TABLE OF - Mouser Electronics · 2019-07-09 · data, Li-Fi uses infrared, ultraviolet, and visible light waves, which offer several advantages. Whereas the Wi-Fi spectrum is crowded

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TABLE OFCONTENTS

Mouser and Mouser Electronics are registered trademarks of Mouser Electronics, Inc. Other products, logos, and company names mentioned herein may be trademarks of their respective owners. Reference designs, conceptual illustrations, and other graphics included herein are for informational purposes only. Copyright © 2019 Mouser Electronics, Inc.—A TTI and Berkshire Hathaway company.

LEDs & Li-Fi Brighten the Future of Connected Lighting SystemsPaul Golata3

13 Dual, Segmented Routers Ideal for Transit System TrafficMouser Electronics Staff

Microgrid Vision Stalls but Smart Automation Offers RebootSteven Keeping15

Will IoT Security Solutions Trickle Down or Bubble Up? Steven Evanczuk17

19 Threat Modeling: Risk Assessments for Long-Term IoT SuccessSteven Evanczuk

24 Video: Connected InfrastructureMouser Electronics Staff

7 Pollution Solution: How the IoT Will Clear City AirSteven Keeping

Empowering Innovation Together

Jack Johnston, Director Marketing Communication

Executive Editor

Deborah S. Ray

Contributing Authors

Steven EvanczukPaul GolataSteven Keeping

Technical Contributor

Paul GolataNihar Kulkarni Christina Unarut

Editorial Contributors

LaKayla Garrett

Design & Production

Michael DuFault

With Special Thanks

Kevin Hess Sr. VP, Marketing

Russell Rasor VP, Supplier Marketing

Jennifer KrajcirovicDirector, Creative Design

Raymond YinDirector, Technical Content

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Until recently, lighting systems operated as localized points of illumination. This purpose is evolving, though, as our dependency on connectivity and data continue to increase. Connected lighting systems (CLSs) blend LED capabilities with IoT connectivity, with the goal of enabling design engineers to simultaneously provide illumination and transfer data. With the IoT as its data-collection platform, CLS will save electrical energy while opening up new gamuts of services and benefits for people and organizations.

At the core of CLSs is an emerging optical wireless communication technology called Li-Fi, which is short

for Light Fidelity. Introduced in 2011, Li-Fi uses IEEE 802.11 protocols and has gained momentum toward a viable alternative to the ever-shrinking Wi-Fi spectrum and toward making lighting a central point in smart infrastructure. This article explores the role of IoT technologies, the potential of Li-Fi to transfer data through lighting, and technologies that need to be developed to bring connected lighting systems to their potential.

The Role of IoTThe explosion in the IoT is a technological revolution. Smart sensor technology and wireless connectivity have led design

engineers to focus on how to sense and collect data, as well as to get it into a digital domain where it can be handled and manipulated on the Web. Sensors, connectivity, and digital data comprise the foundation for further CLS development:

Optical SensorsSensors are everywhere, measuring aspects like humidity, temperature, pressure, air quality, vibration, and volatile organic compounds (VOC), to name a few. Sensors detect analog signals generated from the physical world and then convert them into digital data signals that can be controlled and manipulated by embedded systems (Figure 1).

By Paul Golata, Mouser Electronics

An emerging communication technology called Li-Fi offers the potential for connected lighting systems to both provide light and transfer data.With these capabilities, lighting systems could offer an alternative to theever-shrinking Wi-Fi spectrum and make lighting the hub in smart infrastructure.

LEDS & LI-FI BRIGHTEN THE FUTUREOF CONNECTED LIGHTING SYSTEMS

Optical sensors convert light energy into readable electrical signals that can detect whether the lights are on or off and detect the light’s intensity.

Wireless Connectivity Wi-Fi is a technique that allows devices to connect to a wireless local area network (WLAN). Wi-Fi uses electromagnetic radiation in the radio frequency (RF) spectral range (20kHz–300GHz), at five primary frequency ranges: 2.4GHz, 3.6GHz, 4.9GHz, 5GHz, and 5.9GHz. Currently, most consumer products focus on one or both of the 2.4GHz and 5GHz frequencies. Wi-Fi has drawbacks, however, including being susceptible to hacking and a spectrum that’s already nearing capacity.

Digital DataDigital data is in an easy form to store for future use. Combined with a future where artificial intelligence (AI) and Big Data Analytics (BDA) curate, apply, extend, and leverage this data, the design aim is to couple illumination systems into this overarching technological framework. The IoT promises to enable a variety of end-to-end solutions that are more intelligent.

Li-Fi: Optical Wireless CommunicationCLSs blend LED capabilities with IoT connectivity with the goal of enabling design engineers to provide illumination and transfer data simultaneously. At the core of these systems is solid-state lighting (SSL), which is lighting that uses light-emitting diodes (LEDs) as sources of illumination, rather than filaments, plasma, or gas. LEDs offer designers a palette of options, such as quick modulation between ON and OFF, and the ability to control colors and output lumens. They’re also known to be long lasting, compact, durable, and energy efficient, which make them a good basis for expanded uses.

An optical wireless communication (OWC) technology called Light Fidelity—or Li-Fi—has evolved to deliver data via visible light. Whereas Wi-Fi uses radio waves to deliver data, Li-Fi uses infrared, ultraviolet, and visible light waves, which offer several advantages. Whereas the Wi-Fi spectrum is crowded to the point of nearing crisis, the visible

Li-Fi spectrum is nearly 10,000 times larger than its RF counterpart. Li-Fi can also offer data rates that are competitive with Wi-Fi, with reliable, high-quality transmissions speeds up to >30Mbps. What’s more, Li-Fi uses Line of Sight (LoS) architecture, which makes it highly immune to hacking; data is usually lost or destroyed when a hacker intercepts the data stream.

An LED’s ability to modulate ON and OFF quickly is key to why Li-Fi works: Data is moved and handed from one location to another through these modulation and demodulation schemes. Li-Fi works by taking the streaming data content and inserting it into an SSL driver. This SSL driver is capable of running a string of LED lamps and turning them ON and OFF at high speeds. As the LED lamps are turned ON and OFF, strobed faster than the eye can see, they illuminate the area of context.

Within this area is a Li-Fi dongle, an integrated device that connects to a computer and contains a

photodetector to sense the light. It responds by producing an electric current in proportion to the amount of light impinging on its surface. The tiny electrical signal passes into an electronics circuit with amplifiers that boost the signal. Further signal conditional and processing occur before the signal leaves the dongle through a wireless connection to a device, such as a laptop, portable computer, mobile device, or mobile phone.

A Unified Whole: Products, Systems, and SoftwareSeveral years ago, LED manufac-turers began prioritizing a unified

Figure 1: Sensors receive analog signals from the physical world and convert them into digital data signals that can be controlled by embedded systems. (Source: Mouser)

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“An LED’s ability to modulate ON and OFF quickly is key to why Li-Fi works...”

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solution—lighting capable of interacting with all the various electro-mechanical systems in one place. With the boom in IoT, this initiative revealed three levels of differentiated and sustainable support needed to enable the potential of connected lighting systems: Hardware, IoT, software, and interface assets.

Hardware Assets Hardware assets require the packaging of LED components and the electronic drivers to control the current to the LED components. Additionally, it also takes into account the related electro-optical-mechanical design efforts to see the realization of these devices into successful luminaries. Hardware design may require thermal heat sinking, mounting, packaging, optical control, and integration with common and necessarily applicable design codes with items that the luminaire will be interfacing with.

The addition of intelligent sensors so that analog information can be obtained and converted to digital is also part of this design effort. In short, it requires the suppliers to be concerned with how all the related hardware components will work together.

IoT AssetsHardware, however, is not enough. A robust CLS design requires additional measures to connect and enable it to successfully connect to and work in synergy with other lighting systems. Designers must address system integration issues, including data security. How will data be secured and kept from unauthorized users that attempt to take control? Data security may require work performed in the area of authentication. Authentication

protects sophisticated keys that authorize access. Backend storage, computers servers, and analytics also require thought. When entering into the IoT, CLS designers must ensure that the pathway to get data onto and off the CLS is available, reliable, and robust.

Software AssetsThe IoT provides a broad array of services never before possible by way of the cloud. In the new world of IoT, CLS providers that provide cloud-based technology services to support their offerings will have a competitive advantage because they will allow their customers to obtain maximum benefit from their products immediately.

Future interoperability is paramount to fast growth. The need for standardizing solutions, allowing multiple suppliers’ products to work seamlessly together over time provides economic incentives and benefits. Approaches that cannot work well with others will find themselves at a competitive disadvantage as the need to be able to cross domains and boundaries without issues will be primary.

InterfacesAnother area that CLS suppliers must address is common application programming interfaces (APIs). The US Department of Energy (US-DoE) realizes that this is a major hurdle to achieve some of the desired energy benefits of CLS. SSL is more efficient saving on energy. However, SSL in the form of CSL connected to the IoT may allow the cities of tomorrow

to be even more efficient. Increased efficiency is due to the reality that CSL will allow them to be coupled into the smart grid and allow offices, building, homes, retail, street lights, and similar to be coordinated to work so that they are all optimized as a whole system instead of individually. The data obtained by smart sensors combined with data analytics would work to make adjustments that save money for each area that would not be possible if they were left unconnected from the other. The US-DoE suggests that APIs be made readily available for users, the lighting industry looks at adopting common approaches to security and should seek to minimize system integration issues for the industry.

ConclusionLED lighting has made it so that lighting can move from analog to digital and make life better by adding to it the benefits of IT by connecting to the IoT. Li-Fi offers the potential for connected lighting systems to both provide light and transfer data. With these capabilities, lighting systems could offer an alternative to the ever-shrinking Wi-Fi spectrum and make lighting the hub in smart infrastructure. Connected lighting innovation promises a future that is even brighter than the world we live in today. But like with many future innovations, seeing is believing.

Powering InnovationConnection to a power source and ability to supply the precise voltage at the required wattage to all of the circuitry are important design considerations. Regardless of the industry or application, IoT devices are required to work autonomously for long periods of time while consuming little power. Also, power semiconductors, voltage regulators, inductors and other passive components support the development of robust, low-power architectures today’s infrastructure IoT systems demand.

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“Future interoperability is paramount to fast growth...”

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POLLUTION SOLUTION:HOW THE IOT WILL CLEAR CITY AIRBy Steven Keeping for Mouser Electronics

Burgeoning populations bring traffic congestion, which in turn produces dangerous pollution—a problem that has long been identified but that city planners have struggled to solve. The IoT offers an answer, and some fledgling systems are now in place that bring hope tomorrow’s cities will breathe easier.

Cities are powerful magnets for people. According to the United Nations (UN), nearly half of the planet’s population live in a conurbation of more than 500,000, and this number is set to rise to over two-thirds by 2050. The number of “megacities” (those with populations exceeding 10 million) has increased from 10 in 1990 to 28 today and is forecast to increase to 41 by 2030.

This isn’t necessarily bad news; well-managed metropolises encourage economic development and improve job prospects, housing, electricity, water, sanitation, transportation, healthcare, and education for their inhabitants. And providing services is much less expensive and more environmentally sustainable than doing so for a rural population.

But cities have problems that multiply as their sizes grow. One of the most intractable is traffic congestion. Congestion clogs a city’s arteries and stalls economic processes, but its impact on human health is even worse. Traffic continuously pumps noxious emission particles up to 2.5µm in diameter (and smaller) atmospheric particulate matter (PM) into the air. This “PM2.5” pollution penetrates deep into the lungs and is deemed the most hazardous environmental dangers to health.

Conventional attempts to control traffic and limit pollution—such as congestion charging or preventing certain vehicles from traveling on specific days—are cumbersome

and fail to account for factors involving weather and transient traffic conditions like road work or accidents.

Thankfully, now widespread deployment of commercial air-quality sensors—wirelessly connected to the Internet of Things (IoT) via low-power wide-area networks (LPWANs)—promises to generate the fine-grained data that city planners need to be more reactive to the build-up (and clearing) of atmospheric pollution. Some pioneering authorities are embracing the fledgling IoT to clean up their cities’ air but much more needs to be done to implement pollution control systems that befit the smart cities of tomorrow.

“Car”maggedonIn the United States (US), Henry Ford’s production line was credited with introducing independent mobility to the masses, and city planners were quick to embrace this cultural revolution. For example, 1920s’ Los Angeles (LA) design was pretty much dictated by the car. The city’s low population density and far-flung suburbs were created because of the ease with which people could move around using cars during a decade of rapid expansion from 1910. Today, LA’s traffic system has become a victim of its own success; in 2017, the city’s congestion was the world’s worst for the sixth year in a row, according to INRIX, a transportation analyst. The company says that LA’s commuters are destined to spend over 100 hours a year battling peak-hour congestion.

But congestion is not a uniquely American problem. In China and India, countries where rapidly expanding middle classes are spending much of their disposable income on cars, the problem is bad and set to get much worse. For example, in Mumbai, India, with a metropolitan population of around 18.5 million inhabitants and

2.3 million vehicles (a 55 percent increase in the last seven years), new vehicle registrations numbered around 700 a day in 2017. In Beijing, China, the vehicle fleet is equally impressive, numbering 5.97 million for a metropolitan population of 21.7 million (Figure 1).

Sources of Particulate Pollutionand Its EffectsThere are many sources of PM2.5 pollution, including coal- and oil-fired power stations, domestic cooking, and even natural sources like dust and sea salt. However, a study in Gwangju, Korea, showed that over a third of the PM in that city was

attributable to diesel and gasoline-powered vehicles.

It’s a similar story in Beijing; in late March 2017, the city’s PM2.5 concentration was 238µg/m3, and the city averaged 90µg/m3 over the year. World Health Organization

(WHO) guidelines state that an average PM2.5 reading of just 25µg/m3 over a 24-hour period is unhealthy. The Chinese city’s inhabitants are paying a steep price for mobility; a 2016 report by Nanjing University’s School of the Environment concluded that 31.8 percent of all deaths in Beijing (and

Figure 1: Beijing’s congestion is extreme. (Source: Mouser)

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other congested cities in China) could be linked to PM2.5.

While not as bad as Beijing, Mumbai, with its large car fleet and a bar set low for emission standards, often suffers from appalling air quality. The Times of India reported that the city is ranked the 63rd most polluted among a list of 859 cities around the world and is the fourth most polluted megacity. In 2016, the city’s mean PM2.5 level was 64µg/m3. While Beijing and Mumbai are outliers, Western cities have no reason to be smug. LA records an average annual PM2.5 reading of 18µg/m3. In Europe, Paris recorded a peak reading of 55µg/m3 in February 2018.

According to the WHO, air pollution is mainly responsible for noncommunicable diseases, causing around 24 percent of all adult deaths from heart disease, 25 percent from stroke, 43 percent from chronic obstructive pulmonary disease, and 29 percent from lung cancer.

Pollution Mitigation 1.0Several city authorities have grappled with the task of cleaning up their citizens’ air. Some attempts are more bizarre than others. According to the BBC (a United Kingdom (UK) broadcaster), Delhi, India, has toyed with the idea of mounting jet engines onto flatbeds to tow them to heavily polluted areas to use the engines’ thrust to push particulates high into the atmosphere and away from lungs. And Beijing has recently introduced a permit system, limiting non-residents to only 12 drives into the city per year.

More conventional attempts to control airborne particulates include the deployment of monitoring equipment over a notional pollution hotspot for a few months to establish

a pattern, which is then followed by harsh measures like limiting access or ramping up emission taxes to discourage traffic until the air becomes clearer. Such initiatives fail to consider transient factors like weather and auto accidents and that the heavy-handed solutions frustrate drivers and generally produce short-term results. Using Conventional Technologyto Limit Particulate PollutionBy deploying contemporary electronics such as sensors and web cameras, some cities are achieving better results. The US city of Chicago, Illinois, for example, uses lamp post-mounted sensors to build up a picture of pollution over time in large areas of the city, while Pittsburgh, Pennsylvania, citizens can use webcams to zoom in on specific sources of emissions and make recordings of pollution events to identify a pattern. Elsewhere, Louisville, Kentucky, identifies pollution hotspots by gathering data about when asthma patients use inhalers. Outside of the US, Oslo, Norway, allows bus-lane access, provides lots of recharging stations and privileged parking, and eliminates tolls for electric vehicles. Norway has the highest per capita number of all-electric cars in the world, more than 100,000 for a country of 5.2 million people. Oslo’s mean PM2.5 reading in 2016 was 11µg/m3. Dresden, Germany, is trying to filter particulates from the air by implementing pollution-absorbing “green walls” (Figure 2).

Connectivity Improves Pollution ControlInitiatives cutting the number of cars entering cities are bound to have some impact on air quality. Fewer tailpipes equal lower emissions.

However, the impact is often not as high as anticipated. London, for example, introduced a congestion charging scheme in 2003. By 2014, traffic volumes in the charging zone were nearly a quarter lower than a decade ago. But the number of buses has increased, and taxi and private hire vehicle journeys have risen by nearly 30 percent since 2000.

Has London’s congestion charge improved the air for the city’s population? In 2003, the city’s mean PM2.5 reading was 25µg/m3 (rising to 35µg/m3 in central areas), while in 2016 it was 15µg/m3 (rising to 18µg/m3 in central areas). That’s a marked improvement, but the air quality is well above the WHO threshold of 10µg/m3.

What London’s experience demonstrates is that tackling air pollution involves dozens of dependent factors, and changing one can (sometimes detrimentally) affect others. To react quickly to pollution peaks and bring down the mean

level over time requires data—lots of timely and accurate data.

The IoT’s capacity to quickly generate, collate, and analyze data through compact, inexpensive wireless sensors, which connect to the cloud via LPWANs, will enable city planners to take advantage of “big data” to improve air pollution control.

Building an IoT Air PollutionControl SystemAn IoT-based air pollution system will comprise four essential elements:

• Wireless air quality sensors to monitor and report pollution levels

• LPWAN connectivity to transmit data from short-range wireless sensor networks to the cloud

• Cloud servers with the power to analyze data from tens of thousands of wireless sensors

• Predictive algorithms to suggest measures to prevent air pollution building to hazardous levels

Compact, inexpensive, battery-powered PM2.5 sensors are becoming commercially available. Alternatives are the metal-oxide semiconductor (MOS) gas sensors, which can be “tuned” to pick up the nitrogen dioxide (NO2) and carbon monoxide (CO) prevalent in exhaust gases, to give an overall indication of the air pollution created by vehicles. These sensors are commonly paired with a radio-frequency (RF) transceiver using a battery-friendly wireless protocol such as Bluetooth Low Energy (BLE) or Zigbee to allow data to continuously travel across a network. Because PM2.5 and gas sensors are cheap and unobtrusive, they can be widely distributed across a city to monitor air pollution.

LPWANs form robust, long-range, secure connections between the local area networks (LANs) of short-range wireless sensors and the cloud. Several LPWAN technologies are now commercialized including cellular IoT technologies, such as Long-Term Evolution for machines (LTE-M) and Narrowband IoT (NB-IoT), LoRaWAN, Sigfox, and Weightless (Figure 3).

Once the data is uploaded in a cloud via an LPWAN, it can be aggregated and analyzed to build an almost real-time, fine-grained picture of how air pollution is changing throughout a city. As the historical database builds, algorithms can refer to past events to accurately predict how future patterns will play out—enabling authorities to take preventative measures if necessary. This information will allow more subtle actions than traditional measures such as regulating traffic flows, temporarily lowering road tolls for cleaner vehicles, and quickly advising citizens via a cellular network or Internet to avoid areas that might soon become hazardous.

Early AdoptersLondonLondon, England, is building on its experience with congestion control by introducing an IoT-based trial in the Royal Borough of Greenwich, in collaboration with the GSM Association (an alliance of cellular infrastructure providers). The project, dubbed “Smart London” uses a combination of sensors, cellular IoT

LPWANs, and big data analytical techniques. The sensor network comprises low-cost static and mobile devices attached to vehicles, bikes, and people. Its data is complemented by data from existing Greenwich air-quality monitoring stations.

This information is now allowing authorities to make early decisions. For example, data from air-quality measurements is being used to inform subscribers about a service called “AirTEXT” (via smartphone notifications) that announces when pollution levels are likely to rise in a subscriber’s area. As a result of the data analysis, the borough has introduced pollution mitigating

Figure 2: German cities such as Dresden are using “green walls”to absorb particulate pollution. (Source: Green City Solutions)

Figure 3: LPWANs form long-range connections between wireless sensor networks and the cloud. (Source: Mouser)

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Key Takeaways• Cities are growing, and congestion is growing with them. • Airborne particulates from vehicle exhausts are damaging the health of

city dwellers. • Existing pollution control measures are rudimentary and produce

limited results.• The IoT will provide the rapid, fine-grained data needed to tackle both

long-term and transient episodes of high particulate pollution. • Today’s technology is helpful, but tomorrow’s IoT, combined with AI,

will enhance particulate pollution control.

measures like introducing a ban on large delivery trucks and instead employing a contractor that delivers parcels by bicycle.

While these are the early days of Greenwich’s experiment, the PM2.5 levels are showing a modest decline (Figure 4).

SeoulThis South Korean capital is home to an IoT-based air pollution monitoring scheme operated by a local telecom operator. For a developed country, South Korea’s air is dirty; in late-March 2018, Seoul’s PM2.5 peaked at 100µg/m3. The “Air Map Korea” project collects air-quality data via its nationwide infrastructure, which includes 4.5 million telephone poles, 330,000 mobile base stations, 60,000 public phone booths, and 4,000 central offices in Korea. In addition to PM2.5 and PM10 levels, the sensors also track temperature, noise, and humidity levels. This sensor data is relayed to the company’s existing 4G and new 5G mobile networks via a cellular IoT LPWAN. Once collected, the information is transmitted to the company’s “Air Map Platform” every minute.

So far, Seoul’s air-quality initiative has produced limited results. The city’s government has taken steps to encourage people out of their cars by waiving public transport fees during rush hours, closing parking lots, and introducing fines and bans on certain diesel-fueled vehicles when pollution peaks hit—yet these measures have only reduced traffic by around two percent. PM2.5 improvements over the last five years have been described as “stagnant” by a Korean government spokesperson, according to U.S. News & World Report.

Acting on Air Quality DataWhat London and Seoul’s experience indicates is that while measuring pollution is one thing, acting on the information for meaningful impact is quite another. But we are still in the early days of the IoT, and in the medium-term, city planners will benefit from the large amount of current and historical data building up as a result of widespread monitoring networks and cloud storage.

Improving ResponseHow to react to data generated by the IoT is in large part governed by the political climate, but technology can play a larger role in the decision-making process by ensuring the public receives accurate information in a timely manner. This way citizens are more likely to accept measures that make personal mobility more expensive or even restricted.

Introducing Enhanced Sensor TechnologyAdvances in low-power wireless and semiconductor technology are making air-quality sensors cheaper, smaller, lower in maintenance, more accurate, and longer in range. As a result, future pollution

control schemes will be able to take advantage of a much greater deployment of sensors and improved measurement precision.

Extending Sensor Mesh NetworksBluetooth mesh and Zigbee’s inherent mesh compatibility also aid in widespread sensor deployments. Networking allows sensors to communicate with each other, reducing the number of LPWAN nodes required because data can

be aggregated to and relayed from a single point on a large network. Such an arrangement reduces costs and complexity.

Deploying Citywide LPWANsLPWANs play a key role in building the IoT. Cellular IoT offers an early advantage because 4G and increasingly 5G networks are already established in most cities—allowing early traction for suppliers of metropolitan cellular IoT services. LoRaWAN, Sigfox, and Weightless are busy building up infrastructures across major cities to support their technologies. Once in place, these LPWANs will provide a backbone for a rapid and inexpensive deployment of wireless sensor networks in any area of a city.

Using Artificial Intelligence to Act EarlyBuilding on huge data resources and employing the power of server farms, engineers will develop algorithms that intelligently consider the complex interplay of factors that affect air pollution, produce accurate predictions, and suggest early (and subtle) actions to mitigate hazardous pollution levels. Examples include the air purifying plants that South Korea is building that will automatically switch on when air pollution reaches a threshold as well as China’s drones that spray water or chemicals in pollution hotspots to wash away PM2.5 accumulations.

In SummaryWhile cities remain wedded to diesel and gasoline vehicles, PM2.5 emissions from tailpipes are a potential hazard to human health. Major cities are experiencing a loss of productivity and load on health systems because of respiratory ailments; thus, some cities are making efforts to bring down pollution levels. These pioneering efforts are laudable but expensive to implement, complex to administer, and limited in results.

Increasingly, the IoT is addressing the weaknesses of traditional monitoring systems by generating continuous streams of accurate data in near real time, enabling planners to make more informed decisions to cut traffic flows. Still, the IoT is in its infancy and its full potential for air pollution control won’t be realized until engineers roll out the “next-generation” wireless sensor networks, which are citywide LPWANs and 5G cellular infrastructures necessary to feed data to artificial intelligence (AI) algorithms, triggering early preventative actions.

Figure 4: Greenwich, London, IoT-based air pollution control is having an impact on PM2.5 levels. (Source: Royal Borough of Greenwich)

“...measuring pollution is one thing, acting on the information for meaningful impact is quite another...”

Sensing EverythingFrom cities and utilities to buildings and roadways, the IoT promises to control and reduce costs for today’s overburdened and aging infrastructure. Sensors provide information about the infrastructure-related IoT devices they monitor. Sensors can identity the type of device to measurements related to the device’s physical state. Overall, having the capability to quickly implement remote monitoring and diagnostic solutions for rugged industrial environments where power is not readily available serves as a big advantage.

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DUAL, SEGMENTED ROUTERS IDEALFOR TRANSIT SYSTEM TRAFFICBy Mouser Electronics Staff

Public transportation Wi-Fi networks pose several engineering challenges in providing fast and reliable service between vehicles, infrastructures, and machines. Dual, segmented routers provide the bandwidth, connectivity, and power essential for this application.

Many municipalities are now using Wi-Fi and the Internet in public transportation infrastructures to address the growing need to connect vehicles and passengers during transportation. Multiple communication modes have created several engineering challenges; fortunately, dual, segmented routers provide the bandwidth, connectivity, and power essential for this application.

Communication ModesCurrent connected-vehicle technology requires that communications flow through a network of wireless radios that are installed within the vehicles and at fixed nodes along transportation routes. This network enables several different communication modes:

Vehicle-to-vehicle (V2V)This connection can involve the transmission and reception of data such as location and speed among vehicles or the facilitation of secure communications. This information can be useful to aid in navigation, tracking, or collision avoidance.

Vehicle-to-infrastructure (V2I)This interface involves the capture of vehicle-generated data (e.g., fuel consumption, speed, and position) and provides information throughout the network of vehicles to improve and coordinate travel schedules, enhance vehicle safety, aid in navigation or traffic avoidance, or communicate environment-related conditions (e.g., weather and road closures).

Machine-to-machine (M2M)This connection includes secure point-of-sale (POS) fare, toll and parking payments, or Wi-Fi access to phones, tablets, or computers that passengers may use to send and receive information, emails, text messages, news, and entertainment from the Internet during their commutes.

Engineering ChallengesSeveral challenges must be overcome to create a secure, reliable, and high-performing transit system Wi-Fi network. Such challenges include:

Limited BandwidthPublic transportation vehicles by design carry numerous passengers, some of which may have multiple

devices that require Wi-Fi connectivity. Systems with limited bandwidth may not have adequate capacity to service these large data demands, leading to slow response times and frustrated passengers.

“Dirty Power”Systems that are not specifically designed for use on vehicles may experience Wi-Fi service interruptions due to variations in power, voltage, frequency, and surges that may disable the systems and lead to significant down times until the systems can receive service.

Poor Connectivityor Limited RangeBecause transit system Wi-Fi networks must communicate through antennas via radio, the availability or strength of a signal is affected by the ability of the vehicle to communicate with the router. Numerous problems can limit Wi-Fi availability, including the number and strength of the transceivers and environmental factors, such as the presence of electromagnetic interference, which may cause drop-outs, dead zones, or weak signals.

RuggedizationSystems that are not designed for operation on moving vehicles may be subject to adverse environmental conditions that can lead to hardware

failure. Some of these environments may include thermal loads, such as temperature extremes and cycling, and mechanical loads, such as exposure to shock and vibration.

SoftwareCentralized software is necessary to communicate with and control the performance of potentially numerous stationary and mobile nodes in the field. This software must be capable of communicating with several vehicles simultaneously and in real time.

Dual, Segmented Routers Provide SolutionToday’s dual, segmented routers provide compact enclosures suitable for rugged transportation environments. The following features are emerging to provide enterprise-class routing, making these routers an ideal choice for challenging transportation and mobile environments:

BandwidthBandwidth limitation issues are being addressed using triple carrier aggregation on each cellular interface, which combined can deliver up to 1.2Gbps to passengers. Onboard systems retain priority and any remaining bandwidth is made available to Internet traffic.

PowerReliable circuitry can support 9 to 36VDC input along with ignition sensing for direct integration into a vehicle’s power source.

ConnectivityConnectivity can be segmented to provide private versus public data communications, enabling the secure management of Internet access for passengers and without an impact on onboard bus systems.

RuggedizationAluminum enclosures can qualify to meet military and transportation standards for temperature, humidity, vibration, shock, and dust environments. Flexible mounting options permit installation of the router to minimize environmental loading.

SoftwareSystems can manage and control devices from a central platform in real time. Software enables system administrators to visually monitor, update, and maintain numerous performance parameters throughout the network in a secure and reliable manner.

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MICROGRID VISION STALLSBUT SMART AUTOMATION OFFERS REBOOTBy Steven Keeping for Mouser Electronics

Microgrids hold the key to integrating green energy, but engineers must embrace smart grid technologies before the hype becomes reality.

Microgrids hold the key to the smart grid. These localized, compact distribution networks promise to answer a triple conundrum facing utility engineers: How to build reliable, resilient, and above all, clean electricity systems. The vision reveals a patchwork of interconnected microgrids coordinated by smart automation. Such microgrids would allow engineers to build an electricity distribution network which reduces reliance on giant, centralized (and dirty) power plants by tapping in to a nationwide distribution of small energy-harvesting renewable energy (RE) sources. These RE sources would not only provide power to the local area, but also smooth power variations in other parts of the grid by exporting excess capacity.

Unfortunately, the hype and the reality are somewhat different. Today in the US thousands of uncoordinated microgrids using polluting fossil-fuel power sources such as diesel generators, that only kick in when the main grid is down, make up the microgrid majority. There is a dearth of RE

and it seems that the microgrid vision has stalled. But there’s hope; modern communication technology, advanced distribution automation equipment, and increasingly efficient wind and photovoltaic (PV) “solar” sources are coming together to reboot the clean power dream.

Bringing Resilience to Electricity GenerationAccording to the National Renewable Energy Laboratory (NREL), a US-based energy research facility, a microgrid enables local power generation assets—including traditional generators, RE, and electricity storage—to service a locality even when the larger grid experiences interruptions or, for remote areas, where there is no connection to the larger grid. Apart from the remote examples, a microgrid normally operates connected to and synchronous with the “macrogrid”, but can also disconnect to function autonomously and in isolation as an “electrical island”.

Resilience is assured because failure of one microgrid has no impact on neighboring networks; each can be rapidly isolated from faulty networks and continue supplying power to its own sector from localized generation. Contrast this with today’s centralized model: Failure of a giant power station impacts a wide area and often causes other plants on the network to trip out (due to the macrogrid’s interconnectedness), plunging whole cities and even regions into darkness.

Moreover, localized networks powered by RE promise to have a dramatic impact on carbon emissions by reducing reliance on fossil-fuel generation.With multiple semi-autonomous microgrids operating in parallel, the resulting smart grid would transform vulnerable networks—where a single point of failure can cause a cascade of shutdowns affecting millions of people—with one which is more efficient, secure, and robust because it offers redundancy and eliminates reliance on a chain with many potential weak links.

Nanogrids, Not MicrogridsThere are around 1900 electricity networks described as “microgrids” in the US alone, according to analyst Wood Mackenzie. That sounds promising but many of these grids fall well short of addressing the triple conundrum facing utility engineers. One problem is these installations include many “microgrids” that are actually closer to “nanogrids”; systems that are often little more than a diesel generator in a hospital basement offering back-up in case the city power goes out, or industrial premises taking advantage of a gas-fired combined heat and power (CHP) unit in an attempt to contain energy costs. Such designs typically fail to meet the NREL definition of a microgrid because they are incapable of feeding excess capacity back into the national system.

Worse yet, because many of those 1900 microgrids rely on fossil-fuel generation they do nothing to reduce carbon emissions. The vision of a wind farm combined with battery storage powering a genteel suburb is far from reality in the US and limited to a handful of examples in environmentally-attuned Europe.

Little will change until there’s a fundamental shift in the way national utility systems are designed. Today these systems typically comprise

a massive network built around a limited number of very large power plants; that network would need to transform into a distributed mesh served by many (comparatively) tiny electricity generators with a good proportion of RE to meet the vision. Today, particularly in the US, momentum towards this transformation is slow—but there is cause for optimism.

Realizing theMicrogrid DreamStone Edge Farm in Sonoma, CA demonstrates how a microgrid should be built. The farm combines conventional power generation from a gas turbine with solar power, stores energy in eight different types of battery, and is designed to power the site’s irrigation system. The farm’s disparate power sources are managed by a “distributed optimizer”; a custom device which manages how the different sources of power are switched in and out.

The microgrid received an unplanned workout during late 2017 Californian wildfires. The farm had to be evacuated as flames came within 8km of the property. But the facility was switched to an island mode remotely and continued to operate autonomously for ten days until it was safe for people to return.

Alcatraz Island is home to another microgrid—claimed to be the US’s largest—powered by a 305kW solar array. The solar panels connect to a battery bank and power inverters that help supply energy to the island instead of Alcatraz relying solely on diesel generators. The microgrid has reduced the island’s fuel consumption by 45 per cent since its 2012 installation.

The Sonoma and Alcatraz microgrids demonstrate what’s possible by leveraging modern RE technology and distribution automation equipment. By aggregating together distributed, small-scale resources linked via Intelligent Electronic Devices (IEDs), a microgrid can connect safely to the grid while maintaining power quality—that’s no mean feat.

Such microgrids make things much easier for utility operators; communicating with hundreds of discrete generators is daunting, but microgrids can gather small resources together into manageable units supplying energy to a wider smart grid when power is in high demand, storing energy when it’s not needed for later release, smoothing out frequency and voltage fluctuations on the wider grid, and operating remotely for the benefit of the local community when things go wrong elsewhere.

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WILL IoT SECURITY SOLUTIONSTRICKLE DOWN OR BUBBLE UP? By Steven Evanczuk for Mouser Electronics

Securing the IoT is complex because of the diverse mix of hardware, software, and data involved across multiple layers of various large-scale applications. Despite the increase in security mechanisms and services, IoT security remains fractured and needs a more unified approach.

To the casual observer, IoT (Internet of Things) security today seems fractured by any measure. Developers can find bits and pieces of security solutions scattered all around. Often, those solutions turn out to be mere mechanisms—pieces of a puzzle that must be hammered together rather than sliding smoothly into place to create a complete security picture. Although that’s an improvement on the recent past, it’s not the kind of security environment that’s necessary to deliver end-to-end security solutions needed in large-scale IoT applications. The question remains: Will the availability of more of those mechanisms create a sort of emergent security solution, or will security solutions eventually

come as prepackaged parts of cloud-based IoT platforms?

IoT security is difficult because IoT applications link together so many different types of systems—from deeply embedded real-time systems at the periphery to cloud-based enterprise-level systems at the top of the IoT hierarchy. Within this layered architecture, data needs to flow efficiently among those systems and combine dynamically with any number of other data streams, enterprise resources, and third-party packages required to meet the overall objectives of an IoT application. Adding to this complexity is the idea that this assembly of hardware and software

will by no means remain static, especially as enterprises shift the focus of individual IoT applications in response to greater insight, innovative technologies, market opportunities, and competitive pressure.

From this perspective, IoT security certainly presents itself as a more traditional enterprise problem, requiring security solutions that start in the cloud and work down the stack. In practice, however, the dynamic, real-time nature of IoT systems dictates the need for solutions that begin on the ground level with IoT terminal devices and edge devices. If these devices are not secure, the IoT application itself

is at risk—as are the enterprise resources connected to it. For this reason, individual mechanisms such as hardware-based secure storage, encryption, authentication, and others remain vital elements of an overall IoT security solution. Building on these mechanisms, more advanced features such as secure over-the-air firmware updates, firmware authentication, and a secure boot are essential for establishing “the hardware root of trust” necessary to provide a secure foundation for the rest of the IoT application.

Indeed, effective IoT security both trickles down from the cloud and bubbles up from the

hardware—melding into a unified framework that neither approach can easily produce in isolation. For this reason, we’ll continue to see tighter integration between IoT hardware devices and cloud platforms. Semiconductor manufacturers already offer preconfigured hardware with keys and certificates for turnkey authentication on IoT platforms. Platform specificity is likely to drive more deeply into hardware with the emergence of IoT offerings, and inevitably, IoT security depends on the application of a combination of separate mechanisms at every level of the hierarchy that act in orchestration as a unified whole. Leading IoT platform providers understand this.

Nonetheless, tying hardware to specific IoT platforms won’t work for every development organization or project. Unique requirements, concerns about lock-in, and fast-moving innovation will drive some IoT applications in other directions. For any IoT application, the effective security solutions will be those that both trickle down from cloud-based capabilities as well as bubble up from hardware-based mechanisms.

2019

THREAT MODELING: RISK ASSESSMENTSFOR LONG-TERM IoT SUCCESSBy Steven Evanczuk for Mouser Electronics

The complexity of an IoT application virtually guarantees a security breach at some point in the system’s life cycle. By applying a methodical approach to security, however, organizations can anticipate threats and lessen the impact of successful attacks.

In combining peripheral sensors, gateways, and cloud resources, Internet of Things (IoT) applications are becoming unprecedented targets because of the number of potential attack surfaces and security vulnerabilities they contain. A clear understanding of such threats, their likelihood, and their impact becomes more urgent as enterprises tie these applications more tightly into corporate infrastructures. Using methodical approaches to threat and risk assessments, development teams can harden security where essential or make informed decisions about acceptable risks.

The wide range of security vulnerabilities in connected systems finds expression all too frequently in news reports. Even a quick dip into the headlines shows a startling breadth of attacks, ranging from overt, massive distributed denial-of-service (DDoS) attacks to extremely covert advanced persistent threats (APTs) that linger and quietly extract valuable data or prepare for more extreme strikes.

Despite the sensationalist nature of these exploits, one of the most important lessons learned from these attacks is that the use of security mechanisms and the creation of a secure system are not the same thing. Hackers successfully penetrate systems that are built with all manner of security mechanisms. Even the most security-conscious development team may unknowingly leave open attack surfaces in their designs.

In fact, the sheer complexity of today’s designs increases the chance of open attack surfaces, particularly with multilayered, connected systems such as IoT applications. When large numbers of programmable devices of different types connect to the cloud, end-to-end security becomes more of a statistical probability than an absolute certainty. Each element in such an interconnected system of systems contributes not only its specific functionality but also its own set of vulnerabilities to the security equation.

By fully understanding how each vulnerability can become a threat to the overall application, an enterprise can decide if the associated risk of a successful exploit of that vulnerability will rise above the threshold of acceptance and ultimately require mitigation.

The ability to gain this level of visibility into the nature of a risk provides strategic value that cannot be overstated. At the same time, by intersecting security vulnerabilities with risk assessments, a development team can devise a tactical roadmap for developing a practical response to the nearly endless stream of threats to any connected system. Indeed, without a more rigorous level of understanding gained through threat and risk assessments, even the most experienced development team is gambling on the security of their systems and applications. Gaining this knowledge, however, starts with a clear understanding of the potential threats against a system, which is achievable through a well-documented threat model.

Threat models capture the specific security vulnerabilities associated with a system’s design. Creating a threat model seems simple conceptually: For example, developers analyze their designs to identify security vulnerabilities that relate to each underlying component. However, in practice, threat modeling can involve much more work, research, and strategy than this simple idea suggests—and can yield far more than a list of technical security concerns. More broadly applied, threat modeling can also identify vulnerabilities in the associated life cycle processes and overarching security policies that correlate with an IoT application. Ultimately, what constitutes acceptable threat models can vary as widely as the IoT applications and organizations they serve. Even so, different threat models share certain characteristics, and any threat modeling methodology will follow a few common steps.

Threat ModelingThreat modeling begins with an accurate description of the system, the so-called Target of Evaluation (TOE), associated with a specific use case, such as the operation of a utility water meter. If a threat model paints a picture of system vulnerabilities, the TOE description is the canvas. By widening or tightening the scope of the TOE, a threat modeling team can expand or contract the focus in a threat identification process. For example, Arm’s recently released smart water-meter threat model sharply restricts its TOE, focusing only on the system’s core (Figure 1).

Of course, a TOE confined within a single subset of a larger, more complex system or application translates to a more limited ability to identify threats, assess risks, and build an effective mitigation plan. For a complex system of systems such as an IoT application, experienced threat modelers might create a series of threat models, going from a fairly abstract description of the complete system to increasingly detailed descriptions of subsystems of particular importance or concern to the organization.

Whatever the approach, there is no absolute requirement for the level of detail required in the TOE description. Modeling approaches that intend to provide the exhaustive details of each component may simply exhaust the participants in the process. On the other hand, models that are too abstract are likely to hide subtle vulnerabilities or prevent the identification of vulnerabilities buried deeply in a chain of dependencies or third-party software libraries.

An effective middle ground collects an evolving level of detail up to the necessary level to capture all interactions that cross “trust boundaries” between the separate, unique zones of a system (Figure 2).

For example, an IoT application can comprise multiple zones linked with cloud resources, gateways, IoT terminal devices, and users. Transactions that operate across trust boundaries are particularly vulnerable to an exceptional array of attacks on transferred data, security credentials, or protocols. Even seemingly innocuous attempts to

[ C O N T ’ D O N N E X T P A G E ]

Figure 1: Arm’s water-meter threat model limits its TOE to the system’s core rather than including the full com-plement of subsystems typically included in these sys-tems, resulting in a more manageable scope of analysis. (Source: Arm)

Figure 2: Threat models should provide sufficient details to identify possible transactions that cross trust boundaries between different zones of a system. (Source: Microsoft)

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communicate across a trust boundary can create a pathway for a “fingerprinting” attack—where hackers use known indicators contained in the system’s response to determine the system’s underlying components in preparation for more directed attacks.

Of course, an understanding of the interactions between the underlying components within each zone becomes especially important if some of those components come from third parties. For example, an IoT device that uses a third-party sensor driver could be vulnerable to threats at the driver’s boundary (Figure 3).

Though a suitably detailed description is essential for threat modeling, the identification of specific threats that connect to those details is the payoff. In the case of Arm’s water-meter threat model, the modelers provide a plain-language list of threats associated with each asset, such as firmware, measurement data, and interactions with external entities (like users, administrators, and attackers), that might touch the TOE.

For firmware, the model describes specific threats including the installation of compromised firmware, modifications of associated security certificates utilized to authenticate firmware updates, cloning, and more. Based on the list of assets and identified vulnerabilities, development teams can evolve a set of corresponding security objectives and mitigation methods. For example, Arm’s water-meter model concludes with a list of security

requirements, including those for firmware, such as the need for a secure boot, firmware authentication, a response to a failed authentication, and others.

Available ResourcesIn identifying potential threats, few (if any) development organizations can possibly remain current on every possible threat that might apply to the detailed assets and processes included in their TOE descriptions. The good news is that engineers can find several published sources that can help with this part of the process. Developers can use public resources such as the Common Attack Pattern Enumeration and Classification (CAPEC) list to review, from the top down, the most likely types of attacks. Then, they can work, from the bottom up, to identify the likely targets of attack listed in the Common Weakness Enumeration (CWE) list, which describes inherent flaws in system design approaches, such as the use of hardcoded credentials. As designers identify specific hardware or software components utilized in their designs, they can turn to the Common Vulnerabilities and Exposures (CVE) list, which lists specific software flaws or potential exploits in available hardware or software components.

For risk assessments, resources such as the Common Vulnerability Scoring System (CVSS) provide a consistent approach for rating the risks associated with specific vulnerabilities. Although a risk relates to the nature of a specific vulnerability, it also includes other factors such as the avenue (vector) used to perform the attack, the complexity of the attack required to exploit the vulnerability, and others. For example, an attack that can be performed through a network brings considerably more risk than one that requires physical access. Similarly, an attack that is simple to perform carries significantly more risk than an attack that is highly complex in nature. Using a CVSS calculator, engineers can quickly account for these various contributing factors, arriving at a numeric score for the risk level associated with a particular threat or class of threats. For Arm’s water meter, the CVSS calculator finds that the combination of factors involved in a firmware attack represents a critical risk score of 9.0 (Figure 4).

Because of the broad range of requirements and techniques, automated tools such as Open Web Application Security Project’s (OWASP’s) Threat Dragon Project, Mozilla’s SeaSponge, and Microsoft’s Threat Modeling Tool exist to help developers work through the modeling process. Each uses a different threat modeling

methodology, ranging from system diagramming in the Threat Dragon Project and SeaSponge to Microsoft’s detailed STRIDE (translated as “Spoofing,” “Tampering,” “Repudiation,” “Information disclosure,” “Denial of service,” and “Elevation of privilege”) approach. Though these tools are several years old and generally built for enterprise software systems, threat modeling is a broadly applicable, evergreen process that depends more on the current lists of attack vectors, weaknesses, and vulnerabilities than on specific methodologies. Nevertheless, newer tools are now emerging that promise a tighter link between a system description and threat identification. Despite the rapid emergence of deep learning technologies in other areas, however, significant challenges remain in applying these technologies to automated threat and risk assessments. Even so, the availability of smart modeling and assessment tools is likely soon to come.

In the meantime, developers can find a variety of collections that list security weaknesses, vulnerabilities, and attack patterns—so much so that all the available detail can seem overwhelming, particularly to those just starting to engage in threat modeling. In fact, one of the excuses commonly used to avoid threat modeling is that it is simply too complicated. Rather than jumping into the full depth of details, engineers can start with a more modest approach that focuses just on the most common threats. As of the time of this writing, OWASP is still reviewing its top ten IoT security threats for 2018, but OWASP’s earlier top ten IoT list still provides a useful starting point. In fact, developers need to go no further than their preferred news sites to find a ready catalog of top vulnerabilities and exploits.

For organizations able to move quickly past the basics, however, these same methods can prove invaluable in addressing equally critical aspects of IoT design. For example, systems used in machine control loops typically face associated mission-critical requirements for functional safety. In these systems, security and functional

safety are so intertwined that suitable threat models for these systems will likely need to include scenarios where weakness in security or safety can equally lead to physical risks. In the same way, security and privacy overlap in many respects, yet weaknesses in either area can lead to the same result of a disclosure of personally identifiable information.

ConclusionThe effective application of threat modeling and risk assessments in complex systems goes well beyond any simple list of available options and techniques. Like each specific system, each development organization deals with its own unique constraints and capabilities. The requirements for one system or organization might completely miss the mark for another. What might be the only common requirement is the need to perform threat and risk assessments in the first place. Even so, should an enterprise attempt to create a “complete” threat model and risk assessment? The short answer is no. In fact, an attempt to do so would fall short of this perfect objective.

It is not possible to perfectly predict outcomes. Naturally chaotic processes in the world and the ebb and flow between system mitigations and hacker exploits ultimately derail any attempts toward perfection. At the same time, without building the kind of security roadmap that a threat model and risk assessment provide, it is equally impossible to avoid at least some of the pitfalls and detours that lead to inevitable security breaches.

Figure 4: Using the CVSS calculator, development teams can assign specific risk levels that correlate with different vulnerabilities for TOE assets, such as the firmware in Arm’s water-meter threat model. (Source: FIRST.org)

Figure 3: Although this data-flow diagram was designed to illustrate transactions that cross the boundaries of desktop software drivers, the same principles apply to transactions involving third-party hardware or software components in any connected system, including IoT devices. (Source: Microsoft)

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Making ConnectionsThe IoT is dependent on connectivity. Case in point: Infrastructure solutions are built on connecting, managing, and controlling previously unconnected, remote devices. Frequently, IoT gateways serve as a critical link of a device-enabled system. IoT gateways sit at the intersection of edge systems—sensors, devices, controllers and the cloud. Connectivity options commonly used to deliver robust infrastructure IoT solutions range from traditional fixed-line services and cellular M2M to modern LP-WAN networks.

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CONNECTED INFRASTRUCTURE:A LOOK AT ONE CITY’S TRANSFORMATION By Mouser Electronics Staff

Cities across the globe are becoming IoT enabled by implementing the latest technologies and products in IoT security, connectivity, processing, power, and sensor technologies. In this video, Grant Imahara visits Porto, Portugal to meet with Veniam, a local company that is transforming the city into a Wi-Fi mesh network comprised of mobile hot spots.

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zQSFP+ Stacked Belly-To-Belly Cagesmouser.com/te-zqsfp-plus-btb-cages/

MAX14828 IO-Link Device Transceivermouser.com/maxim-max14828-transceivers/

XBee® Cellular 3G Embedded Modemmouser.com/digi-xbee-cellular-3g/

Watch online at mou.sr/allthingsiot3video

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