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From Data to Decisions: Edge AI Empowering IoT Innovations and Smart Sensors

Cons

Throughout this blog series on Edge AI, we have touched upon various fascinating applications, including Edge AI in autonomous vehicles and Edge AI in consumer electronics. In autonomous vehicles, edge AI plays a pivotal role in enabling real-time decision-making and improving the overall safety and efficiency of transportation systems. Meanwhile, in consumer electronics, edge AI enhances user experiences by providing smart, responsive features in everyday devices such as smartphones, smart home systems, and wearable technology.

Lastly, in the rapidly evolving landscape of technology, Edge AI is paving new ways to harness the power of IoT (Internet of Things) devices and smart sensors. These advancements are not just buzzwords but fundamental shifts that promise to enhance efficiency, improve data management, and offer unprecedented insights. This blog will explore the effects of Edge AI on IoT devices and smart sensors, providing insights into its current applications, benefits, and future potential. By the end, you’ll have a comprehensive understanding of how Edge AI can revolutionize your business operations.

Smart Sensors Explained

This RealPars video explores the transformative role of Smart Sensors in Industry 4.0’s Smart Factory framework

It traces the evolution from the First Industrial Revolution to today’s IoT-driven Smart Factories, highlighting how Smart Sensors surpass traditional sensors with advanced features like data conversion, digital processing, and cloud communication. Discover how these intelligent devices are revolutionizing manufacturing, enhancing efficiency, and driving innovation.

The Intersection of Edge AI and IoT

Real time

Enhancing Real-Time Data Processing

One of the most significant benefits of Edge AI is its ability to process data in real-time. Traditional IoT systems often rely on cloud-based servers to analyze data, which can result in delays and increased latency. Edge AI mitigates these issues by enabling IoT devices to process and analyze data locally. This real-time processing capability is crucial for applications requiring immediate responses, such as autonomous vehicles or industrial automation.

For example, consider a manufacturing plant equipped with smart sensors to monitor machinery performance. With Edge AI, any anomalies in the data can be detected and addressed instantly, preventing potential breakdowns and costly downtime.

Improving Bandwidth Efficiency

Bandwidth efficiency is another critical advantage of Edge AI on IoT devices. Sending vast amounts of raw data to the cloud for processing can strain network resources and incur significant costs. By processing data locally, Edge AI reduces the amount of data that needs to be transmitted, thus optimizing bandwidth usage.

Imagine a smart city project where thousands of sensors collect data on traffic, weather, and public safety. Edge AI can filter and preprocess this data locally, sending only the most relevant information to the central server. This approach not only conserves bandwidth but also ensures faster and more efficient decision-making.

Enhancing Security and Privacy

Security

Security and privacy are paramount concerns in the age of data-driven technologies. Edge AI offers enhanced security by minimizing the need to transfer sensitive data over the network. Localized data processing reduces the risk of data breaches and unauthorized access, making it a more secure option for businesses dealing with sensitive information.

For instance, healthcare facilities using IoT devices to monitor patient vitals can benefit from Edge AI. By processing data locally, patient information remains within the facility’s secure network, reducing the risk of data breaches and ensuring compliance with privacy regulations.

Take, for example, a hospital equipped with smart beds that monitor patient heart rates, blood pressure, and oxygen levels. With Edge AI, these smart beds can analyze data in real-time and alert medical staff to any abnormalities immediately, thereby enhancing patient care and response times.

Another example is remote patient monitoring systems used in home healthcare setups. Edge AI can process data from wearable devices, such as glucose monitors or digital stethoscopes, ensuring that sensitive health information is analyzed on the device itself before only the necessary summarized data is sent to healthcare providers. This not only preserves the patient’s privacy but also ensures timely intervention when needed.

Pros of Edge AI on IoT Devices and Smart Sensors

Operational Costs

Reduced Latency

One of the most significant advantages of Edge AI is its ability to reduce latency. By processing data closer to the source, Edge AI eliminates the delays associated with transmitting data to and from cloud servers. This reduced latency is crucial for applications requiring real-time decision-making, such as autonomous vehicles or industrial automation.
Consider a smart factory where machines are equipped with sensors to monitor their performance. With Edge AI, any anomalies in the data can be detected and addressed instantly, preventing potential breakdowns and costly downtime.

In an automated warehouse where robotic systems manage inventory, Edge AI can be used to process data from various sensors in real time. If a sensor detects an obstruction in the robot’s path, Edge AI can immediately reroute the robot, avoiding potential collisions and maintaining a smooth flow of operations. This instant decision-making capability minimizes interruptions and maximizes operational efficiency, showcasing how Edge AI significantly benefits environments that rely on the timely processing of critical data.

Improved Bandwidth Efficiency

Another positive aspect of Edge AI is its ability to enhance bandwidth efficiency. By processing data locally, Edge AI minimizes the volume of data transmitted to central servers. This is particularly advantageous for data-intensive applications, such as video surveillance or smart city monitoring. For instance, in a smart city, Edge AI can process video feeds from traffic cameras locally and only send relevant alerts or summarized data, significantly reducing network load and transmission costs.

Enhanced Resilience and Reliability

Edge AI enhances system resilience and reliability by ensuring critical functions can operate even without network connectivity. For instance, in autonomous vehicles, edge computing allows real-time decision-making even in regions with poor internet connections. Similarly, in industrial automation, machines can perform essential operations independently of cloud-based systems. This decentralized approach ensures that even in the event of network failures, Edge AI devices maintain functionality and consistent performance.

Cons of Edge AI on IoT Devices and Smart Sensors

Cons

Initial Setup Costs

One of the primary challenges of implementing Edge AI is the initial setup cost. Deploying Edge AI infrastructure requires significant investment in hardware, software, and skilled personnel. For small and medium-sized businesses, these costs can be a barrier to adoption.

However, it’s important to consider the long-term benefits and potential cost savings associated with Edge AI. Businesses that invest in Edge AI can achieve significant returns through improved efficiency, reduced operational costs, and enhanced decision-making capabilities.

Limited Processing Power

Another potential drawback of Edge AI is the limited processing power of edge devices. Unlike cloud servers, edge devices may have limited computational resources, which can impact their ability to handle complex AI algorithms.

Businesses must carefully evaluate their specific use cases and determine whether Edge AI devices have the necessary processing power to meet their needs. In some cases, a hybrid approach that combines edge and cloud processing may be the most effective solution.

Data Management Challenges

Data Management

Edge AI also presents data management challenges for businesses. With data being processed and stored on various edge devices, managing and maintaining this data can be complex and time-consuming. This issue is further compounded by the sheer volume of data generated by IoT devices, making it challenging to extract meaningful insights.

To address this challenge, businesses must have robust data management strategies in place, including implementing efficient data storage solutions and leveraging advanced analytics tools to make sense of large datasets. Overall, while there are challenges associated with Edge AI on IoT devices, its numerous benefits make it a valuable tool for businesses looking to utilize real processing and improve decision-making capabilities.

Maintenance and Management

Maintaining and managing Edge AI infrastructure can be challenging, especially for businesses with limited IT resources. Edge devices require regular updates, monitoring, and maintenance to ensure optimal performance and security. Businesses can partner with managed service providers (MSPs) that specialize in Edge AI deployment and management. MSPs can provide the expertise and support needed to maintain a robust and secure Edge AI infrastructure.

Future Plans and Developments

Future

Advancements in Edge AI Hardware

The future of Edge AI is bright, with ongoing advancements in hardware technology. Next-generation edge devices will feature more powerful processors, enhanced memory capabilities, and improved energy efficiency. These advancements will enable businesses to deploy even more sophisticated AI algorithms at the edge.
For example, companies like NVIDIA and Intel are developing cutting-edge processors specifically designed for Edge AI applications. These processors will enable faster and more efficient data processing, opening up new possibilities for IoT and smart sensor applications.

Integration with 5G Networks

5G

The rollout of 5G networks will significantly impact the adoption of Edge AI. With its ultra-low latency and high-speed data transmission capabilities, 5G will enhance the performance of Edge AI applications, enabling real-time decision-making and data processing on a larger scale.

Industries such as autonomous driving, smart cities, and industrial automation will benefit greatly from the combination of 5G and Edge AI. The synergy between these technologies will drive innovation and transform the way businesses operate. Overall, the future of Edge AI looks promising, with endless possibilities for improving efficiency, security, and decision-making capabilities in various industries. As hardware technology continues to advance and more businesses adopt Edge AI solutions, we can expect to see even greater developments and advancements in this field.

Expansion of Edge AI Use Cases

As Edge AI technology continues to evolve, we can expect to see an expansion of use cases across various industries. From healthcare and agriculture to manufacturing and retail, businesses will find new and innovative ways to leverage Edge AI to improve efficiency, enhance customer experiences, and drive growth.
For instance, in agriculture, Edge AI-powered drones can monitor crop health in real time, enabling farmers to make data-driven decisions and optimize their yields. In retail, smart shelves equipped with Edge AI can track inventory levels and automatically reorder products, reducing stock outs and improving customer satisfaction. The possibilities are endless, and the future of Edge AI is full of exciting potential. One example of a company that is in charge of creating Edge AI-powered drones for agriculture is DroneDeploy. DroneDeploy offers innovative solutions that enable farmers to monitor crop health with precision and efficiency.

Conclusion

As we conclude our Edge AI blog series, we hope you have gained valuable insights into the benefits, challenges, and future developments associated with this transformative technology. From understanding its impact on various industries to exploring its innovation potential, Edge AI represents a significant advancement in the way we process and utilize data.

Edge AI is revolutionizing the way businesses leverage IoT devices and smart sensors. By enabling real-time data processing, optimizing bandwidth usage, and enhancing security, Edge AI offers significant benefits for businesses across various industries. However, it’s essential to consider the initial setup costs, limited processing power, and maintenance challenges associated with Edge AI.

Looking ahead, advancements in Edge AI hardware, integration with 5G networks, and the expansion of use cases will drive the continued growth and adoption of this technology. For CEOs, technology executives, and business owners, staying informed about Edge AI developments and exploring its potential applications can provide a competitive advantage in today’s tech-driven world. Stay tuned for more in-depth explorations of the latest trends and technologies shaping our world.

Machine Learning Unlocks Quantum Potential: A Paradigm-Shifting Partnership

Three Dimensional Qubit

In the modern world, technology has revolutionized the way we work, carry out our tasks, and interact with one another. These technological transformations have come into existence due to the application of various scientific discoveries and computing power advancements. In recent years, Machine Learning and Quantum Computing have both evolved to become game-changers, taking their place in the revolutionary field of computer science. This blog will discuss the effects of machine learning on Quantum Computing, and how the models and algorithms derived in machine learning can be applied to enhance the power of quantum computing.

Machine learning has been a hot topic in the world of computer science, with its ability to analyze and make predictions from vast amounts of data. This has led to significant advancements in various fields such as healthcare, finance, and transportation. On the other hand, quantum computing has sparked excitement with its potential to solve complex problems that are impossible for traditional computers.

The Impact of Machine Learning on Quantum Computing

Machine learning and quantum computing are two powerful technologies that have the potential to complement each other. The combination of these two fields can create a cutting-edge technology that can solve some of the most complex problems known to humankind. One of the key areas where machine learning has shown its impact on quantum computing is in the optimization of quantum algorithms.

Quantum computers are known for their ability to process large amounts of data in a fraction of the time it would take traditional computers. However, implementing quantum algorithms can be challenging due to the complexity involved. This is where machine learning comes into play. By using machine learning models and algorithms, scientists and researchers can optimize these quantum algorithms to work more efficiently and accurately. This not only saves time and resources but also improves the overall performance of quantum computers.

Another area where machine learning has shown its potential in enhancing quantum computing is in error correction. As with any technology, errors are inevitable. In quantum computing, these errors can significantly impact the accuracy and reliability of calculations. By utilizing machine learning techniques, researchers have been able to develop algorithms that can detect and correct errors in quantum systems. This has greatly improved the stability and efficiency of quantum computers, making them more viable for practical use.

Difference between a Bit and Qubit

Exactly How is Machine Learning Impacting Quantum Computing?

Quantum computing, on the other hand, is a unique form of computing that employs quantum-mechanical phenomena such as superposition and entanglement to manipulate information. Unlike classical computers, where information is represented in bits (0s and 1s), quantum computers use qubits to represent information. This allows them to handle and process multiple calculations simultaneously, making them incredibly powerful.

The integration of machine learning with quantum computing has opened new avenues for the development of more sophisticated algorithms and models that can solve complex problems. Machine learning techniques such as neural networks and deep learning are being applied to quantum computing, allowing for enhanced data processing and analysis. This has led to a better understanding and utilization of quantum properties, resulting in improved performance and accuracy in solving complex problems. The potential of this partnership is immense, and it has the potential to shape the future of computing.

Neural Network

Challenges and Opportunities

While the partnership between machine learning and quantum computing offers many opportunities, there are also some challenges that need to be addressed. One major challenge is the limited availability of quantum hardware. Quantum computers are still in their early stages of development, and only a few companies and research institutions have access to them. This can hinder the progress of using machine learning techniques in quantum computing.

Additionally, there is a shortage of experts who possess both machine learning and quantum computing knowledge. Both fields require a deep understanding of complex mathematical concepts, making it challenging to find individuals with expertise in both areas. As such, there is a need for more interdisciplinary training and collaboration between these fields to bridge this gap.

Machine Learning and Quantum Computing Effects

Machine learning and quantum computing have significant positive effects when used together. Machine learning can help quantum computing to identify, react, and handle large volumes of data quickly and efficiently. Both technologies rely on deep mathematical connections, and when combined, they can improve the precision and accuracy of quantum computations. This will enable quantum computers to solve complex problems much quicker than before. Additionally, machine learning can help in reducing the sensitivity of quantum computers to errors and noise, which are common in these systems. This will lead to improved stability and reliability of quantum computers, making them more practical for solving real-world problems.

Quantum Circuit

Moreover, the integration of machine learning with quantum computing can also aid in the development of new quantum algorithms. These algorithms can be used in various applications such as optimization problems, simulation, and machine learning. The combination of these two technologies has the potential to transform various fields, including finance, drug discovery, and climate modeling.

Some Examples of Companies using Machine Learning for Quantum Computing

Several companies use machine learning and quantum computing to improve their processes and services such as: IBM, Google, Microsoft, Rigetti and Anyon Systems.

IBM: IBM Quantum is at the forefront of research and development in quantum machine learning algorithms. They’ve launched the Qiskit Machine Learning library, enabling users to implement quantum machine learning models on IBM’s quantum computers.

Google: Known for its Quantum AI lab, has been exploring the acceleration of machine learning tasks using quantum processors, particularly in the development of quantum neural networks.

Rigetti: Rigetti has been actively using quantum computers for machine learning applications. They offer the Quantum Machine Learning (QML) toolkit, which implements machine learning algorithms on quantum hardware.

Microsoft: Microsoft has been actively researching quantum machine learning and has integrated quantum computing capabilities into their Azure cloud platform, providing resources for quantum machine learning research.

Anyon Systems: Anyon Systems, a quantum software company, explores the application of quantum computing to machine learning and optimization problems, providing software tools for quantum machine learning research.

It’s worth noting that the field of quantum computing is rapidly evolving, and new companies and developments are emerging continually.

Future Possibilities

Quantum Mechanics and Drug Discovery

The combination of machine learning and quantum computing holds immense potential for the future. As both technologies continue to advance and evolve, their integration will lead to groundbreaking innovations in fields such as drug discovery, finance, materials science, and more. With the ability to process vast amounts of data quickly and efficiently, quantum computers powered by machine learning will revolutionize problem-solving and decision-making processes. This will have a profound impact on various industries, leading to the development of new products and services that were previously unimaginable.

Here are some future possibilities and effects of the synergy between machine learning and quantum computing:

Faster Optimization: Quantum computers excel at solving optimization problems, which are prevalent in machine learning. They can significantly speed up tasks like hyperparameter tuning, portfolio optimization, and feature selection, making machine-learning models more efficient and accurate.

Quantum Machine Learning Models: Quantum machine learning algorithms may become a reality, utilizing the inherent properties of quantum systems to create novel models capable of solving complex problems.

Improved Data Processing: Quantum computing can enhance data preprocessing tasks like dimensionality reduction, clustering, and pattern recognition. Quantum algorithms can efficiently handle large datasets, potentially reducing the need for extensive data cleaning and preparation.

Enhanced AI Training: Quantum computers could expedite the training of deep learning models, which is a computationally intensive task. This could lead to faster model training and the ability to tackle more complex neural network architectures.

Quantum Data Analysis: Quantum computing can facilitate the analysis of quantum data, which is generated by quantum sensors and experiments. Quantum machine learning can help in extracting meaningful insights from this data, leading to advancements in physics, chemistry, and materials science.

Drug Discovery and Material Science: Quantum computing combined with machine learning can accelerate drug discovery and materials research. Quantum simulations can accurately model molecular structures and properties, leading to the development of new drugs and materials.

Quantum-Assisted AI Services: Cloud providers may offer quantum-assisted AI services, allowing businesses and researchers to harness the power of quantum computing for machine learning tasks via the cloud, similar to how cloud-based GPUs are used today.

Improved Security: Quantum machine learning can contribute to enhancing cybersecurity by developing more robust encryption and security protocols. Quantum-resistant encryption algorithms are being explored to safeguard data against quantum attacks.

It’s important to note that the full realization of these possibilities depends on advancements in both quantum hardware and quantum algorithms, as well as the integration of quantum computing into existing machine learning workflows. While quantum computing is a promising technology, it is still in its early stages, and practical applications may take several years to become widespread.

Additional Benefits of Machine Learning on Quantum Computing

With machine learning, quantum computing can quickly recognize patterns and anomalies, which can lead to improvements in supply chain logistics and customer service. Additionally, it has the potential to aid breakthrough research in cancer treatments and other scientific issues that currently require significant amounts of time and effort. Using machine learning with quantum computing could generate the solutions more efficiently. Moreover, as quantum computers continue to scale, the applications and potential benefits will only increase. It’s an exciting time for both fields, and the future possibilities are limitless. Combining these two technologies will pave the way for groundbreaking discoveries and advancements that will shape our society in unimaginable ways.

Qubit

Machine Learning has led to significant improvements in many sectors, and in recent years, Quantum Computing has begun to change how various industries process and analyze data. The effects of machine learning on Quantum Computing can enhance computing efficiency and precision and lead to groundbreaking research. As we continue to explore the possibilities of machine learning and quantum computing, the future is looking increasingly bright for the integration of these two innovative technologies. The application of machine learning to quantum computing has the potential to transform how we conduct research, and it is exciting to think about what changes will come about in the not-too-distant future. The possibilities are endless, and the integration of these two fields is just beginning. We can only imagine the advancements that will be made through this synergy and eagerly await what’s to come. So, it is essential to continue learning about both machine learning and quantum computing, staying updated on new developments, and exploring potential applications in various industries. By doing so, we can fully embrace and harness the power of machine learning and quantum computing, leading to a more advanced and innovative future. So, let’s keep learning and exploring the possibilities together!

In conclusion, machine learning and quantum computing are powerful technologies on their own, but when combined, their potential becomes even greater. As we continue to make advancements in both fields, it is crucial to explore and embrace the possibilities of their integration.

How Zigbee Pro Makes Life Easier for IoT Developers

The IoT has proliferated our everyday lives in a growing variety of ways. In 2021, there were more than 10 billion active IoT devices. That number is expected to grow past 25.4 billion by 2030. IoT solutions will generate $4-11 trillion in economic value by 2025.

Hundreds of manufacturers create IoT devices of all varieties—interoperability is necessity. In order to facilitate this, IoT developers generally adhere to a communications protocol known as Zigbee Pro.

WHAT IS ZIGBEE PRO?

 

Zigbee Pro is a low power, low data rate Wireless Personal Area Network (WPAN) protocol which streamlines device connections. The goal of the protocol is to deliver a single communications standard that simplifies the nauseating array of proprietary APIs and wireless technologies used by IoT manufacturers.

Zigbee Pro is the latest in a line of protocols. The certification process is facilitated by the Zigbee Alliance—now commonly known as the Connectivity Standards Alliance—which formed in 2002. The Connectivity Standards developed the first version of Zigbee in 2004 and gradually rolled out improved versions until the most current version in 2014.

HOW DOES IT WORK?

Zigbee is composed of a number of layers that form a protocol stack. Each layer contributes functionality to the ones below it, making it easier for developers to deploy these functions without explicitly having to write them. The layers include a radio communication layer based on the IEEE standard 802.15.4, a network layer (Zigbee Pro), the application layer known as Dotdot, and the certification layer which is compliant with the Connectivity Standards Alliance.

One of the focuses of the Zigbee standard is to deliver low-power requirements. Battery powered devices must have a 2 year battery life in order to be certified.

ZIGBEE DEVICES

Mesh networking enables Zigbee networks to operate more consistently than WiFi and Bluetooth. Each device on the network becomes a repeater, which ensures that losing one device won’t affect the other devices in the mesh.

There are three classes of Zigbee devices:

Zigbee Coordinator – The coordinator forms the root of the network tree, storing information about the network and functioning as a repository for security keys. This is generally the hub, bridge, or smart home controller—such as the app from which you control your smart home.

Zigbee Router – The router can run application functions as well as act as an intermediate router to pass data on to other devices. The router is generally a typical IoT device, such as a powered lightbulb.

Zigbee End Device – This is the simplest type of device—requiring the least power and memory to perform the most basic functions. It cannot relay data and its simplicity enables it to be asleep the majority of the time. An example of an end device would be a smart switch or a sensor that only sends a notification when a specific event occurs.

The Zigbee Pro protocol has become the gold standard for IoT developers. Many commercial IoT apps and smart home controllers function under the Zigbee Pro protocol. Examples include: Samsung SmartThings Hub, Amazon Echo, and the Philips Hue Bridge.

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

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

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

WHAT IS AIOT?

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

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

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

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

CCTV-Traffic-Monitoring-1024x683

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

WEARABLES

Wearable-IoT-Devices

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

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

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

SMART CITIES

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

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

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

RETAIL

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

smart-shopping-cart

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

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

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

CONCLUSION

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

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

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

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

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

Surveillance

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

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

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

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

Disaster Prevention

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

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

Cybersecurity

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

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

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

Conclusion

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

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

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

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

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

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

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

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

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

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AUTONOMOUS VEHICLES

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

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

Integrated-Roadways

SMART ROADS

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

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

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

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SMART TRAFFIC LIGHTS

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

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

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SMART PUBLIC TRANSIT

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

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

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

CONCLUSION

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

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

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

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

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

Check out the breakdown below:

Breakdown

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

STRUCTURAL HEALTH

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

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

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

ENVIRONMENTAL APPLICATIONS

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

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

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WASTE MANAGEMENT

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

CONCLUSION

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

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

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

Are you ready for smart cities of the future?  Over the next few weeks, we will be endeavoring on a series of blogs exploring what the big players are developing for smart cities and how they will shape our world.

When the world becomes smart, life will begin to look a lot more like THE JETSONS!
When the world becomes smart, life will begin to look a lot more like THE JETSONS!

Our cities will become smart when they are like living organisms: actively gathering data from various sources and processing it to generate intelligence to drive responsive action. IoT, 5G, and AI will all work together to enable the cities of the future. IoT devices with embedded sensors will gather vast amounts of data, transmit it via high-speed 5G networks, and process it in the cloud through AI-driven algorithms designed to come up with preventative action. From smart traffic to smart flooding control, the problems smart cities can potentially solve are endless.

Imagine a world where bridges are monitored by hundreds of tiny sensors that send information about the amount of pressure on different pressure points. The data from those sensors instantly transmits via high-speed internet networks to the cloud where an AI-driven algorithm calculates potential breaking points and dispatches a solution in seconds.

That is where we are headed—and we’re headed there sooner than you think. Two-thirds of cities globally are investing in smart city technology and spending is projected to reach $135 billion by 2021. Here are the three of the top applications leading the charge in the Smart Cities space.

Smart Infrastructure

SMART INFRASTRUCTURE

As our opening description of smart bridges implies, smart infrastructure will soon become a part of our daily lives. In New Zealand, installed sensors monitor water quality and issue real-time warnings to help swimmers know where it’s safe to swim.

In order to enable smart functionality, sensors will need to be embedded throughout the city to gather vital information in different forms. In order to process the abundance of data, high-volume data storage and high-speed communications powered by high-bandwidth technologies like 5G will all need to become the norm before smart infrastructure can receive mass adoption.

Stay tuned for our next blog where we’ll get more in-depth on the future of smart infrastructure.

Smart Cars

SMART TRANSPORTATION

From smart parking meters to smart traffic lights, from autonomous cars to scooters and electric car sharing services, transportation is in the midst of a technological revolution and many advanced applications are just on the cusp of realization.

Smart parking meters will soon make finding a parking space in the city and paying for it easy.  In the UK, local councils can now release parking data in the same format, solving one of the major obstacles facing smart cities: Data Standardization (more on that later).

Autonomous cars, powered by AI, IoT, and 5G, will interact with the smart roads on which they are driving, reducing traffic and accidents dramatically.

While there is a debate about the long-term effectiveness of electric motorized scooters as a mode of transportation, they’ve become very popular in major US cities like San Francisco, Oakland, Los Angeles, Salt Lake City and are soon to come in Brooklyn.

With the New York Subway system in shambles, it seems inevitable the biggest city in the world will receive a state-of-the-art smart technology to drastically improve public transit.

Surveillance State

SMART SECURITY

The more you look at potential applications for smart security, the more it feels like you are looking at the dystopian future of the novel 1984.

Potential applications include AI-enabled crowd monitoring to prevent potential threats. Digital cameras like Go-Pros have shrunk the size of surveillance equipment to smaller than an apple. Drones are available at a consumer level as well. While security cameras can be placed plentifully throughout a city, one major issue is cultivating the manpower required to analyze all of the footage being gathered for potential threats. AI-driven algorithms to analyze footage for threats will enable municipalities to analyze threats and respond accordingly.

However, policy has not caught up with technology. The unique ethical quandaries brought up by smart security and surveillance will play out litigiously and dictate to what degree smart security will become a part of the cities of the future.

CONCLUSION

We can see what the future may look like, but how we’ll get there remains a mystery. Before smart technologies can receive mass adoption, legislation will need to be passed by both local and national governments. In addition, as the UK Parking Meter issue shows, data standardization will be another major obstacle for smart technology manufacturers. When governments on both a local and a national level an get on the same page with regard to how to execute smart city technology and legislation, the possibilities for Smart Cities will be endless.

Stay tuned next week for our deep dive into the future applications of Smart Infrastructure!

How Wearable Smart Clothing Will Revolutionize Health, Fitness, and Fashion

Wearables are in a nascent stage since coming into vogue through the advent of Samsung Galaxy Gear in 2013 and the Apple Watch in 2015. Smartwatches and fitness devices like Fitbit continue to reign supreme and help us make our lives more efficient while tracking vital health data and improving our workouts.

The next generation of wearables will be able to cultivate even more data and transmit that information to health professionals with the help of 5G. Machine learning algorithms will help predict potential health issues based on the data gathered. In order to cultivate this data, we predict that wearable clothes fitted with sensors will rise in popularity, yielding ground-breaking applications in Fitness, Healthcare, Emergency Services, and Fashion.

FITNESS

The release of Fitbit in 2009 marked the first consumer-grade wearable focused on activity tracking, precipitating the advent of the smartwatch by four years. Since then, Fitbit has designed and released a line of products focusing on activity tracking, including the Fitbit Versa, a health and fitness smartwatch, and the Fitbit Ace, an activity tracker for children 8+.

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Fitbit isn’t the only major player in the wearables game. Nadi X Yoga Pants use built-in haptic vibrations to encourage wearers to move and hold positions.

Sensoria’s second generation connected socks use textile pressure sensors to track the pressure put on the user’s foot when running and inform the user when it senses too much pressure on a particular body part to prevent injury. They also track time, cadence, pace, speed, and distance.

The Thin Ice smart vest cools your body using thermo receptors, activating the bodies brown fat pathways which effectively burns white fat (bad fat).

OMsignal’s OmBra measures heart rate and breathing rhythm in addition to time, distance, cadence pace, and impact for runners.

athos

The Athos Core is perhaps the most thorough and expansive application in smart clothes today. Athos Core collects data from a line of clothing embedded with micro-EMG sensors and analyzes it to help improve your workout. Athos shirts can evaluate electrical activity produced by your muscles to track the exertion of major upper-body muscle groups (pecs, bis, tris, delts, lats, and traps).

HEALTHCARE AND EMERGENCY SERVICES

As cited in our last blog on 5G and healthcare, 86% of doctors say wearables increase patient engagement with their own health.

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The second generation Owlet Smart Sock is a smart-sock made for babies which uses pulse oximetry technology to monitor heart rate and sleep patterns.

Siren Smartsocks are designed to prevent diabetics from suffering from foot injuries. They have microsensors designed to continuously monitor temperature for inflammation and alert users through their smartphone app.

As wearable clothes become more popular, applications will automatically contact emergency services when the wearer’s health shows major warning signs.

Invisiwear offers wearable smart jewelry and other accessories with a panic button which gives the option to share your location with loved ones and 9-1-1.

The iBeat Heart Watch monitors health and notifies your loved ones and an EMT team in emergency situations.

FASHION

“Fashion tech” is gradually earning adoption.

On a mass consumer level, Levi’s teamed up with Google’s Project Jacquard to offer a smart trucker jacket designed for urban cyclists. Conducive yarn makes it easy for the user to tap, swipe, or hold the sleeve to fulfill simple tasks like changing music tracks, block or answer calls, or access navigation information.

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A London-based design firm THE UNSEEN created a line of luxury accessories including a backpack, phone case, scarf, and more which respond to air pressure, body temperature, wind, sunlight, and touch to change colors.

THE FUTURE OF WEARABLE CLOTHES

Recently, NBA commissioner Adam Silver unveiled the future of the NBA Jersey: a line of smart jerseys that allow you to customize the name and number on the jersey.

There’s no doubt that the future of wearable clothes is still unveiling itself to us as 5G receives mass adoption and programmers continue to uncover potential applications of machine learning. What is clear at this point is that wearable clothes will help make the human race smarter, stronger, healthier, and more efficient.

Apple Brings the Internet of Things Home with HomeKit & iOS 10

Anyone engrossed in the tech scene knows the Internet of Things is one of the trendiest technology topics on the web. The IoT is shaping our world and building fortunes for innovators, futurists and top app development companies. However, in the common household, the IoT has yet to break through to the mainstream. The biggest company in the world is now looking to enact change.

Tim Cook, in his September Announcement, declared that iOS 10’s HomeKit update is the first time home automation has been integrated with a major platform. While Apple introduced HomeKit in 2014 with iOS 8, iOS 10 comes with a dedicated app called Home that controls all home automation devices.

HOME IS A HOME RUN

Home Apple App via Wareable

Home combines IoT technology with the masterful UI of iOS. Previous iterations of iOS and HomeKit required the user to manage each interface separately. So if a phone had 20 HomeKit apps, they would have 20 user interfaces to manage. The Home app unifies HomeKit apps, creating a central control center for all home automation applications.

With over 1 billion active Apple devices across the world, Home enters the market with giant global reach. Virtually every major manufacturer of home automation devices now supports HomeKit. Accessories cross all major categories, from lights and air conditioners to window shades, locks and home security. Commercial IoT companies now have massive domestic reach, and iOS users have more incentive to update their homes than ever before.

HAVE SIRI SET THE SCENE

Siri Scenes via Next Market

One of the coolest features of Home is the Siri integration. Users can control Home from both the Control Center and Siri, but Siri can work at the speed of your language. Siri’s ability to handle multiple requests means users can accomplish their ideal environmental preferences in the speed of a sentence. Apple refers to these combination commands as “scenes” and users can give “scenes” a nickname. A rambunctious user might say “Hey Siri, let’s get funky,” prompting Siri to lock the doors, dim the lights, put Barry White on the speakers at a reasonable volume and provide the most apt customized ambience for the user to rock out.

THE APPLE TV IS THE HEARTH

The fourth generation Apple TV can also act as a hub for the Home app, with the Siri Remote making it easy to control your home on the go. Apple TV’s seamless integration with HomeKit and other iOS products makes it the ultimate smart TV for a smart home, providing yet another reason for consumers to consistently buy iOS products.

BUILDING COMMERCIAL IOT FROM THE GROUND UP

The Home Automation page on the Apple website is a clear indicator of Apple’s intentions to not only be a household name, but to be the name on your household. The company already has a major market share of phones, tablets, computers, TVs and watches. They are rumored to be looking to acquire McLaren as a part of Project Titan. Apple understands that the ubiquity of the iOS platform makes them the most appealing platform for manufacturers of smart devices. Apple also announced that leading home builders, including Brookfield Residential, KB Home, Lennar Homes and R&F Properties, are integrating many HomeKit devices into new homes.

With the Apple Home potentially on the horizon, one can only wonder how much of Apple’s vision of the smarthome will be realized in the next 5-10 years.