Interoperability as innovation engine for Smart Cities in Latin America
Smart Cities are gaining traction in Latin America, where governments are investing to improve the use of resources and quality of life in the region’s burgeoning urban centers. Latin America counts several densely populated megacities of ten million-plus inhabitants, and city managers struggle to steer urban growth, keep pace with citizen’s needs, and mitigate the environmental impact.
The Latin America cities deploying the greatest numbers of Smart Cities applications include Santiago in Chile, Buenos Aires in Argentina, São Paulo and Rio de Janeiro in Brazil, Bogotá and Medellín in Colombia, Mexico City in Mexico, which are piloting and scaling up solutions to enhance urban mobility, public security, energy distribution and utility management, as well as healthcare and housing services.
But smart technologies are not a sole prerogative of megacities. Even medium-sized communities are taking advantage of Internet of Things and digitalization to become more efficient while providing better services to residents and visitors. In Las Condes, Chile, the major and the city council are committed to constantly improve public governance and services for the benefit of the entire community, always taking care of citizens’ wellbeing and satisfaction.
Las Condes began its Smart City journey in 2018 by replacing most of the streetlights with energy-efficient LED lamps and implementing a wireless IoT network to manage and control public applications. Based on our Smart Urban Network, the cybersecure 6LoWPAN infrastructure currently enables key applications such as streetlighting, parking management, traffic surveillance, environmental monitoring, and more.
Interoperability springs up among the critical success factors of Smart City projects such as Las Condes. What is it about? Interoperability ensures that technology products and services – even from different vendors – can interact, exchange information, and work together seamlessly. Interoperability has many benefits for cities, including product commoditization with lower acquisition and management costs. But it is mainly a powerful innovation engine, since it drives future-proof projects where new applications can be added over time without concerns, minimizing obsolescence risks and integration costs.
We will discuss the benefits of interoperability and standard-based technologies for Latin America cities at Smart City Expo Bogotà, the trade fair taking place at Corferias Bogotá, Colombia from May 31 to June 2, 2023: join MinebeaMitsumi and our partner CELSA to learn more about our technologies and how they support smart projects for streetlighting, parking management, solid waste collection, environmental monitoring, and many more.
And don’t miss the session “El uso de estándares abiertos en los sistemas tecnológicos como motor de innovación y desarrollo económico de las ciudades” on June 1 - Solution Talk Plenaria 3 – to deep dive interoperability requirements and advantages with our regional sales manager Nelson Cubillos.
Contact us today to have a complimentary congress pass!
Interoperability rules at the new IoT Lab in Madrid
Today marks the official inauguration of the Internet of Things Laboratory of the city of Madrid (IoTMADLab), the joint initiative between the Madrid City Council and CEDINT-UPM, the R&D center of the Technical University of Madrid. The Lab is intended as a neutral space where public and private organizations can work together on innovative sensors and devices, leveraging an open and interoperable IoT network model to test and explore the connectivity of different objects in different areas of municipal activity through a common, standardized protocol.
IoT sensorization is anything but new in Madrid, since it is part of the Madrid, Digital Capital strategy to reinforce the position of the city as a benchmark in the digital context. One of the objectives of this strategy is to inject intelligence into decision-making processes by applying technologies for hyperconnectivity and hyperautomation, including the IoT.
Madrid is already taking advantage of smart technologies to control streetlighting and other public services, but in many cases the IoT devices, although they work in a similar way, are connected by isolated systems and cannot interoperate with each other. IoTMADLab was established with the main objective of achieving standardization so that services and devices of any type can interact with each other, share information in real time and act based on common data and variables.
At Paradox Engineering, we advocate open standards and, together with our parent company MinebeaMitsumi, we are committed to creating multi-supplier, interoperable solutions for cities and utilities. That’s why we are particularly proud to support IoTMADLab and provide our know-how and solutions to pilot new applications for smart lighting, parking management, municipal solid waste collection, environmental monitoring, and more.
“Today we open a new IoT Laboratory whose focus is the acceleration of the smart journey of Madrid. I am pleased that Paradox Engineering/MinebeaMitsumi have joined Madrid City Council, the Technical University of Madrid, and the other partners of this program, and I am confident that their expertise in open and interoperable data models will greatly benefit our project,” said Fernando Alvarez Garcia, Subdirector de Transformación Digital. D.G. Oficina Digital en Ayuntamiento de Madrid.
“Interoperability and openness allow cities to head for sustainable growth, creating services to solve today’s urban needs and pave the way for future developments. We welcome the opportunity to collaborate with IoTMADLab partners in this inspiring environment and contribute to the deployment of new IoT solutions for Madrid and other cities”, added Ferdinando Sabatino, Sales Manager South Europe IoT and Smart City Solutions at NMB Italia, MinebeaMitsumi Group.
From Smart to Open Cities: be welcomed at Smart City Expo World Congress 2022
Smart Cities changed a lot in the last decade. Back in 2011, when we introduced our first solution for the remote monitoring and control of urban services, we were mainly challenged on process automation, energy efficiency, and cost saving.
Sustainability continues to be a mandatory goal for City leaders, but nowadays there is a pressing call to move beyond and build carbon neutral communities. It’s the time of Open Cities, shared innovation, and inclusion. And it’s even the time of cybersecurity, as public infrastructures and services are increasingly threatened by hackers and cybercriminals.
We are delighted to invite you at the Smart City Expo World Congress, the leading international Smart City exhibition taking place in Barcelona (Spain) from 15 to 17 November 2022. The event motto – Cities Inspired by People – remind us that cities must do their part to make the world a better place, but they are transformed through people, their creative power, and meaningful connections.
If you are visiting Smart City Expo World Congress, be welcomed at our booth – D81 in Hall 2 – to learn more about our flagship platform PE Smart Urban Network and how we can enable the smart journey of your city. Our experts will be pleased to introduce our solutions and services, show some demo and real-life experiences, and answer all your questions.
Don’t miss the events we will be hosting during the three days of the Congress: mark them in your agenda!
Tuesday, 15 November 2022 at 11:00am
“From Smart to Open Cities: IoT Enabling Urban Communities – the Las Condes case“ (Congress Area - Blue Room)
Join Julia Arneri Borghese, Deputy CEO at Paradox Engineering, and Daniela Peñaloza Ramos, Mayor of Las Condes (Chile), to learn how the Internet of Things enabled the smart journey of this municipality.
Wednesday, 16 November 2022 at 12:30am
“The Security Challenge: Can Your City Be Smart and Cybersecure?” (Paradox Engineering/ MinebeaMitsumi booth - Hall2, D81)
Open talk with Gianni Minetti, CEO at Paradox Engineering.
Wednesday, 16 November 2022 at 1:30pm
“Bettering Urban Design through City Analytics” (Congress Area - Red Room)
Join Julia Arneri Borghese, Deputy CEO at Paradox Engineering, to discuss key benefits and features of PE Smart Urban Network.
Wednesday, 16 November 2022 at 16:30pm
“Why Your City Should Head for Interoperability” (Paradox Engineering/MinebeaMitsumi booth – Hall2, D81)
Meet the uCIFI Alliance to learn more about interoperable technologies, open standards, and data models to create multi-supplier solutions for smarter cities and utilities.
Any question? Feel free to contact us anytime.
Infrastructure investments to accelerate Smart Cities
Many countries around the world are struggling to make their cities smarter by leveraging data, advanced technologies, and more efficient resource management systems. Smart journeys vary a lot, as different approaches are being experimented.
For instance, in Japan many smart communities are built from scratch: think of ‘sustainable smart towns’ by Panasonic in Fujisawa and Suita, or Woven City by Toyota in Susono by Mt. Fuji.
But creating smart neighborhoods from scratch is not always possible. In most cases, heading for smartness means evolving existing districts, one by one, one application at a time, starting from the existing physical and digital infrastructure.
That’s the idea behind the Infrastructure Investment and Jobs Act (IIJA) in the United States. As President Biden explained, this act “will rebuild America’s roads, bridges and rails, expand access to clean drinking water, ensure every American has access to high-speed internet, tackle the climate crisis, advance environmental justice, and invest in communities that have too often been left behind”. The final IIJA version welcomes approximately $1.2 trillion in spending, with huge funding opportunities for cities willing to modernize public services and create future-proof smart platforms for sustainable and inclusive urban growth.
How will the IIJA contribute to the development of Smart Cities in the US? This will be widely discussed at Smart City Expo USA, the leading event for cities taking place in Miami (Florida) on September 14 and 15, 2022.
Let’s meet and share thoughts at booth #204: our Smart City experts will explain how PE Smart Urban Network enables the digital transformation of key public services such as street lighting, mobility and parking management, solid waste collection, environmental monitoring, and more.
See you in Miami!
Connected streetlights? It’s time for smart adaptive lighting
Street lighting accounts on average for 40% of a city’s electricity bill: not surprisingly, it is one of the first services city managers focus on when challenged with budget constraints or sustainability targets.
Up to 80% in power consumption and related costs can be saved by turning streetlights to energy-efficient LED lamps and connecting them to a wireless Internet of Things (IoT) network. With our PE Smart Urban Network platform, cities can transform their lighting infrastructure into a smart, sentient network and enable full remote control of single or grouped luminaires.
PE Smart Urban Network allows to turn on/off and dim single or grouped luminaires from the central management system, and enables the definition of customized outdoor lighting schedules. Operating hours and brightness can be programmed upon daily solar times or ambient light levels, and default combinations can be set for given districts or areas.
What's more? Our platform enables adaptive, sensor-based lighting. By interfacing streetlights with motion sensors or vehicle detection systems, dynamic lighting can be triggered, further reducing consumed power up to 30%. Adaptive lighting patters can be defined, ie. turning lamps on in real time upon vehicle or pedestrian transit, reducing brightness in low-traffic areas or empty roads.
Look at this example: Along a bicycle path, street luminaires can be preset to remain off with daylight and provide light intensity at 40% at night. Thanks to the integrated motion sensor, when the environment light is below the 50 lux threshold and a vehicle is detected, the light level is increased from 40% to 100% for 2 minutes.
Lighting can dynamically mirror traffic intensity. Light points can be integrated with vehicle traffic counters to track the number of cars passing through in a given timeframe. When a specific threshold is overpassed, an automatic command is sent to set a group of lamps on a pre-defined dimming level.
For example, an IP camera can be configured to count vehicles crossing a couple of lines, resetting counters every 15 minutes and sending the related command to dim lights. Three scenarios are considered: with low traffic condition, dimming level is set to a minimum of 40%; medium traffic raises dimming to 50%, and high traffic to 70%.
The dimming control can be also based on Lux, rain and environmental sensors measuring wind intensity, temperature, humidity and pressure. Supposing the physical data to be collected every 5 seconds and correlated with related thresholds, a command is sent to LED drivers over the DALI2 bus to adjust lighting levels.
Want to learn more about PE Smart Urban Network and adaptive lighting? Watch our webinar and feel free to contact our Smart Lighting experts to have all your questions answered!
Bringing smart lighting to decentralised cities and rural areas
In 2020, over 56 percent of the world’s population was urban, and the United Nations estimated that urbanisation could reach 68 percent by 2050. However, the Covid-19 pandemic may curb this trend, as the rise of remote working may encourage more people to leave cities in search of a different way of life.
The future may be about decentralised cities, making the traditional metropolis model evolve towards polycentric, multi-nodal conglomerates. This would create a “new normal” for urban density — and push urban IoT infrastructures to change accordingly.
What does this mean for Smart Lighting? The standard case for street lighting is about city centres or densely clustered areas, where it is generally simple and cost-effective to upgrade existing lamps to LED and design a mesh IoT network. Once connected, smart luminaires can be monitored and managed from a centralised software system, while some gateways act as border routers, network coordinators, and data concentrators. Under normal operating conditions, a single gateway can manage up to 400 connected streetlights.
If considering decentralised cities or rural areas, the scenario may be completely different. Think of suburbs and countryside villages in Europe or the US, for instance. Due to the low population density, we may have dispersed groups of a few streetlights, or even single isolated lamps. This makes it difficult and expensive to reach them, as more gateways would be needed to reliably connect them to the mesh network.
Installing more gateways to connect hard to reach streetlights increases complexity and generates additional costs, as average costs per light point soar. What if we had a different lighting device serving both as a node and a gateway? May it connect single or isolated group of lamps to the existing IoT infrastructure?
Paradox Engineering’s new smart hybrid node is expected to hit the market during 2022. Read more on Cities Today!
Author: Paolo Veronesi, software engineer at Paradox Engineering
In an increasingly complex and interconnected world, Machine Learning is proving to be highly beneficial for Smart Cities and any IoT application where large and high-dimensional volumes of data need to be processed, correlated, and actioned.
At Paradox Engineering we are piloting neural networks in specific use cases such as municipal solid waste collection or patient monitoring in hospital and clinics, both requiring medium to high computational powers. Neural networks are generally applied when it is possible to exploit large computing resources (like workstations or servers), but we are now exploring how to use these technologies in a diametrically opposed occurrence – thus with extremely limited resources (such as few hundreds KB of ram, and standard 32-bit processors with less than a hundred MHz).
Leveraging neural networks on embedded IoT devices is a new, emerging application. Our goal is to process data locally, on the embedded device, without sending information over the network: this allows the devices to work properly even in case of network failures, improves the scalability of the system and its overall security, as sensitive data are processed at local level.
The use case we are focusing is Smart Lighting. In a typical urban outdoor installation, streetlights are connected to a mesh network and managed by gateways, operating as border routers, network coordinators, and data concentrators. A central management software enables the remote management and control of the network, as well as of single or grouped devices.
Streetlights are expected to run smoothly even when the connectivity to the nearest gateway or the CMS is not available. To allow this, devices are configured to execute their routines based on current date and time, assuming the gateway provides this information. This is vital for streetlights to derive the ephemeris and calculate daily sunrise and sunset – according to this data, lamps can switch on/off and dim according to programmed schedules.
What happens if a node is isolated and cannot connect to a gateway, for example because of adverse environmental conditions or network topology? Or if the gateway is not installed at all? In such occurrences, the device should find a new way to calculate the current time. We decided to leverage data analysis and Machine Learning capabilities to let streetlights derive exact daytime from data collected from integrated environmental sensors.
Estimating time from parameters such brightness, temperature, or pressure, isn’t a simple task. Environmental conditions vary a lot from day to day, from season to season, with a level of complexity that it would not be possible to solve with a traditional programming approach.
Neural networks can efficiently manage time series and, if trained with an adequate quantity and variety of data, can provide an accurate answer to our question. In our experiment, we trained the system using data generated by a set of environmental sensors over the course of one year.
Results were really promising: isolated streetlights were able to process data locally and estimate the right daytime with an average accuracy of about 16 minutes – and perfectly execute their schedules. Without Machine Learning, this operational continuity would not have been possible.
Learn more about our Smart Lighting application: download our latest white paper (free registration required), and contact our Machine Learning experts!
8 things you wanted to know about Smart Lighting
Smart Lighting is one of the most mature smart urban applications - and its benefits have been widely discussed. We all know LED-based and remotely managed streetlights consume less power, contribute to climate change mitigation, and generally make cities safer, liveable and more attractive places.
However, it is sometimes difficult for city and utility managers to separate the hype from the reality and make a meaningful business case. In a newly published report - Smart Lighting: how switched on are you? (free download upon registration) - we answered 8 key questions that urban leaders might have so they can embark on their smart journey from a more informed position.
Let us offer a sneak peek...
How much money can cities really save with Smart Lighting?
Streetlighting accounts, on average, for 40 per cent of a city’s electricity bill. Savings due to smart technologies depend on a number of variables, but usually they are worth the case.
The municipality of Gijon in Spain deployed a public and interoperable IoT infrastructure in 2016. Streetlighting was the first urban application to be run on it, with an initial installation of 1,150 LED luminaires being monitored and actioned by our software management platform. The economic saving for city coffers was assessed at around €100,000 a year.
Another interesting example is the Tesserete‐Canobbio bike trail in Ticino, Switzerland, where local utility AEM upgraded existing luminaires to LED technologies, connected them to a wireless network and interfaced every lamp with a motion sensor, so lights automatically switch on and are dimmed to 100 per cent only when cyclists or pedestrians pass by. As a result, annual operating hours along the trail reduced from 4,300 to 600, and average costs decreased from 11.19 to 1.56 Swiss francs per light point.
Does Smart Lighting make the management, maintenance and repair of luminaires more complex and expensive?
No, that is exactly the opposite! By having all light points connected to same network, cities can fully monitor and operate luminaires remotely, leveraging a single software platform. Proactive to real-time detection of luminaire failures or out-of-the ordinary behaviours becomes possible, and technicians can be sent out only when and where needed, arriving on site already informed of and equipped to address the specific issue. As well as reduced costs and more streamlined operations, this also improves the quality of service and citizens satisfaction.
Can Smart Lighting generate revenues?
Smart lighting investments can pay for themselves and even turn into a revenue generation opportunity. In 2016, the city council of San Leandro in California, USA, started its smart journey by replacing around 5,000 streetlights with LED lamps and implementing a wireless IoT network with a centralised management and control system. Other applications and services were added over time to the network, such as integrated parking, public wireless internet and traffic video surveillance. Upon the initial spending of $5.2 million for energy- and water-saving equipment, it was calculated that the investment would generate $8 million savings over 15 years through reductions in energy and water use, and $1.5 million in positive cash flow over that time.
This happens when urban networks are designed as interoperable infrastructures, able to host multiple applications and launch public-private collaborations and more, they can create viable opportunities to monetise the data they generate.
Eager to read more? Download our paper and don't miss our free webinar on Thursday, July 1st 2021 at 9am (EST), 2pm (BST), 3pm (CET), 5pm (GST): we will discuss benefits and success factors of best-in-class Smart Lighting, providing an overview of some real-life urban experiences. Register today!
Intelligent surveillance to improve senior and patient care
Author: Riccardo Biella, software engineer at Paradox Engineering
Health systems face immense challenges in many countries around the world. Lots deal with an ageing population and need to control public spending for healthcare service, but in many cases patients outnumber available resources including basics such as hospital beds, doctors, and nurses. This shortage became far more relevant during Covid-19 emergency, with most countries struggling to provide adequate pandemic response and quality of care.
Smart technologies are enabling a whole new class of services to improve patient assistance, specifically the treatment of elderly and long-term patients, whether in hospital, in assisted living facilities, or at home. Connected healthcare systems and remote patient monitoring (RPM) technologies are being increasingly used to ensure constant surveillance, support professional caregivers in their daily duties, and relief family members.
Together with Minebea Intec and MinebeaMitsumi Sensing Division, at Paradox Engineering we are working on an innovative solution for the managed care industry: our goal is to finalize a secure and open technology platform for non-invasive patient monitoring in hospitals, clinics, nursing, care houses and home environments.
It is meant as an adaptive and knowledge building solution, able to generate patient-specific intelligence by collecting data from multiple sensors, correlating multiple parameters, and integrating a unique Artificial Intelligence (AI) engine for situation recognition, behavior analysis, long-term health evolution monitoring and personal relationships.
The solution provides local AI to prevent possible data leakages from the hospital environment and better protect patient privacy. We are currently piloting Machine Learning to improve some specific features, such as the detection of adverse events: by processing data coming from connected smart cameras and sensors, the system can recognize when something goes wrong and immediately alert caregivers in case of falls, lack of movements or other types of anomalies, ensuring constant monitoring of patients around the clock.
Another feature we are testing relates to room access control. We are using Machine Learning to elaborate images from connected smart cameras in order to recognize the number of people in a room and distinguish patients from hospital staff. This is a highly valuable feature to increase guest safety by detecting unauthorized access.
We started from an initial dataset made of hundreds of images of patients and doctors to train the system. PE Smart Cameras are now able to perform object detection tasks in real time, distinguishing people within the room and classifying them as patients or hospital staff, thanks to the use of a Deep Neural Network that can also recognize things such as scrubs, uniforms, nightgowns, medical instruments and postures.
Watch this short video to have a sneak peek:
Want to learn more? Contact our Machine Learning experts!
Smart Waste, Machine Learning enables bin filling level prediction
Author: Riccardo Biella, software engineer at Paradox Engineering
Annual waste production will increase by 70% to 3.4 billion tons by 2050, says the World Bank. At this growth rate, we will need about 3 worlds to absorb and dispose of our trash without compromising our health and the environment.
Cities are increasingly looking for innovative solutions to improve municipal solid waste collection and treatment to preserve both public health and urban livability. At Paradox Engineering, we have extended our flagship platform PE Smart Urban Network to include a ready-to-use Smart Waste application for City and utility managers.
The news is, we are integrating Artificial Intelligence and Machine Learning algorithms to enhance this solution and make it even more performing. While traditional algorithms convert input to output using some given rules, we chose to have Machine Learning to build an accurate predictive model based on sample data.
First step is to equip bins with Paradox Engineering-MinebeaMitsumi 6LoWPAN sensors and allow municipal waste operators to collect and monitor data about the level of filling, date and time of latest waste collection, and bin location information.
Thanks to Machine Learning techniques, we are evolving the system from a raw data collection platform to an actionable prediction solution: the bin level prediction system can receive data from smart bins’ filling sensors and process it, providing an estimate of the date when the bin will reach its capacity limit.
Did you know that bins are generally emptied when they are only 40% full? With our bin filling level prediction system, operators can dispatch trucks only when and where the bins are close to full, thus optimising solid waste collection, reducing the number of truck rolls and the mileage associated with them, which in turn decreases pollution and congestion.
The bin filling level prediction system leverages a deep neural network that is trained on a dataset containing historical data of smart bins. Features considered during software training are based on the current level of filling correlated with time data, as well as geographic location data.
More complex logic will soon be integrated to teach the bin identify relevant points of interest nearby, such as supermarkets, stadiums, stations, or hospitals – and predict waste input upon variables such as people density, calendar, festivities, holiday seasons, and more.
Continual Learning techniques allow the model to continuously evolve combining new data and accumulated knowledge. This means that, by analysing filling patterns, operators can also take data-driven decisions about the quantity, capacity, and location of containers. Bins can be positioned to fit different urban scenarios and adapt within a few days to changing conditions, ie. different residents’ habits or behaviours, like the increase of packaging-related trash due to the rise of e-commerce and home delivery in pandemic time.
Thanks to PE Smart Urban Network and Machine Learning algorithms, cities can truly improve waste collection and management, enhancing livability while protecting public health and the environment.
Learn more about our Smart Waste application: download our white paper (free registration required), and contact our Machine Learning experts