloud resources.
Figure 1: Different interaction models and roles for edge and Cloud resources.
Gaps in Edge+Cloud Computing Platform
While there is a growing trend in utilizing the edge devices as first-class computing platforms, several gaps exist. This has parallels with the transition of mobile telephony from feature to smart phones – while feature phones started to have high computing power, it was the advent of a software platform for app development (iOS, Android) and Cloud-based services that enabled that transformation. We are at that cusp now.
The key limitation to using edge devices effectively is the lack of a platform ecosystem that allows generic and distributed applications to be designed, deployed and executed on them. This can be at the infrastructure layer, whereby a light-weight “container” with pre-defined applications can be spawned on the edge devices, similar to VMs in an Infrastructure-as-a-Service (IaaS) Cloud. This should allow container resources to be acquired transparently, on-demand, and applications deployed within them. The infrastructure would also offer access to on-board sensors and controllers, and linkages with Cloud services. We are seeing such solutions from VMWare Liota and Eclipse Kura for gateway device management, but more is required for distributed device management rather than a single-device.
Alternatively, a Platform-as-a-Service (PaaS) offering would make defining, deploying and managing distributed IoT applications across edge and Cloud easy. A simple model would be like sandboxed “apps” running on a single device, much like a smart phone. E.g., Cloud providers like Microsoft’s Azure IoT Gateway and Amazon’s AWS Greengrass offer limited support for defining event analytics on the edge coupled with scalable stream processing in their Clouds, and Apache Edgent, supported by IBM, is a similar open source offering. But the need is for more general purpose application platforms, and for their distributed execution across multiple edges and Cloud VMs.
Recently, we are leveraging Apache NiFi, a light-weight dataflow execution engine, to compose and execute generic IoT applications on Pi-class devices. We have extended NiFi to operate in a distributed model across multiple edge devices cooperatively, or between edge and Cloud VMs. This is well suited for streaming execution of micro-batch datasets, and can be coupled with other specialized application platforms as well. E.g., one of our applications classifies vehicles from video streams using a Tensorflow deep neural network encapsulated within a NiFi dataflow executing across multiple Pis. This helps with local analytics of video data streams close to the camera source, but with the flexibility of using the same deployment in the Cloud too, say, when the edge is constrained. Another emerging edge-centric platform based on Node.js is Node-RED. The potential to design many novel applications exists if such application platforms for the edge become popular.
Edge devices need to be complemented with Cloud resources to help with coordination and also to off-load computing when the edge is over loaded, draining battery, or needs access to large datasets. An edge-only solution, such as Peer-to-Peer (P2P) computing, poses unnecessary complexity given the ready availability of Cloud resources. Fog computing, where accelerated servers or mini-clusters are available close to the edge (e.g., every city block), will also become feasible within city infrastructure deployments.
Security, privacy and trust are additional concerns, but one can make arguments both in favor and against the use of edge and/or Cloud resources. Edge devices may be fully controlled by the end-user or utility, but their location in public spaces makes them physically accessible, while public Clouds with multi-tenancy may pose limitations for highly sensitive data. The choice depends on individual IoT applications.
Lastly, we need to consider the reliability and scalability of edge devices. Using edge platforms for generic applications makes them less resilient than embedded applications, and the application should be robust to such faults. Connectivity with the Cloud may also be intermittent, and mobility of the edge devices raise additional issues. These will all have to be carefully considered before edge devices take-off as ubiquitous computing platforms to design the next wave of innovative IoT applications.
- Edge-centric Computing: Vision and Challenges, Pedro Garcia Lopez, et al, ACM SIGCOMM Computer Communication Review, Volume 45 Issue 5, October 2015.
- Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions, Prateeksha Varshney, Yogesh Simmhan, in IEEE International Conference on Fog and Edge Computing, 2017.
Yogesh Simmhan is an Assistant Professor of Computational and Data Sciences at the Indian Institute of Science, Bangalore. His research explores abstractions, algorithms and applications on distributed systems, spanning Cloud and Edge Computing, Internet of Things, and Big Data platforms. He has applied these to several Smart City projects including the Los Angeles Smart Grid and the IISc Smart Campus.
Yogesh has won the IEEE/ACM HPC Storage Challenge and IEEE TCSC SCALE Challenge, and has over 75 refereed publications. He is a Senior Member of IEEE and ACM. He has a Ph.D. in Computer Science from Indiana University, and was earlier affiliated with Microsoft Research, and the University of Southern California. He can be reached at email@example.com and www.dream-lab.in.
How the IoT Can Bring About a Circular Economy
How the IoT Can Bring About a Circular Economy
The circular economy describes sustainable business principles whereby components and raw materials are continually recycled. Through their reuse, the materials retain their value and this reduces the need to use up more precious resources. Additionally, the price stimulus known as ‘the polluter pays’ is applied consistently. This leads to efficient processes and the use of clean energy.
The benefits of the circular economy are huge. By viewing used products as a source of valuable materials, highly interesting commercial opportunities arise. Additionally, in this way, we pass on a healthy world to our descendants. However, both producers and consumers appear to be having difficulty in applying the ideas of the circular economy in practice. This is a shame, as it means we are passing up opportunities to develop our economies in a beneficial way. In general, there are two main ways to stimulate a circular economy: through legislation and through new business models. Together with my colleagues Elmer Rietveld and Oscar Rietkerk, I believe that the key to achieving new circular business models lies in digital technology. I will explain why.
Take, for example, your hot water boiler at home (assuming you own one). No doubt you chose your boiler in part based on the advertised energy conversion efficiency. But the installation engineer had no real reason to adjust the settings optimally – as long as it worked, he would get paid. And now, you probably have no idea what level of energy conversion your boiler is actually achieving.
The same can be said of cars: last year car manufacturers such as Volkswagen and Mitsubishi had to own up to deceiving their customers with false fuel efficiency data for many years. How were we to know?
In the near future, your hot water boiler will be connected via the internet of things and you will be able to monitor real-time how it is performing. Service engineers will be able to inspect and optimize its performance from a distance, and replace parts just before they fail.
This will change the boiler industry for good. The manufacturer will learn like never before about the functioning of its products in situ. This information will provide invaluable insights into how to improve the design.
But, if you think it through, the end user would be even better off if she did not need to buy a hot water boiler at all. If she only paid for the amount of hot water that she used. Via this business model, the producer remains the owner and is responsible for the operational costs. Which is an extra stimulus to make sure everything is performing optimally.
When the sensor data indicate that the efficiency is declining, the boiler can be replaced and all the parts can be revamped and given a new life in other products. Also, in this way, the producer can learn how to develop the design of their products such that it becomes easy to repair and reuse the components.
As such, by making use of the data from smart products, the circular economy is not only possible, it is also profitable: both for the user and the producer.
This development creates a number of new and very interesting challenges which firms are going to have to face. First, in the area of customer relationship management. The smart boilers, cars, home appliances, TVs, bicycles and you name it, will really become profitable once firms learn how to adapt their products to each consumers’ usage patterns. If a city bike indicates that it is not being used, and is available to others, the customer pays less. For the sport-lover’s weekend cycling break along a part of the Tour de France, the city bike can be exchanged for a racer. Service provision is continually made to measure. Firms will therefore have to create better systems for learning what their users’ needs are at any given moment. And if this is done well, a strong and loyal customer relationship will develop.
A second challenge for new business models based on the principles of the circular economy, is the sharing of data through the supply chain. Firms are now hesitant to do this because they are fearful. As soon as you give another organization access to your data, your competitors might gain an advantage. But this is a debilitating thought that prevents extra value creation. By sharing data through the business ecosystem, according to suitable contractual agreements, it is possible for suppliers and producers to profit from each other’s data, so that the end user is served well.
A highly promising development, recommended by the European Resource Efficiency Platform, is a passport for each product which records the raw materials the product is made of, and how it can be repaired or recycled. Not a paper passport, but a chip with all the necessary information that can be accessed from a distance.
Firms will then be able to showcase their products made from parts that are already enjoying their tenth reincarnation!
In the long term, the only truly sustainable economy is a circular economy. The internet of things offers a practical way to create that future and to create new business opportunities. Increasing numbers of firms are taking steps to lead the way in this direction. Is your firm among them?
Dr. David J. Langley works at TNO, the largest applied research institute in the Netherlands, as senior scientist in the field of internet, innovation and strategy. He received his PhD in 2009 and is also Associate Research Fellow at the department of Innovation Management and Strategy at the University of Groningen. Since 2015 he is columnist for the Dutch Financial Times writing about the societal implications of internet technologies. He is co-chair of the European Alliance for IoT Innovation (AIOTI), Ecosystems working group. Working in the area of internet innovations since 1991, he has set up and led research projects in a wide variety of industries, including telecoms, energy, banking and the public sector. He is the inventor of an award-winning method to predict the adoption of innovative products and services at an early stage of their development. His scientific research has been published in leading journals, including Technological Forecasting and Social Change, Journal of Interactive Marketing and Journal of Product Innovation Management.
Can a Remote IoT Stop Fuel Theft in Africa?
Can a Remote IoT Stop Fuel Theft in Africa?
This article illustrates a concrete application scenario where the power of IoT is harnessed to solve a concrete problem with a practically immediate return on investment. In particular, it shows an IoT-based system, developed to implement a financial management system for fuel stations in Africa. The problem seems to be local to Africa, but building this IoT system has three key learnings that are global in nature. The IoT system aspires to solve a regional problem. It connects remote, poor connectivity locations. It motivates the formation of a creative secure deployment.
IoT to Solve a Regional Problem
In Africa, salespeople dispense fuel in fuel stations by hand and keep account of it manually. This leads to fuel theft by some dishonest employees. Individuals privately own groups of fuel stations. These enterprising fuel station owners end up with financial losses because of lack of ability to monitor the fuel dispensed remotely. We connected a pulsar alongside the nozzle of each dispenser system in the fuel station. We linked the pulsar with the LCD (screen) to show the amount of fuel sold. The Automatic Dispenser system interfaces with the fuel pulsar to get each pulse in real-time. The data pulled from the pulsar is pushed to the remote server along with date and time, and the name of the sales representative.
At server side, the data is analyzed to report how much fuel was sold. The system saves the amount of fuel sold per day, per week, per month. It also emails this report to the owner of the fuel station. This transparency of fuel pumped by each employee helps to deter fuel theft and save the fuel station businesses.
Connectivity for Remote Locations
The connectivity choice made applies to any IoT system in a remote location where there are multiple options, but none are reliable all the time. It was important to ensure that the system was safe from hackers and theft. So a combination of GPRS, SMS and voice call remote connectivity options was chosen. In particular, a mobile sim card, and a GPRS enabled modem were used. The network could switch between different available mobile carrier networks and GPRS connectivity was used to push the data to the Internet. The fuel station managers have remote access so a fuel station owner can check the fuel pump usage remotely at any time.
To ensure security the Server Name/IP address that made the data uploads were tracked. Same for the path on the server that ran the server-side script responsible for processing and storing information sent from the dispenser, which is saved with a high level of encryption for protection and can be only decrypted with a functional key from the microprocessor. These provided enhanced security and kept the system safe from tampering, making it very reliable.
Creative Secure Deployment
In remote fuel stations of Nigeria, there is no tech support personnel available. Therefore, the software and setup for the initial configurations was bundled at deployment with configuration parameters set to enable remote access control and detect unauthorized tampering. To ensure a secure deployment, the unit was programmed to detect unauthorized opening of the system without first disarming it. The system also records the date and time of each usage and forwards this information along with the next upload (litres dispensed) as a bit position in a 16-bit word value. The system guarantees continuous logging of dispenser operation with flexible user notification interfaces. This system monitors fuel level relative to a reference height using high-end industrial sensors. The system is designed to accommodate different storage tank configurations. Due to its flexible programming interface, various parameters settings allow the detection of a tank full or empty, tank temperature and percentage volume.
The system transmits the internal status of the storage container in real-time to remote technical personnel and it has many modes to accommodate the varying needs of the customers. Alarms can be configured to initiate upload when a set of user-set conditions are met. Users with registered accounts can easily access these servers for system log visualization and downloads as well as for unattended site management. Eight different alarms combination defines system’s monitoring bounds. Four output actuators operated using a mathematical formula applied to the monitored inputs and the user-set alarm conditions.This article shows a different perspective on IoT coming from a developing Country where the lack of telecommunication infrastructure and different problems people deal with in their day-by-day living, create situations substantially different than what normally hits the headlines. Here IoT, with its low-cost, wide coverage and easy to integrate solutions, is used to address a concrete problem (fuel theft) in a region with unreliable connectivity, producing a return on (low) investment which is almost immediate, saving money for fuel dispensing station owners keeping their local business alive.
Oluwatobi Oyinlola (IEEE member) is an embedded system engineer with years of experience. He is technical lead and an embedded system engineer for various companies in Nigeria, adopting embedded systems in consumer products for smart homes, smart agriculture and smart industry. He is also member of the IoT Council in Europe and recently he has been working in the avionics sector with rLoop Incorporated (a company sharing the dream of realizing the fifth mode of transportation initiated by Elon Musk, i.e. the Hyperloop).
Key Elements and Enablers for Developing an IoT Ecosystem
Key Elements and Enablers for Developing an IoT Ecosystem
The Internet of Things (IoT) is one of today’s most widely discussed technology topics. From smart agriculture through to smart cities to smart factories, the expectation is that IoT will be transformative. The 4th industrial revolution. However, the reality is that IoT still remains a promise. And, more significantly, IoT remains fragmented. Indeed, most of the applications that do exist are vertical solutions that do not represent a dynamic, interconnected world that the name, internet-of-things, would suggest. One key cause is the lack of true IoT ecosystems.
The IoT ecosystem
Clearly, no company has the capabilities and resources to do it all in the IoT. Instead, businesses targeting this opportunity will always be part of an ecosystem. This means that ecosystems are ultimately the competitive unit in the IoT – and that the battle will be between these ecosystems, not between individual companies. Moreover, there will not be single but many interlinked ecosystems. An ecosystem of ecosystems if you will.
Notably, an ecosystem is more than a set of arms-length partnerships. It is a network of independent contributors who interact closely to create mutual value. This, in turn, creates interdependency among partners in the ecosystem. All partners share the same fate – individual partners will be successful only if the ecosystem is successful. This complex dynamic presents a challenge for businesses trying to figure out an IoT strategy. A better understanding of how ecosystems are created is required.
Key elements and enablers for developing an IoT ecosystem
As shown in Figure 1, there are three main elements that make up a successful IoT ecosystem: these are an IoT platform, the market expectation and the network effects.
Figure 1: Elements and enablers of an IoT ecosystem.
The platform is a key building block of the ecosystem and the focus of much investment and commentary in the industry. Examples include Microsoft’s Azure IoT suite and AWS IoT. Whereas this element is key, it is the other two that are more nuanced and challenging for businesses to figure out. Indeed, building an IoT ecosystem is a complex undertaking with many interconnected factors that need to be juggled with. Supporting an ecosystem requires more than just having a platform and making APIs available to third parties. Companies offering platforms need to be able to create the right incentives (financial and other kinds), support systems for partners, and define how they – and not competing players – will create more value for their partners.
There are a number of key enablers that enterprises should focus on, when developing their IoT ecosystems. These are briefly discussed below.
- Enabling platforms: as mentioned above, platforms are the foundation of the ecosystem. Businesses need to deploy IoT platforms that fulfil the expectations of both customers and partners in terms of functionality, reliability, security and flexibility. The platform needs to enable not only vertical solutions, but a true ecosystem in the form of a marketplace for IoT products and services.
- APIs: APIs are the basic building blocks of an IoT ecosystem, and businesses must therefore develop a strong API strategy. This strategy should be based on a deep understanding of the IoT markets that the business intends to target. Designing and supporting APIs for everyone is impractical, which means that a focused approach is recommended. The business should also develop an API roadmap that is in line with its overall IoT strategy, while the API pricing and support model must be aligned with the business’ ecosystem revenue model. APIs can ultimately foster – or discourage – network effects. If using your APIs is too onerous or does not create sufficient value, ecosystem partners will be reluctant to invest time or effort. It is therefore vital that businesses define their API strategies with market and partner needs in mind.
- Communities: for ecosystems to be true ecosystems, communities of partners need to exist. These partners should be able to develop products and services based on the company resources (via APIs), as well as those of other ecosystem participants. The benefits to businesses can be immense. By enabling others to invest and create new products and services, the business is able to boost innovation. This is achieved without incurring every cost and risk involved, but by sharing these with the ecosystem partners. Companies like IBM, Amazon and Microsoft are very active in this area sponsoring hackathons and sponsoring university research programs and incubators.
- Own branded services: in many cases, it makes sense for businesses to offer complete IoT solutions, either with their own products or through integration with partners. This to signal commitment to market and to kick-start the ecosystem expansion. A good example is Digital Life from AT&T, a telco in the US - the company has developed an integrated home monitoring service together with partners, and markets the service as an AT&T-branded product. This branded service serves to signal AT&T’s commitment to the IoT and, as the service establishes itself in the market, AT&T is looking at opening it to a wider array of partners, thus further developing the initial ecosystem.
- Revenue models: revenue models are a key aspect for the successful development of IoT ecosystems. Businesses looking to attract ecosystem partners need to define the right revenue generation and sharing model – one that incentivizes partners to join the ecosystem, reduces risks for partners to innovate and fits with the business model of the individual partners. Some partners will be attracted to a revenue sharing model, while others will prefer a licencing or fixed royalty-based model. Models like “freemium” can be good to encourage experimentation and early adoption in IoT communities. This means that firms will need to support several revenue and partnership models, which in turn will require new decision and management systems.
- Ecosystem support functions: the final (and perhaps most overlooked) enabler is the internal organization and the related support functions. A critical function here is partner management, which not only means being able to recruit but to incentivize and support ecosystem partners throughout the partnership lifecycle. This is a capability that goes beyond basic reseller agreements. Businesses will also require dedicated teams to support the ecosystem. This support includes technical (e.g. how to use an API) but also marketing (e.g. sell your apps on our marketplace) and operational (e.g. “fulfilled by Amazon”).
Moreover, a governance model that establishes clear ‘ecosystem rules’ is critical in order to maintain harmony among members and a healthy cooperative ecosystem.
In the battle to establish leadership in the IoT, ecosystem will ultimately be the competitive unit. At the same time, building an IoT ecosystem is a complex undertaking that requires many interconnected factors to be balanced. The challenge for businesses is to establish an IoT ecosystem strategy that is holistic, taking into account all the elements described above and adopt an ecosystem mindset that moves away from vertical value chains with one set of customers at the end of it. Indeed, when it comes to developing an IoT ecosystem, businesses need to take the view that they have customers not only in the form of end-customers, but intermediate customers in the form of partners who need attracting, supporting and delighting just as much as an end customer.
Omar Valdez-de-Leon is a Principal Consultant in Ericsson’s Consulting & Systems Integration organization. Additionally, he provides independent advisory services and lectures on digital transformation.
Over the years Omar has worked across IT, telecom and industry in companies such as Bosch, Logica-CGI, Elster and Vodafone, with a focus on new business initiatives grounded in emerging technologies, including machine-to-machine and the Internet of Things.
His experience in IoT ranges from advising utility companies on smart grid strategies to devising IoT plans for large Telecom operators and smaller start-ups. He has also built, launched and managed IoT solution portfolios in utilities, transportation and retail.
Omar holds an MSc in Technology & Innovation Management from the University of Sussex in the UK, and an MBA at Manchester Business School, UK.