Tag Archives: IoT

LiDAR: The Next Revolutionary Technology and What You Need to Know

In an era of rapid technological growth, certain technologies, such as artificial intelligence and the internet of things, have received mass adoption and become household names. One up-and-coming technology that has the potential to reach that level of adoption is LiDAR.

WHAT IS LIDAR?

LiDAR, or light detection and ranging, is a popular remote sensing method for measuring the exact distance of an object on the earth’s surface. Initially used in the 1960s, LiDAR has gradually received increasing adoption, particularly after the creation of GPS in the 1980s. It became a common technology for deriving precise geospatial measurements.

LiDAR requires three components: the scanner, laser, and GPS receiver. The scanner sends a pulsed laser to the GPS receiver to calculate an object’s variable distances from the earth surface. The laser emits light which travels to the ground and reflects off things like buildings, tree branches and more. The reflected light energy then returns to the LiDAR sensor where the associated information is recorded. In combination with photodetector and optics, it allows for an ultra-precise distance detection and topographical data.

WHY IS LIDAR IMPORTANT?

As we covered in our rundown of the iPhone 12, new iOS devices come equipped with a brand new LiDAR scanner. LiDAR now enters the hands of consumers who have Apple’s new generation of devices, enabling enhanced functionality and major opportunities for app developers. The proliferation of LiDAR signals toward the technology finding mass adoption and household name status.

There are two different types of LiDAR systems: Terrestrial and Airborne. Airborne LiDAR are installed on drones or helicopters for deriving an exact measurement of distance, while Terrestrial LiDAR systems are installed on moving vehicles to collect pinpoints. Terrestrial LiDAR systems are often used to monitor highways and have been employed by autonomous cars for years, while airborne LiDAR are commonly used in environmental applications and gathering topographical data.

With the future in mind, here are the top LiDAR trends to look out for moving forward:

SUPERCHARGING APPLE DEVICES

LiDAR enhances the camera on Apple devices significantly. Auto-focus is quicker and more effective on those devices. Moreover, it supercharges AR applications by greatly enhancing the speed and quality of a camera’s ability to track the location of people as well as place objects.

One of the major apps that received a functionality boost from LiDAR is Apple’s free Measure app, which can measure distance, dimensions, and even whether an object is level. The measurements determined by the app are significantly more accurate with the new LiDAR scanner, capable of replacing physical rulers, tape measures, and spirit levels.

Microsoft’s Seeing AI application is designed for the visually impaired to navigate their environment, however, LiDAR takes it to the next level. In conjunction with artificial intelligence, LiDAR enables the application to read text, identify products and colors, and describe people, scenes, and objects that appear in the viewfinder.

BIG INVESTMENTS BY AUTOMOTIVE COMPANIES

LiDAR plays a major role in autonomous vehicles, relying on a terrestrial LiDAR system to help them self-navigate. In 2018, reports suggest that the automotive segment acquired a business share of 90 percent. With self-driving cars inching toward mass adoption, expect to see major investments in LiDAR by automotive companies in 2021 and beyond.

As automotive companies look to make major investments in LiDAR, including Volkswagen’s recent investment in Aeva, many LiDAR companies are competing to create the go-to LiDAR system for automotive companies. Check out this great article by Wired detailing the potential for this bubble to burst.

LIDAR DRIVING ENVIRONMENTAL APPLICATIONS

Beyond commercial applications and the automotive industry, LiDAR is gradually seeing increased adoption for geoscience applications. The environmental segment of the LiDAR market is anticipated to grow at a CAGR of 32% through 2025. LiDAR is vital to geoscience applications for creating accurate and high-quality 3D data to study ecosystems of various wildlife species.

One of the main environmental uses of LiDAR is for soliciting topographic information on landscapes. Topographic LiDAR is expected to see a growth rate of over 25% over the coming years. These systems can see through forest canopy to produce accurate 3D models of landscapes necessary to create contours, digital terrain models, digital surface models and more.

CONCLUSION

In March 2020, after the first LiDAR scanner became available in the iPad Pro, The Verge put it perfectly when they said that the new LiDAR sensor is an AR hardware solution in search of software. While LiDAR has gradually found increasing usage, it is still a powerful new technology with burgeoning commercial usage. Enterprising app developers are looking for new ways to use it to empower consumers and businesses alike.

For supplementary viewing on the inner workings of the technology, check out this great introduction below, courtesy of Neon Science.

How AI Fuels a Game-Changing Technology in Geospatial 2.0

Geospatial technology describes a broad range of modern tools which enable the geographic mapping and analysis of Earth and human societies. Since the 19th century, geospatial technology has evolved as aerial photography and eventually satellite imaging revolutionized cartography and mapmaking.

Contemporary society now employs geospatial technology in a vast array of applications, from commercial satellite imaging, to GPS, to Geographic Information Systems (GIS) and Internet Mapping Technologies like Google Earth. The geospatial analytics market is currently valued between $35 and $40 billion with the market projected to hit $86 billion by 2023.

GEOSPATIAL 1.0 VS. 2.0

geospatial

Geospatial technology has been in phase 1.0 for centuries; however, the boon of artificial intelligence and the IoT has made Geospatial 2.0 a reality. Geospatial 1.0 offers valuable information for analysts to view, analyze, and download geospatial data streams. Geospatial 2.0 takes it to the next level–harnessing artificial intelligence to not only collect data, but to process, model, analyze and make decisions based on the analysis.

When empowered by artificial intelligence, geospatial 2.0 technology has the potential to revolutionize a number of verticals. Savvy application developers and government agencies in particular have rushed to the forefront of creating cutting edge solutions with the technology.

PLATFORM AS A SERVICE (PaaS) SOLUTIONS

Effective geospatial 2.0 solutions require a deep vertical-specific knowledge of client needs, which has lagged behind the technical capabilities of the platform. The bulk of currently available geospatial 2.0 technologies are offered as “one-size-fits-all” Platform as a Service (PaaS) solutions. The challenge for PaaS providers is that they need to serve a wide collection of use cases, harmonizing data from multiple sensors together while enabling users to simply understand and address the many different insights which can be gleaned from the data.

shutterstock_754106473-768x576

In precision agriculture, FarmShots offers precise, frequent imagery to farmers along with meaningful analysis of field variability, damage extent, and the effects of applications through time.

Mayday

In the disaster management field, Mayday offers a centralized artificial intelligence platform with real-time disaster information. Another geospatial 2.0 application Cloud to Street uses a mix of AI and satellites to track floods in near real-time, offering extremely valuable information to both insurance companies and municipalities.

SUSTAINABILITY

The growing complexity of environmental concerns have led to a number of applications of geospatial 2.0 technology to help create a safer, more sustainable world. For example, geospatial technology can measure carbon sequestration, tree density, green cover, carbon credit & tree age. It can provide vulnerability assessment surveys in disaster-prone areas. It can also help urban planners and governments plan and implement community mapping and equitable housing. Geospatial 2.0 can analyze a confluence of factors and create actionable insight toward analyzing and honing our environmental practices.

As geospatial 1.0 models are upgraded to geospatial 2.0, expect to see more robust solutions incorporating AI-powered analytics. A survey of working professionals conducted by Geospatial World found that geospatial technology will likely make the biggest impact in the climate and environment field.

CONCLUSION

Geospatial 2.0 platforms are very expensive to employ and require quite a bit of development.  The technology offers great potential to increase revenue and efficiency for a number of verticals. In addition, it may be a key technology to help cut down our carbon footprint and create a safer, more sustainable world..

AIoT: How the Intersection of AI and IoT Will Drive Innovation for Decades to Come

We have covered the evolution of the Internet of Things (IoT) and Artificial Intelligence (AI) over the years as they have gained prominence. IoT devices collect a massive amount of data. Cisco projects by the end of 2021, IoT devices will collect over 800 zettabytes of data per year. Meanwhile, AI algorithms can parse through big data and teach themselves to analyze and identify patterns to make predictions. Both technologies enable a seemingly endless amount of applications retained a massive impact on many industry verticals.

What happens when you merge them? The result is aptly named the AIoT (Artificial Intelligence of Things) and it will take IoT devices to the next level.

WHAT IS AIOT?

AIoT is any system that integrates AI technologies with IoT infrastructure, enhancing efficiency, human-machine interactions, data management and analytics.

IoT enables devices to collect, store, and analyze big data. Device operators and field engineers typically control devices. AI enhances IoT’s existing systems, enabling them to take the next step to determine and take the appropriate action based on the analysis of the data.

By embedding AI into infrastructure components, including programs, chipsets, and edge computing, AIoT enables intelligent, connected systems to learn, self-correct and self-diagnose potential issues.

960x0

One common example comes in the surveillance field. Surveillance camera can be used as an image sensor, sending every frame to an IoT system which analyzes the feed for certain objects. AI can analyze the frame and only send frames when it detects a specific object—significantly speeding up the process while reducing the amount of data generated since irrelevant frames are excluded.

CCTV-Traffic-Monitoring-1024x683

While AIoT will no doubt find a variety of applications across industries, the three segments we expect to see the most impact on are wearables, smart cities, and retail.

WEARABLES

Wearable-IoT-Devices

The global wearable device market is estimated to hit more than $87 billion by 2022. AI applications on wearable devices such as smartwatches pose a number of potential applications, particularly in the healthtech sector.

Researchers in Taiwan have been studying the potential for an AIoT wearable system for electrocardiogram (ECG) analysis and cardiac disease detection. The system would integrate a wearable IoT-based system with an AI platform for cardiac disease detection. The wearable collects real-time health data and stores it in a cloud where an AI algorithm detects disease with an average of 94% accuracy. Currently, Apple Watch Series 4 or later includes an ECG app which captures symptoms of irregular, rapid or skipped heartbeats.

Although this device is still in development, we expect to see more coming out of the wearables segment as 5G enables more robust cloud-based processing power, taking the pressure off the devices themselves.

SMART CITIES

We’ve previously explored the future of smart cities in our blog series A Smarter World. With cities eager to invest in improving public safety, transport, and energy efficiency, AIoT will drive innovation in the smart city space.

There are a number of potential applications for AIoT in smart cities. AIoT’s ability to analyze data and act opens up a number of possibilities for optimizing energy consumption for IoT systems. Smart streetlights and energy grids can analyze data to reduce wasted energy without inconveniencing citizens.

Some smart cities have already adopted AIoT applications in the transportation space. New Delhi, which boasts some of the worst traffic in the world, features an Intelligent Transport Management System (ITMS) which makes real-time dynamic decisions on traffic flows to accelerate traffic.

RETAIL

AIoT has the potential to enhance the retail shopping experience with digital augmentation. The same smart cameras we referenced earlier are being used to detect shoplifters. Walmart recently confirmed it has installed smart security cameras in over 1,000 stores.

smart-shopping-cart

One of the big innovations for AIoT involves smart shopping carts. Grocery stores in both Canada and the United States are experimenting with high-tech shopping carts, including one from Caper which uses image recognition and built-in sensors to determine what a person puts into the shopping cart.

The potential for smart shopping carts is vast—these carts will be able to inform customers of deals and promotion, recommend products based on their buying decisions, enable them to view an itemized list of their current purchases, and incorporate indoor navigation to lead them to their desired items.

A smart shopping cart company called IMAGR recently raised $14 million in a pre-Series A funding round, pointing toward a bright future for smart shopping carts.

CONCLUSION

AIoT represents the intersection of AI, IoT, 5G, and big data. 5G enables the cloud processing power for IoT devices to employ AI algorithms to analyze big data to determine and enact action items. These technologies are all relatively young, and as they continue to grow, they will empower innovators to build a smarter future for our world.

How App Developers Can Leverage the iPhone 12 to Maximize Their Apps

On October 23rd, four brand new iPhone 12 models were released to retailers. As the manufacturer of the most popular smartphone model in the world, whenever Apple delivers a new device its front-page news. Mobile app developers looking to capitalize on new devices must stay abreast of the latest technologies, how they empower applications, and what they signal about where the future of app development is headed.

With that in mind, here is everything app developers need to know about the latest iPhone models.

BIG DEVELOPMENTS FOR AUGMENTED REALITY

LiDAR is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor

LiDAR is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor

On a camera level, the iPhone 12 includes significant advancements. It is the first phone to record and edit Dolby Vision with HDR. What’s more, Apple has enhanced the iPhone’s LiDAR sensor capabilities with a third telephoto lens.

The opportunities for app developers are significant. For AR developers, this is a breakthrough—enhanced LiDAR on the iPhone 12 means a broad market will have access to enhanced depth perception, enabling smoother AR object placement. The LIDAR sensor produces a 6x increase in autofocus speed in low light settings.

The potential use cases are vast. An enterprise-level application could leverage the enhanced camera to show the inner workings of a complex machine and provide solutions. Dimly lit rooms can now house AR objects, such as Christmas decorations. The iPhone 12 provides a platform for AR developers to count on a growing market of app users to do much more with less light, and scan rooms with more detail.

The iPhone 12’s enhanced LiDAR Scanner will enable iOS app developers to employ Apple’s ARKit 4 to attain enhanced depth information through a brand-new Depth API. ARKit 4 also introduces location anchors, which enable developers to place AR experiences at a specific point in the world in their iPhone and iPad apps.

With iPhone 12, Apple sends a clear message to app developers: AR is on the rise.

ALL IPHONE 12 MODELS SUPPORT 5G

5G 2

The entire iPhone 12 family of devices supports 5G with both sub-6GHz and mmWave networks. When iPhone 12 devices leverage 5G with the Apple A14 bionic chip, it enables them to integrate with IoT devices, and perform on ML algorithms at a much higher level.

5G poses an endless array of possibilities for app developers—from enhanced UX, more accurate GPS, improved video apps, and more. 5G will reduce dependency on hardware as app data is stored in the cloud with faster transfer speeds. In addition, it will enable even more potential innovation for AR applications.

5G represents a new frontier for app developers, IoT, and much more. Major carriers have been rolling out 5G networks over the past few years, but access points remain primarily in major cities. Regardless, 5G will gradually become the norm over the course of the next few years and this will expand the playing field for app developers.

WHAT DOES IT MEAN?

Beyond the bells and whistles, the iPhone 12 sends a very clear message about what app developers can anticipate will have the biggest impact on the future of app development: AR and 5G. Applications employing these technologies will have massive potential to evolve as the iPhone 12 and its successors become the norm and older devices are phased out.

The Future of Indoor GPS Part 2: Bluetooth 5.1’s Angle of Arrival Ups the Ante for BLE Beacons

In the last installment of our blog series on indoor positioning, we examined an overview of the top indoor positioning technologies. This week, we will examine the most precise and popular method: Bluetooth BLE Beacons and how Bluetooth 5.1 enables them to be the most popular indoor positioning tool on the market.

As the world transitions into a wireless society, Bluetooth technology has evolved and gained more and more popularity. Apple’s decision to remove 1/8th inch audio ports from their devices, while irksome to many consumers, was a definitive move in the direction of Bluetooth.

The growing market for indoor positioning has incentivized an evolution in the landscape of Bluetooth technology. The first consumer bluetooth device was launched in 1999. This year, the world is forecasted to ship more than 4.5 billion Bluetooth devices worldwide. Behind the scenes, manufacturers are using Bluetooth technology for asset tracking and warehouse management. Bluetooth 5.1 technology, in concert with Bluetooth BLE Beacons, is the most popular indoor positioning method.

Nordic nRF52840-Dongle

Nordic nRF52840-Dongle

BLUETOOTH 5.1

Announced in January 2019 by the Bluetooth Special Interest Group (SIG), Bluetooth 5.1 is the latest and most powerful iteration of Bluetooth technology yet.

Bluetooth 5.1 can connect with other devices at a distance of 985 feet, quadruple Bluetooth 4.0. Bluetooth 5.1 improves upon Bluetooth 4.0’s indoor positioning capabilities with Angle of Arrival (AoA) and Angle of Departure (AoD) features. When used for indoor location, Bluetooth 5.1 can provide up to 1-10 centimeters of accuracy with very little lag. At 48MBps, Bluetooth 5.1 is twice as fast as Bluetooth 4.0.

In addition to being faster and more powerful, Bluetooth 5.1 is the continuation of Low Energy LE, consuming less power than previous iterations of Bluetooth.

INDOOR POSITIONING

Bluetooth BLE Beacons are attached to objects, vehicles, devices, etc. and used to track their location. Bluetooth BLE beacons enable Bluetooth devices to communicate with IoT products and other devices. The top suppliers in the  beacon space include Kontakt, Blukii, Minew, Gimbal, Estimote, and EM Microelectronic.

AoA and AoD features are at the core of what enhances positioning technologies in Bluetooth 5.1.

Angle of Arrival diagram via ScienceDirect.com

Angle of Arrival diagram via ScienceDirect.com

In AoA, the  device or tag transmits a specific direction-finding packet using one antenna. The receiving device receives the incoming signal with multiple antennas, each antenna receiving the signal at slightly different times relative to each other. An algorithm factors in the shifts in signal and yields precise coordinate information.

AoD flips the scenario. The device sending the signal has an array of antennas and transmits a packet via the antenna ray. The receiving device then makes an IQ sampling of its antenna to determine the coordinate calculation.

USE CASES

Enhanced indoor positioning enables a number of use cases. In sports stadiums and music venues,  a locating hub near the center of the arena can receive signals from devices using AoA technology and determine location coordinates. Keys, perhaps the most commonly lost object, can be embedded with a sensor and located using a locator hub equipped by a smart home.

Bluetooth BLE Beacons, harnessing Bluetooth 5.1, remain the most cost and energy-efficient method of attaining precise indoor positioning locations.

Stay tuned for the next entry in our Indoor Positioning blog series which will explore the wonders of Ultra-Wideband (UWB) technology!

A Smarter World Part 4: Securing the Smart City and the Technology Within

In the last installment of our blog series on smart cities, we examined how smart transportation will make for a more efficient society. This week, we’ll examine how urban security stands to evolve with the implementation of smart technology.

Smart security in the modern era is a controversial issue for informed citizens. Many science fiction stories have dramatized the evolution of technology, and how every advance increases the danger of reaching a totalitarian state—particularly when it comes to surveillance. However, as a society, it would be foolish to refrain from using the technical power afforded to us to protect our cities.

Here are the top applications for smart security in the smart cities of the future:

Surveillance

minority-report-iris-scan-blog-hero-778x391

Surveillance has been a political point of contention and paranoia since the Watergate scandal in the early 1970s. Whistleblower Edward Snowden became a martyr or traitor depending on your point of view when he exposed vast surveillance powers used by the NSA. As technology has rapidly evolved, the potential for governments to abuse their technological power has evolved with it.

Camera technology has evolved to the point where everyone has a tiny camera on them at all time via their phones. While monitoring entire cities with surveillance feeds is feasible, the amount of manpower necessary to monitor the footage and act in a timely manner rendered this mass surveillance ineffective. However, deep learning-driven AI video analytics tools can analyze real-time footage and identify anomalies, such as foreboding indicators of violence, and notify nearby law enforcement instantly.

In China, police forces use smart devices allied to a private broadband network to discover crimes. Huawei’s eLTE system allows officers to swap incident details securely and coordinate responses between central command and local patrols. In Shanghai, sophisticated security systems have seen crime rates drop by 30% and the amount of time for police to arrive at crime scenes drop to 3 minutes.

In Boston, to curb gun violence, the Boston police force has deployed an IoT sensor-based gunfire detection system that notifies officers to crime scenes within seconds.

Disaster Prevention

shutterstock_457990045-e1550674981237

One of the major applications of IoT-based security system involves disaster prevention and effective use of smart communication and alert systems.

When disasters strike, governments require a streamlined method of coordinating strategy, accessing data, and managing a skilled workforce to enact the response. IoT devices and smart alert systems work together to sense impending disasters and give advance warning to the public about evacuations and security lockdown alerts.

Cybersecurity

The more smart applications present in city infrastructure, the more a city becomes susceptible to cyber attack. Unsecured devices, gateways, and networks each represent a potential vulnerability for a data breach. The average cost of a data breach according to IBM and the Poneman Institute is estimated at $3.86 million dollars. Thus, one of the major components of securing the smart city is the ramping up of cybersecurity to prevent hacking.

smart-city-1 graphic

The Industrial Internet Consortium are helping establish frameworks across technologies to safely accelerate the Industrial Internet of Things (IIot) for transformational outcomes. GlobalSign works to move secure IoT deployments forward on a world-wide basis.

One of the first and most important steps toward cybersecurity is adopting standards and recommended guidelines to help address the smart city challenges of today. The Cybersecurity Framework is a voluntary framework consisting of standards, guidelines, and best practices to manage cybersecurity-related risk published by the National Institute of Standards and Technology (NIST), a non-regulatory agency in the US Department of Commerce. Gartner projects that 50% of U.S. businesses, critical infrastructure operators, and countries around the globe will use the framework as they develop and deploy smart city technology.

Conclusion

The Smart City will yield a technological revolution, begetting a bevy of potential applications in different fields, and with every application comes potential for hacker exploitation. Deployment of new technologies will require not only data standardization, but new security standardizations to ensure that these vulnerabilities are protected from cybersecurity threats. However, don’t expect cybersecurity to slow the evolution of the smart city too much as it’s expected to grow into a $135 billion dollar industry by 2021 according to TechRepublic.

This concludes our blog series on Smart Cities, we hope you enjoyed and learned from it! In case you missed it, check out our past entries for a full picture of the future of smart cities:

A Smarter World Part 1: How the Future of Smart Cities Will Change the World

A Smarter World Part 2: How Smart Infrastructure Will Reshape Your City

A Smarter World Part 3: How Smart Transportation Will Accelerate Your Business

A Smarter World Part 3: How Smart Transportation Will Accelerate Your Business

In the last installment of our blog series on smart cities, we examined how smart infrastructure will revolutionize smart cities. This week, we will examine the many applications which will soon revolutionize smart transportation.

A smarter world means a faster, more efficient and environmentally-friendly world. And perhaps the biggest increase in efficiency and productivity will be driven by the many ways in which AI can optimize the amount of time it takes to get where you’re going.

Here are the top applications in smart transportation coming to a city near you:

Autonomous-vehicle-AdobeStock_174958313_rm

AUTONOMOUS VEHICLES

Some say autonomous vehicles are headed to market by 2020. Others say it could take decades before they are on the road. One thing is for certain, they represent a major technological advancement for smart transportation. Autonomous cars will communicate with each other to avoid accidents and contain state-of-the-art sensors to help keep you and your vehicle safe from harm.

Although autonomous vehicles are arguably the largest technological advancement on the horizon, they will also benefit greatly from a variety of smart transportation applications that will accelerate navigating your local metropolis.

Integrated-Roadways

SMART ROADS

What if we could turn roads into a true digital network, giving real-time traffic updates, supporting autonomous car technology, and providing true connectivity between vehicles and smart cities?

That’s the question tech start-up Integrated Roadways intends to answer. Integrated Roadways develops fiber-connected smart pavement outfitted with a vast amount of sensors, routers, and antennae that send information to data centers along the highway. They recently inked a 5 year deal to test out patented fiber-connected pavement in Colorado.

Smart Roads represent a major advancement in creating vehicle-to-infrastructure (V2I) connectivity. With 37,133 deaths from motor vehicles on American roads in 2017, the combination of AI applications in smart roads and autonomous cars could revolutionize vehicular transport and create a safer, faster world.

smart-traffic-lights-1_AMaJV_24429

SMART TRAFFIC LIGHTS

The vehicle-to-infrastructure connectivity spans beyond the roads and into the traffic light. Idling cars generate an estimated 30 million tons of carbon dioxide. Traffic jams can make it harder for first responders to reach emergencies. Rapid Flow proposes that the answer may be their AI-based adaptive traffic management system called Surtrac.

Surtrac uses a decentralized network of smart traffic lights equipped with cameras, radar, and other sensors to manage traffic flows. Surtrac’s sensors identify approaching vehicles, calculate their speed and trajectory, and adjust a traffic signal’s timing schedule as needed.

red line bus

SMART PUBLIC TRANSIT

There are a variety of smart applications which are revolutionizing public transportation.

In Singapore, hundreds of cameras and sensors citywide analyze traffic congestion and crowd density, enabling government officials to reroute buses at rush hour, reducing the risk of traffic jams. In Indianapolis, the electric Red Line bus service runs a 13 mile path that travels within a quarter of a mile of roughly 150,000 jobs.

One of the major disruptors which has seen rapid adoption in the smart public transport are electric scooter sharing services like Bird and Lime. Electric scooters fill in the public transportation gap for people looking to go 1-3 miles without having to walk or take a taxi. Electric scooters have seen adoption in Los Angeles, San Francisco, Salt Lake City, Brooklyn, and more cities around the globe.

CONCLUSION

Smart cities will have a host of revolutionary applications working in unison and communicating through smart infrastructure with municipalities to ensure maximum efficiency and safety when it comes to transportation. In our next installment of our series on smart cities, we’ll examine how smart security will help keep city-dwellers safe.

A Smarter World Part 2: How Smart Infrastructure Will Reshape Your City

Imagine a city that monitors its own health, identifies potential fail points using AI algorithms, and autonomously takes action to prevent future disasters.

This is the smart-city of the future. In our first installment of our blog series on Smart Cities, we ran through an overview of how Smart Cities will change our world. In this second entry of our blog on smart cities, we’ll examine perhaps the biggest building block necessary to create a smart city: smart infrastructure.

The construction of a smart city begins with developing a vast, city-wide IoT system, embedding sensors and actuators into the infrastructure of the city to create a network of smart things. The sensors and actuators collect data and send it to field gateways which preprocess and filter data before transmitting it through a cloud gateway to a Data Lake. The Data Lake stores a vast amount of data in its raw state. Gradually, data is extracted for meaningful insights and sent to the Big Data warehouse where it’s structured. From here, monitoring and basic analytics will occur to determine potential fail points and preventative measures.

Check out the breakdown below:

Breakdown

As you can see, it all begins with the construction of smart infrastructure that can collect data. Here are some of the big applications in the smart infrastructure space:

STRUCTURAL HEALTH

One of the major applications of smart infrastructure will be monitoring key data points in major structures, such as the vibrations and material conditions of buildings, bridges, historical monuments, roads, etc.

Cultivating data will initiate basic analysis and preventative measures, but as we gather more and more data, AI and machine learning algorithms will learn from vast statistical analysis and be able to analyze historical sensor data to identify trends and create predictive models to prevent future disasters from happening with unprecedented accuracy.

Learn more about how Acellant is building the future of structure health monitoring.

ENVIRONMENTAL APPLICATIONS

There are a multitude of potentially environmental applications for smart infrastructure designed to optimize city activities for environmental health. For example, embedding street lights with intelligent and weather adaptive lighting will reduce the amount of energy necessary to keep roads alight.

Air pollution monitoring will help control CO2 emissions of factories and monitor the pollution emitted by cars. Ultimately, earthquake early detection can help monitor distributed control in specific places of tremors.

2017-05-08-151015.975562trashcity

WASTE MANAGEMENT

Boston is well-known as one of the top college cities in the United States. Every fall, over 160,000 college students from MIT, Harvard, Northeastern, BU, BC, Berklee School of Music, and more move in to their new living spaces, causing undue stress on the city’s waste management administration. ANALYZE BOSTON, the city’s open data portal, provided key data points such as housing rentals, trash volume and pick-up frequency, enabling a project called TRASH CITY to reroute waste management routes during this trying time.

CONCLUSION

Projects like Trash City show the many ways in which we can optimize city operations by analyzing data effectively. As smart infrastructure enables the collection of more and more data, projects like TRASH CITY will become more efficient and more effective.

Of course, the biggest application of Smart Infrastructure will be the many ways in which it will change how you get from A to B. Next week, we’ll focus in on smart transportation and how it will reshape metropolitan transportation.

How 5G Will Enable the Next Generation of Healthcare

In the past month, we’ve explored 5G, or fifth generation cellular technology, and how 5G will shape the future. In this piece, we’ll spotlight the many ways in which 5G will revolutionize the healthcare industry.

DATA TRANSMISSION

Many medical machines like MRIs and other imaging machines generate very large files that must then be sent to specialists for review. When operating on a network with low bandwidth, the transmission can take a long time or not send successfully. This means patients must wait even longer for treatment, inhibiting the efficiency of healthcare providers. 5G networks will vastly surpass current network speeds, enabling healthcare providers to quickly and reliably transport huge data files, allowing patients and doctors to get results fast.

EXPANDING TELEMEDICINE

why-use-telemedicine

A study by Market Research Future showed that the future of telemedicine is bright—an annual growth rate of 16.5% is expected from 2017 to 2023. 5G is among the primary reasons for that level of growth. In order to support the real-time high-quality video necessary for telemedicine to be effective, hospitals and healthcare providers will need 5G networks that can reliably provide high-speed connections. Telemedicine will result in higher quality healthcare in rural areas and increased access to specialists around the world. Additionally, 5G will enable growth in AR, adding a new dimension to the quality of telemedicine.

REMOTE MONITORING AND WEARABLES

It’s no secret that 5G will enable incredible innovation in the IoT space. One of the ways in which IoT will enable more personalized healthcare involves wearables. According to Anthem, 86% of doctors say wearables increase patient engagement with their own health and wearables are expected to reduce hospital costs by 16% in the next five years.

Wearables like Fitbit track health information that can be vital for doctors to monitor patient health and offer preventative care. While the impact may initially be negligible, as technology advances and more applications for gathering data through wearables emerge, 5G will enable the high-speed, low-latency, data-intensive transfers necessary to take health-focused wearables to the next level. Doctors with increased access to patient information and data will be able to monitor and ultimately predict potential risks to patient health and enact preventative measures to get ahead of health issues.

Companies like CommandWear are creating wearable technology that helps save lives by enabling first responders to be more efficient and more conveniently communicate with their teams.

ARTIFICIAL INTELLIGENCE

In the future, artificial intelligence will analyze data to determine potential diagnoses and help determine the best treatment for a patient. The large amounts of data needed for real-time rapid machine learning requires ultra-reliable and high-bandwidth networks—the type of networks only 5G can offer.

One potential use case for AI in healthcare will be Health Management Systems. Picture a system that combines the Internet of Things with cloud computing and big data technology to fully exploit health status change information. Through data-mining, potential diseases can be screened and alarmed in advance. Health Management Systems will gradually receive mass adoption as 5G enables the data-transmission speeds necessary for machine learning to operate in the cloud and develop algorithms to predict future outcomes.

MAJOR PLAYERS

Right now, the major players who serve to benefit from 5G are the telecom companies developing technology that will enable mass adoption. Companies like Huawei Technologies, Nokia, Ericsson, Qualcomm, Verizon, AT&T, and Cisco Systems are investing massive sums of money into research and development and patenting various technologies, some of which will no doubt become the cornerstones of the future of healthcare.

Qualcomm recently hosted a contest to create a tricoder—a real life device based on a machine in the Star Trek TV movie franchise. Tricoders are portable medical devices that would enable patients to diagnose 13 conditions and continuously monitor five vital signs.

For a full list of major players in the 5G game, check out this awesome list from GreyB.

CONCLUSION

With human lives at stake, healthcare is the sector in which 5G could have the most transformative impact on our society. As the Qualcomm Tricoder contest shows, we are gradually building toward the society previously only dreamed about in sci-fi fiction–and 5G will help pave the way.

Everything You Need to Know About Machine Learning

A calculator can solve complex problems which would take even the most savvy mathematicians an incomparable amount of time. Artificial intelligence has become one of the most hotly debated and highly funded aspects of technology because the speed at which machines can process information yields innumerable possibilities and applications which can and will benefit humanity. One of the first popular incarnations of AI is Machine Learning.

Machine Learning is the ability for a computer to learn without being explicitly programmed. Machine learning focuses on computer programs which can identify patterns and create its own algorithms when exposed to new data. It is used in self-driving cars, in newsfeed algorithms on social media, in evaluating job candidates, in recognizing faces on your phone, and more.

The most powerful form of machine learning currently active is called “deep learning”. “Deep learning” builds a complex mathematical structure known as a neural network out of vast quantities of data. Machine learning’s ability to handle mass amounts of data makes it crucial to the advancement of IoT. The IoT collects enormous amounts of data which require computers with machine learning to recognize patterns and create algorithms.  In self-driving cars, IoT cameras and sensors in each autonomous vehicle absorb their surroundings and turn them into huge amounts of data. The data is then sent to the cloud where it is accessible to all autonomous vehicles on the road. Thus, when one self-driving car makes a mistake, all of them learn. In conjunction with the Internet of Things, machine learning will be vital to the building of a smartworld.

TOP PROGRAMMING LANGUAGES

Machine learning requires a great deal of statistical analysis; it demands an intelligent programming language which can process a number of complex issues and general paradigms.

R: Considered a statistical workhorse, R has emerged as one of the top programming languages for machine learning. R is intended for advanced users because of its complex nature and wide learning curve.

Python: A rising star for machine learning, Python is a data science book which has been in use in the manufacturing industry for awhile. Python gives users direct access to predictive analytics, making it the foremost data science language. Developers turn to Python when they are looking to frame better questions or expand the capabilities of their existing machine learning systems.

MATLAB/Octave: Millions of engineers are already using MATLAB, a matrix-based language, to analyze and develop cutting edge systems. MATLAB has emerged as the simplest way to demonstrate computational mathematics.

MACHINE LEARNING AND iOS 10

Machine learning laid much of the groundwork for the biggest upgrade in iOS 10. It is very difficult for computers to comprehend the intricacies of the human language. Machine learning has enabled iPhones to sense contextual clues with increasing confidence, improving iMessage’s ability to autocorrect and for Siri to understand the particulars of your vernacular. In the iPhone 7 camera, machine learning allows the device to separate the background from the foreground to achieve amazing portraits once possible only with DSLR cameras.

MACHINE LEARNING AND ANDROID

Google is among the dominant forces in machine learning. Much of Google Search’s prominence is owed to advances in the machine learning field. In November 2015, Google released TensorFlow, an open-source software library for machine intelligence. TensorFlow effectively simulates “deep learning” neural networks across different computer hardware and offers a straightforward way for users to train computers to perform tasks by feeding them large amounts of data.

Google uses Tensorflow in many of their internal processes, including RankBrain for information retrieval, image classification, SmartReply, and more.

MAXIMIZING MACHINE LEARNING IN MOBILE APPS

Now that mobile devices have the high productive capacity level to perform tasks to the same degree as a traditional computer, the question of what machine learning can offer apps has arisen. Large retailers like Amazon and eBay use machine learning in their mobile apps to improve customer experience with smarter product search and recommendation features, along with the ability to forecast buying trends with analytics.

While Machine Learning algorithms require a high level of programming experience and a ton of data to be effective, integrating apps with Siri & iMessage for iOS 10 allows developers to take advantage of the vast deep learning neural networks embedded into Apple’s 1st-party apps.

While the future of machine learning  on a commercial level remains to be seen outside of tech titans like Facebook, machine learning algorithms will be crucial in conjunction with the IoT in building a new SmartWorld with unparalleled predictive capabilities.