Tag Archives: IoT

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

Artificial Intelligence & Machine Learning, , , , , , , , , ,

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.

Navigating the Future: Discover How Edge AI is Revolutionizing Autonomous Vehicles

Artificial Intelligence & Machine Learning, , , , , , , , , ,

Autonomous vehicles

This article marks the beginning of an insightful blog series dedicated to exploring the transformative impact of Edge AI on various sectors, starting with autonomous vehicles. Over the coming weeks, we will delve into the nuances of Edge AI, its technical foundations, and how it’s reshaping industries such as autonomous vehicles, consumer electronics, IoT devices, and smart sensors. Stay tuned as we unpack this cutting-edge technology’s advancements, challenges, and future prospects.

Imagine a world where cars drive themselves, adapting instantly to their surroundings with minimal latency. This isn’t science fiction; it’s the promise of Edge AI autonomous vehicles. Edge AI combines artificial intelligence and edge computing to process data locally, right where it’s generated, instead of relying on centralized cloud servers. In this blog, we’ll explore Edge AI’s profound impact on autonomous vehicles, offering insights into its advantages, challenges, and future potential. Whether you’re a CTO, CMO, tech enthusiast, CEO, or business owner, understanding this technology’s implications can help you stay ahead of the curve.

Understanding Edge AI

Edge AI refers to the deployment of AI algorithms on devices close to the source of data generation, such as sensors in autonomous vehicles. This approach reduces the need for constant communication with distant servers, resulting in faster decision-making and lower latency. By processing data at the edge, these vehicles can make real-time decisions essential for safe and efficient operation. Edge AI-powered vehicles can also communicate with other vehicles, road infrastructure, and pedestrians, enhancing their situational awareness and overall performance.

The integration of Edge AI into autonomous vehicles brings several notable benefits. Primarily, the ability to process data locally enhances the vehicle’s speed and responsiveness, which is crucial in dynamic driving environments. This reduces the lag time associated with sending data to and from cloud servers, ensuring that autonomous vehicles can react instantaneously to sudden changes such as a pedestrian stepping into the road or an unexpected obstacle appearing. Additionally, decentralized data processing helps to maintain a higher level of privacy and security, as sensitive information does not need to be transmitted over potentially vulnerable networks.

Google’s Waymo Self Driving Cars

As of June 2024, seven hundred Waymo self driving cars are on public roadways.

In this captivating video, we explore how Google’s Waymo self-driving cars are making waves in San Francisco and Los Angeles, showcasing the transformative power of autonomous technology in urban environments. Watch as these vehicles navigate bustling streets, interact seamlessly with traffic, and adapt to various driving conditions, all while prioritizing safety. With real-time data processing powered by Edge AI, these cars demonstrate unprecedented efficiency and reliability, paving the way for the future of transportation. Join us on this journey to witness the evolution of mobility and the potential for self-driving cars to reshape our cities.

Enhancing Real-Time Decision Making

Decision Making

Edge AI plays a crucial role in advancing the safety, efficiency, and robustness of autonomous driving technology. It enhances real-time decision-making by processing data on the vehicle itself, thereby reducing delays associated with traditional cloud-based systems. For instance, an autonomous car can analyze and respond almost instantaneously to unexpected obstacles, improving safety and performance, especially in challenging driving conditions like heavy traffic or adverse weather.

Additionally, Edge AI fosters a more reliable autonomous driving experience through redundancy and fault tolerance. By enabling multiple AI processes to occur independently at the edge, vehicles can maintain functionality even if one process fails. This approach also reduces bandwidth usage, mitigating the risks of network congestion and data bottlenecks. Collectively, these advantages illustrate the instrumental role of Edge AI in the future of autonomous driving.

Improving Safety and Reliability

Safety is paramount in autonomous driving, and Edge AI plays a crucial role in enhancing it. With the ability to process data locally, vehicles can detect and react to hazards more quickly. Think of a pedestrian suddenly stepping onto the road. Edge AI allows the vehicle to recognize the danger and take immediate action, potentially preventing accidents. This localized processing also adds a layer of reliability, as the vehicle remains operational even if network connectivity is lost. In contrast, cloud-based systems may experience downtime if connection issues arise.

Beyond immediate hazard detection, Edge AI contributes to more nuanced safety measures through continuous environment monitoring and adaptive learning. This means the vehicle can learn from its surroundings, improving its response to repeated patterns of certain conditions like heavy pedestrian traffic near schools or sharp turns in mountainous roads. Edge AI systems can be continually updated with new data and software enhancements without needing extensive downtime, ensuring the vehicles are up-to-date with the latest safety algorithms and threat detection models.

Lastly, Edge AI facilitates better fleet management for companies that operate multiple autonomous vehicles. By collecting and processing data locally, fleet operators can monitor vehicle performance and health in real-time, scheduling proactive maintenance and detecting potential issues before they lead to breakdowns or safety incidents. This degree of oversight ensures that each vehicle remains in optimal working condition, enhancing the overall safety and reliability of autonomous transportation systems.

Reducing Operational Costs

Operational Costs

Edge AI can significantly reduce operational costs for autonomous vehicle fleets. By minimizing data transmission to cloud servers, companies can save on bandwidth and storage expenses. Additionally, local processing reduces the reliance on expensive, high-speed internet connections. Over time, these cost savings can be substantial, making autonomous vehicles more economically viable for businesses. This can accelerate the adoption of autonomous vehicles, leading to increased efficiency and productivity in transportation.

Enhancing User Experience

User Experience

For passengers, the user experience is a critical aspect of autonomous travel. Edge AI contributes to a smoother and more responsive ride. Imagine a scenario where the vehicle needs to reroute due to sudden traffic congestion. Edge AI enables quick recalculations and adjustments, ensuring passengers reach their destinations efficiently. This improved responsiveness can lead to higher satisfaction and increased adoption of autonomous vehicles.

Pros and Cons of Edge AI Autonomous Vehicles

Pros

One of the most significant advantages of Edge AI is low latency. Immediate data processing allows vehicles to make real-time decisions, thereby enhancing safety and performance. The quicker a vehicle can respond to its environment, the safer and more efficient it becomes.

Another considerable benefit is reliability. With continuous operation even without network connectivity, Edge AI ensures that the vehicle can always make critical decisions. This resilience is especially important in areas with poor network coverage or temporary signal loss.

Cost savings present another advantage. By reducing the need to constantly transmit data to and from cloud servers, operational expenses connected to bandwidth and storage are minimized. This cost efficiency makes autonomous vehicle fleets more economically viable, encouraging broader adoption.

Cons

Despite its advantages, Edge AI does come with hardware limitations. Edge devices often have constraints in terms of processing power and storage capacity. This limitation can affect the vehicles’ ability to process complex algorithms locally, posing a challenge that needs to be overcome with advanced technology and engineering.
Complexity is another challenge. Integrating Edge AI into autonomous systems requires sophisticated algorithms and robust infrastructure. The intricacies involved in ensuring seamless operation can be a hurdle for vehicle manufacturers looking to adopt this technology.

Finally, security risks are a significant concern. Localized data processing means that Edge AI systems can be more vulnerable to physical tampering and cyber threats. Securing the data and ensuring the integrity of the processing units are critical tasks that must be addressed to maintain the safety and reliability of autonomous vehicles. Understanding these pros and cons is essential for businesses and technologists aiming to harness the full potential of Edge AI in autonomous vehicles.

Future of Edge AI in Autonomous Vehicles

Future

The future of Edge AI in autonomous vehicles looks promising. With advancements in AI algorithms and edge computing hardware, we can expect even greater capabilities and efficiencies. Upcoming developments may include more sophisticated object detection, predictive maintenance, and enhanced passenger personalization. These innovations will continue to push the boundaries of what autonomous vehicles can achieve. As technology improves, it is vital to address the associated challenges and risks to ensure the safe and seamless integration of Edge AI in autonomous vehicles.

The journey towards fully autonomous vehicles continues, with Edge AI playing a significant role in shaping its future. Therefore, businesses, researchers, and policymakers must collaborate and invest in this innovative technology to bring us closer to a safer and more efficient transportation system. The potential benefits are vast, and with continued development and refinement, we can expect even greater advancements in the near future. Embracing Edge AI in autonomous vehicles will undoubtedly pave the way towards a smarter and more connected future. Let’s continue to explore the possibilities and strive towards a world where vehicles can navigate the roads with precision, speed, and safety through the power of Edge AI.

Conclusion

Edge AI is set to revolutionize autonomous vehicles, offering significant improvements in safety, efficiency, and user experience. By harnessing the power of local data processing, these vehicles can make real-time decisions, ensuring smoother and safer rides. Enhanced reliability, even in areas with poor network connectivity, further solidifies Edge AI’s role in the future of transportation. Additionally, the operational cost savings associated with minimized data transmission can lead to a more economically viable approach for businesses, accelerating the adoption of autonomous vehicles.

Understanding the full impact and potential of Edge AI is crucial for business leaders and technologists. Anticipating these changes allows for better strategic planning and investment in infrastructure that supports this advanced technology. As we continue to explore the possibilities of Edge AI, it’s essential to address the challenges related to hardware limitations, complexity, and security risks to fully leverage its benefits.

Stay tuned for our next blog in the series where we’ll delve into Edge AI in Consumer Electronics. We’ll explore how this technology is transforming everyday devices, from smart home systems to personal gadgets, enhancing daily life through improved functionality, responsiveness, and user experience. The journey of Edge AI is just beginning, and its influence is expected to permeate various sectors, bringing unprecedented advancements and efficiencies. Embracing this innovation will undoubtedly pave the way towards a smarter, safer, and more interconnected world.

The Future of Personalization: How the Internet of Behaviors is Crafting Individual Experiences

Internet Of Behaviors IoB, Internet of Things, , , , , , , , , ,

IOB Contextual Targeting

Throughout this blog series, we’ve explored various facets of IoB, from its application in smart cities to its role in behavioral analytics. By examining how IoB is revolutionizing personalization and enabling precision targeting, we aim to offer a comprehensive understanding of this burgeoning field. Whether creating tailored experiences or enhancing engagement through data-driven insights, IoB leads modern technological advancement.

In today’s digital landscape, the Internet of Behaviors (IoB) has become a transformative force, changing how businesses approach personalization and targeting. For tech executives, CMOs, CTOs, and business owners, understanding and leveraging IoB can provide a significant competitive edge. This blog delves into IoB’s profound effects on personalization and targeting and connects it to our previous discussion on behavioral analytics. Let’s explore how IoB is shaping future business strategies.

Introduction to IoB in Personalization and Targeting

IoB in Personalization and Targeting

As consumers interact with digital platforms, they generate vast amounts of data. IoB leverages this data to gain insights into user behaviors, preferences, and patterns. By analyzing these behaviors, businesses can craft highly personalized experiences that resonate with individual users. The result? Enhanced targeting, increased engagement, and improved conversion rates. This shift towards personalization is not just a trend but a necessity in today’s customer-centric market. IoB enables companies to deliver hyper-relevant messages and offers at the right time, in the right context, and through the right channels.

By continuously monitoring and analyzing consumer behavior, IoB enables businesses to stay ahead of shifting trends and adapt their strategies in real-time. This dynamic approach not only enhances the consumer experience but also builds brand loyalty, as customers feel understood and valued. Furthermore, IoB-driven insights allow for more precise segmentation, ensuring that marketing efforts are well-spent on the appropriate audiences. Ultimately, the integration of IoB in personalization and targeting processes empowers businesses to maximize their ROI and foster long-term relationships with their customers.

The Power of Contextual Targeting

Context is crucial for personalization. With access to data from multiple sources like websites, social media, and location-based services, IoB provides a comprehensive view of customers. This enables businesses to understand not just what consumers are doing, but why. By leveraging this data, companies can tailor their messaging and offers to fit a user’s current situation, needs, and preferences. For instance, a retail brand can use IoB to send personalized promotions to shoppers who have shown interest in specific products. Contextual targeting helps businesses refine their customer journey maps, identifying pain points and optimizing interactions for a seamless experience. This leads to improved customer satisfaction, reduced friction, and a more cohesive brand experience, ultimately driving loyalty and long-term success.

Enhanced User Experience through Personalization

IOB User Experience

One of the most significant benefits of IoB in personalization is the ability to deliver tailored content and experiences. By understanding individual user behaviors, businesses can provide recommendations, offers, and content that align with each user’s interests. For example, e-commerce platforms like ShopEase leverage IoB to create seamless shopping journeys. ShopEase collects data from various touchpoints, constructing comprehensive profiles for each user. This enables the platform to personalize product suggestions, driving higher sales and customer satisfaction. Moreover, IoB extends to customer support, where virtual assistants and chatbots use behavioral data to anticipate needs and provide timely responses, enhancing overall user experience.

The Ethical Implications of IoB in Personalization

While IoB offers vast benefits in personalization, it also presents ethical considerations. Companies must responsibly and transparently handle sensitive data, ensuring explicit user consent and robust privacy measures. Ethical practices are essential to maintain consumer trust as IoB evolves. Moreover, businesses must address potential algorithmic biases in IoB-driven personalization, which could reinforce stereotypes or exclude users. Regular audits, diverse development teams, and transparency in data use can help mitigate these risks. By prioritizing these measures, businesses can ensure their IoB strategies are both effective and equitable for all users.

Precision in Marketing Efforts

IOB Marketing Efforts

IoB enables precise targeting by allowing businesses to segment their audience based on behavior, complementing traditional demographic-based targeting with behavioral data. This comprehensive understanding leads to more effective marketing campaigns, as tailored messages and offers resonate with specific user segments. For instance, CMOs can use IoB data to identify high-value customers and create targeted campaigns that yield higher ROI.

By leveraging IoB data, businesses can also employ real-time marketing strategies that adjust based on current customer behaviors and conditions. This dynamic approach ensures the relevance and timeliness of marketing messages. For example, a travel agency could send personalized destination recommendations based on recent travel searches, while retailers might push real-time discounts to nearby customers to drive immediate foot traffic. This precision and responsiveness not only enhance customer engagement but also improve the effectiveness of marketing efforts, increasing conversions and fostering customer loyalty.

Improved Customer Retention

IOB Customer Retention

Understanding customer behavior is key to retention. IoB allows businesses to anticipate customer needs and proactively address potential issues. By analyzing patterns in user behavior, companies can identify signs of churn and implement retention strategies. For example, subscription-based services can use IoB to detect when a user is likely to cancel and offer personalized incentives to retain them. This proactive approach not only reduces churn but also strengthens customer loyalty. By leveraging IoB-driven insights, companies can implement more tailored and timely interventions that resonate with their customers on a personal level.

Integration with Behavioral Analytics

Our previous discussion on IoB in behavioral analytics highlighted the importance of understanding user behavior to drive business decisions. When combined with personalization and targeting, IoB provides a comprehensive framework for optimizing customer interactions. Behavioral analytics offers insights into why users behave a certain way, while IoB focuses on leveraging these behaviors for targeted actions. This synergy enhances the effectiveness of both personalization and targeting strategies, creating a seamless customer experience. By combining the strengths of IoB and behavioral analytics, businesses can create highly adaptive and responsive customer engagement models. This integration allows for continuous learning and adjustment based on real-time data, ensuring that marketing efforts remain pertinent and effective.

Cons

Collecting and analyzing behavioral data raises significant privacy concerns that need to be addressed to maintain user trust. Ensuring the security of sensitive user data is paramount to protect against breaches and misuse. The implementation complexity of integrating IoB into existing systems can be both resource-intensive and challenging, requiring substantial investment in time and technology. Moreover, the effectiveness of IoB is highly dependent on the quality and accuracy of the data collected; poor data quality can lead to ineffective or misleading insights. Finally, adhering to data protection regulations is crucial for compliance, as failure to do so can result in legal repercussions and diminished consumer confidence.

The Future of IoB in Personalization and Targeting

IOB Future of IoB in Personalization and Targeting

Looking ahead, the future of IoB in personalization and targeting is promising yet challenging. As technology continues to advance, the potential for even more granular and real-time personalization will grow. However, businesses must navigate privacy concerns and regulatory landscapes carefully. The integration of IoB with emerging technologies like artificial intelligence and machine learning will further enhance its capabilities, providing deeper insights and more precise targeting. Companies that invest in robust IoB strategies will be well-positioned to thrive in the competitive digital marketplace. Overall, IoB is transforming the way businesses interact with their customers and will continue to shape the future of personalization and targeting in the years to come. So, it is essential for companies to embrace this technology and put ethical frameworks in place to ensure responsible use of user data for a seamless and personalized customer experience.

Conclusion

The Internet of Behaviors (IoB) represents a revolutionary way to understand and influence user behavior through data. Throughout this blog series, we’ve explored various aspects of IoB, from smart cities to behavioral analytics, and its impact on personalization and targeting. It’s clear that IoB has the potential to transform customer interactions, urban management, and business strategies. However, businesses must implement ethical practices and robust data protection measures to build trust. Leveraging behavioral data enables companies to deliver tailored experiences, driving engagement, conversions, and retention. While promising, IoB also presents challenges that need careful navigation. Tech executives, CMOs, and business owners must embrace IoB to stay competitive and succeed in a more personalized future.

Smart Cities: Transforming Urban Living for a Sustainable Future

Smart City
Internet Of Behaviors IoB, Internet of Things, , , , , , , , , ,

The Internet of Behaviors (IoB) is transforming how we interact with technology and data. By leveraging insights from user behaviors, IoB aims to enhance user experiences, streamline operations, and drive innovation across various sectors. Among its most promising applications is within the realm of smart cities. IoB smart cities are designed to create more efficient, sustainable, and livable urban environments by integrating IoT devices, data analytics, and behavioral insights. In this blog, we’ll delve into what makes IoB smart cities a blueprint for the future. 

The Rise of Smart Cities

The concept of smart cities revolves around creating environments that are not only technologically advanced but also focused on improving the everyday experiences of their inhabitants. At the core, smart cities utilize a network of interconnected devices and systems—often referred to as the Internet of Things (IoT) —to gather real-time data. This extensive data collection enables city planners and administrators to make informed decisions on urban planning and management.

For instance, smart transportation systems can reduce traffic congestion and improve public transit efficiency by analyzing patterns and predicting travel demands. Similarly, smart grids help optimize energy consumption, leading to more sustainable power usage and reduced environmental impact. Waste management systems benefit from sensors that monitor bin levels and dynamically adjust collection routes, ensuring efficient resource use and cleaner urban spaces. Public services such as street lighting, emergency response, and even healthcare are enhanced through data-driven approaches that adapt to the needs of residents.

Overall, the vision of a smart city is to seamlessly integrate technology and data into the urban fabric, creating a responsive, adaptive, and holistic environment that prioritizes the well-being of its citizens while managing resources judiciously.

The Importance of IoB Smart Cities

Internet Of Behaviors

The importance of IoB smart cities cannot be overstated. They represent a critical step towards creating urban environments that are not only more efficient and sustainable but also more responsive to the needs and behaviors of their inhabitants. By harnessing the power of data, IoB smart cities can optimize resource allocation, enhance public services, and foster economic growth, ultimately improving the quality of life for their residents. As we continue to explore the potential of IoB, smart cities will undoubtedly play a significant role in shaping our future. So, it is crucial to monitor and regulate their development carefully. IoB smart cities can pave the way toward a smarter, more connected, and sustainable future with proper planning and consideration for ethical and privacy concerns. 

As we continue to innovate and expand our understanding of IoB, the potential for smart cities will only continue to grow. By embracing the principles of sustainability, efficiency, and citizen-centric design, IoB smart cities have the power to create a better world for all. With careful planning and consideration for ethical and privacy concerns, these cities can pave the way toward a smarter, more connected, and sustainable future.

Benefits of IoB Smart Cities

One of the primary advantages of IoB smart cities is their contribution to sustainability and the environment. Utilizing green energy solutions such as solar panels and wind turbines, these cities can significantly reduce their carbon footprint. Additionally, advanced waste management systems powered by IoB technologies enable efficient sorting and recycling processes, leading to reduced landfill usage. Air quality monitoring devices provide real-time data, helping to mitigate pollution and protect public health. Water conservation efforts are also enhanced through smart metering and leak detection technologies, ensuring sustainable water usage. Overall, IoB smart cities promote sustainable living and contribute to a healthier environment.

Urban Mobility

IOB Smart City

Urban mobility is another area where IoB smart cities excel. Autonomous vehicles, guided by IoB data, promise to reduce traffic congestion and lower accident rates, offering a safer and more efficient mode of transport. Innovations in public transport include real-time scheduling and route optimization based on passenger behavior and preferences. Bike-sharing programs, powered by IoB, provide a flexible and eco-friendly alternative to traditional transportation methods. Advanced traffic management systems use data analytics to adjust traffic signals dynamically, reducing wait times and improving overall traffic flow. These advancements contribute to a more streamlined and efficient urban mobility experience.

Public Safety and Security

Smart Governance

Public safety and security are paramount in IoB smart cities. Modern surveillance systems equipped with facial recognition and behavior analysis can preemptively identify and address potential security threats. Emergency response technologies, such as connected fire alarms and medical alert systems, ensure rapid assistance during crises. Cybersecurity measures are crucial to protect the vast amounts of data generated and stored within IoB ecosystems, safeguarding both infrastructure and citizen information from malicious attacks. Through these technological advancements, IoB smart cities create a safer and more secure environment for their citizens.

Healthcare

In terms of healthcare and well-being, IoB plays a transformative role. Telemedicine services allow for remote consultations, making healthcare more accessible, especially in underserved areas. Smart hospitals utilize IoB technologies for patient monitoring, resource management, and operational efficiency, improving the overall standard of care. Wearable health monitoring systems enable individuals to track their vital signs and receive personalized health advice. Public health data collected through IoB devices aid in the early detection and management of disease outbreaks, promoting a healthier community. These advancements in healthcare and well-being, powered by IoB, contribute to a better quality of life for citizens.

Smart Governance

iOB governance

Smart governance is another cornerstone of IoB smart cities. E-governance platforms facilitate seamless interaction between citizens and government bodies, improving service delivery and citizen satisfaction. Data-driven decision-making ensures that policies and initiatives are based on accurate and timely information, enhancing their efficacy. Citizen services, such as online portals for utilities and permits, simplify processes and reduce administrative burdens. Open data initiatives enable citizens to access and analyze government data, fostering transparency and accountability. Through these digital advancements, IoB smart cities promote efficient, transparent, and responsive governance.

Connectivity in IoB Communities

Smart City Grid

Communities within IoB smart cities are connected through a seamless network of IoT devices and data platforms. This interconnectedness enables real-time communication and collaboration across various sectors. For example, smart grids dynamically balance energy supply and demand, while smart street lighting adjusts based on pedestrian and vehicular traffic patterns. Public transport systems communicate with traffic management centers to optimize routes and schedules, ensuring efficient and reliable service. These connected communities also foster citizen engagement and participation through social media platforms and community forums, allowing for input and feedback on various initiatives. The connectivity in IoB communities promotes a sense of belonging and enables collaboration towards shared goals, ultimately creating a more cohesive society.

Furthermore, IoB technologies facilitate community engagement. Residents can provide feedback and report issues through mobile apps, contributing to continuous improvement and fostering a sense of ownership and participation. Social media platforms and online forums enable the sharing of experiences and ideas, strengthening community bonds.

Cons of IoB Smart Cities

Despite their numerous benefits, IoB smart cities have challenges. The reliance on extensive data collection raises significant privacy concerns. Ensuring that personal information is securely stored and ethically used is paramount to maintaining public trust. Additionally, the high cost of implementation and maintenance can be a barrier for some municipalities. Integrating diverse technologies requires substantial investment in infrastructure and ongoing operational costs. 

There is also the risk of technological obsolescence. As new technologies emerge, existing systems may become outdated quickly, necessitating continuous upgrades and investments. Interoperability issues arise when integrating multiple IoT devices and platforms, potentially leading to inefficiencies and increased complexity. Finally, the need for standardization and regulations for IoB technologies can create compliance challenges and hinder widespread adoption. Policymakers and industry leaders need to address these concerns and establish guidelines to ensure the responsible implementation of IoB in smart cities.

The Future of IoB Smart Cities

Machine Learning

Looking ahead, the potential of IoB smart cities is immense. Future developments may include even more sophisticated AI and machine learning algorithms capable of predicting and responding to urban challenges with unprecedented accuracy. Enhanced connectivity through 5G networks will provide faster and more reliable communication between devices, enabling more complex and integrated solutions. As the IoB ecosystem continues to expand, it will also create new job opportunities in fields such as data science and cyber security. However, careful consideration must be given to data privacy and security as these technologies continue to evolve. As technology continues to shape the world around us, IoB smart cities will play a crucial role in how we design and manage our urban environments. By leveraging the power of data, connectivity, and citizen engagement, these cities have the potential to become more sustainable, efficient, and responsive

We can also expect advancements in autonomous systems, from self-driving cars to drone-based delivery services, further revolutionizing urban mobility and logistics. The integration of virtual and augmented reality technologies may offer new ways for citizens to interact with their environment, from virtual city tours to augmented reality navigation aids. The possibilities are endless, and as we continue to push the boundaries of what is possible with IoB, smart cities will continue to evolve and shape the world around us. Municipalities, policymakers, and industry leaders need to work together to ensure the responsible and ethical development of IoB smart cities for the betterment of society as a whole. With careful planning, collaboration, and consideration for all stakeholders, we can create a future where technology enhances our lives and makes our cities smarter, more connected, and more sustainable than ever before.

Conclusion

IoB smart cities represent a promising vision for the future of urban living. By integrating IoT devices, data analytics, and behavioral insights, these cities can achieve greater efficiency, sustainability, and quality of life for their residents. However, addressing privacy, cost, and technological obsolescence challenges is crucial for realizing their full potential.

As we continue to explore the possibilities of IoB, the next frontier lies in its application to behavioral analysis, which will be the focus of our upcoming blog. In our next blog, we will dive deeper into how IoB revolutionizes behavioral analysis, offering new insights into human behavior and driving innovations across various sectors.

How Zigbee Pro Makes Life Easier for IoT Developers

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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.

How 5G Is Unlocking the Hidden Potential of Mobile Gaming

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5G is the fastest growing mobile generation of all time. There are 236 active 5G subscriptions with 3 billion 5G subscriptions anticipated by 2025. The vastly improved speed will have a transformative impact on a number of technologies—especially mobile gaming.

The game industry generates over $175 billion in revenue. Mobile gamers constitute over 2.6 billion mobile gamers worldwide. Mobile gaming is big business and 5G stands to unlock even more hidden potential in a variety of ways.

SAY GOODBYE TO LATENCY

Imagine thousands of players simultaneously playing Call of Duty on mobile devices with no lag and professional-level speed. Mobile operators like Ericsson are pushing the 5G revolution everyday to make these scenarios a reality.

In competitive gaming, latency can make or break a game. Online gamers can easily become frustrated and churn if latency ruins their experience. With speeds up to 10Gbps, 5G delivers 10 times the speed of 4G LTE. Response times as low as 5 milliseconds will virtually eliminate lag between input and response. As 5G becomes more reliable, it will hold true even for massive online games with a large number of competitors.

In a recent survey conducted by OpenSignal on the best networks for gaming experiences, mmWave 5G led the way—ahead of private wi-fi networks.

OFFLOADING PROCESSING

For IoT developers, 5G’s ability to bolster cloud computing poses major opportunities. 5G enables simple devices and sensors to complete complex tasks by offloading major processing duties to the cloud.

Mobile gaming can similarly benefit from the raw speed of 5G. Higher bandwidth and lower response times open up the possibility of offloading key processes. Games will be able to handle more arduous rendering duties remotely, producing a higher quality stream. With the cloud carrying the workload, the quality and age of the user’s device would matter significantly less provided the user has a 5G data plan. Gamers will still be able to expect the same high-quality experiences regardless of whether they have the latest smartphone.

AR AND VR BOON

Massive increases in streaming speed and cloud processing power will enable AR and VR developers to take their projects to the next level. Expect to see bigger, more detailed and immersive virtual worlds. For AR and VR apps to realize their potential, data needs to be rendered at the moment of decision-making. Lag and latency take users out of what’s intended to be the pinnacle of immersive gaming. The speeds of 5G will trigger a momentous evolution for AR and VR gaming.

CAN MOBILE GAMING OVERTAKE CONSOLES?

Smartphones provide game developers with the broadest platform and userbase. The bolstered processing power provided by smartphones with 5G will enable mobile devices to provide graphics on par with consoles. However, before mobile gaming can dethrone console gaming in terms of popularity, there are a few roadblocks.

While some major console games are available on mobile devices, the vast majority are not. Gamers are not at the point where they expect the same games on their mobile devices. Until game developers can effectively transfer major franchises to mobile devices, console gaming will remain king.

One of the major obstacles facing mobile gaming is the controller. iOS and Android touch screens simply cannot match the precision of playing on an Xbox Series X or PS5. Gamepads like the Backbone One latch onto smart devices to enable more fluid controls, but they also take away the mobile aspect of games since they are arduous to carry. Furthermore, they require every game to map inputs to each controller’s layout. The controller poses a simple but major problem and until it’s addressed, console gaming will provide more reliable experiences.

How the Internet of Behaviors Will Shape the Future of Digital Marketing

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In the digital age, businesses need to leverage every possible platform and cutting-edge technology in order to get a leg up on the competition. We’ve covered the Internet of Things extensively on the Mystic Media blog, but a new and related tech trend is making waves. This trend is called the Internet of Behaviors and according to Gartner, about 40% of people will have their behavior tracked by the IoB globally by 2023.

WHAT IS THE IOB?

Internet of Behavior, or the IoB, exists at the intersection of technology, data analytics, and behavioral science. The IoB leverages data collected from a variety of sources, including online activities, social media, wearable devices, commercial transactions and IoT devices, in order to deliver insights related to consumers and purchasing behavior.

With devices more interconnected than ever, the IoB tracks, gathers, combines and interprets massive data sets so that businesses can better understand their consumers. Businesses leverage analysis from the IoB to offer more personalized marketing with the goal of influencing consumer decision making.

HOW DOES IT WORK?

Traditionally, a car insurance company would analyze a customer’s driving history in order to determine if they are a good or bad driver. However, in today’s digital age, they might take it a step further and analyze social media profiles in order to “predict” whether a customer is a safe driver. Imagine what insights they could gather from a user’s Google search history or Amazon purchases? Access to large datasets enables large companies to create psychographic profiles and gather an enhanced understanding of their customer base.

Businesses can use the IoB for more than just purchasing decisions. UX designers can leverage insights to deliver more effective customer experiences. Large companies such as Ford are designing autonomous vehicles that change based on the city, modulating behavior based on vehicle traffic, pedestrians, bicycles and more.

GBKSOFT created a mobile application that collects data from wearable devices in order to help golfers improve their skills. The application records each golf ball hit, including the stroke, force, trajectory and angle, and delivers visual recommendations to improve their swing and technique. Insights gathered through data are translated into behavioral trends that are then converted into recommendations to improve the user’s game.

The IoB is all about collecting data that can be translated into behavior which helps companies understand consumer tendencies and translate them into meaningful actions.

CONCERNS

While there is quite a bit of enthusiasm surrounding the potential impact of the IoB for B2C companies, a number of legal concerns come with it. A New York Times article, written by Harvard Business School emeritus professor Shoshana Zuboff, warns of the age of surveillance capitalism where tech behemoths surveil humans with the intent to control their behavior.

Due to the speed at which technology and the ability to collect data has proliferated, privacy and data security are under-regulated and major concerns for consumers. For example, Facebook was applying facial recognition scans in advance of the 2016 election without user’s consent. Cambridge Analytica’s use of psychoanalytic profiles has been the subject of much derision. Momentum for data privacy regulation is growing and since the IoB hinges on the ability for companies to collect and market data, forthcoming regulations could inhibit its impact.

CONCLUSION

Despite regulatory concerns, the IoB is a sector that we expect to see grow over time. As the IoT generates big data and AI evolves to learn how to parse through and analyze it, it’s only natural that companies will take the next step to leverage analysis to enhance their understanding of their customers’ behaviors and use it to their advantage. The IoB is where that next step will take place.

How Bluetooth Became the Gold Standard of Wireless Audio Technology

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Bluetooth technology has established itself over the years as the premiere wireless audio technology and a staple of every smartphone user’s daily mobile experience. From wireless headphones, to speakers, to keyboards, gaming controllers, IoT devices, and instant hotspots—Bluetooth is used for a growing variety of functions every year.

While Bluetooth is now a household name, the path to popularity was built over the course of over 20 years.

CONCEPTION

In 1994, Dr. Jaap Haartsen—an electrical engineer working for Ericsson’s Mobile Terminal Division in Lund—was tasked with creating an indoor wireless communication system for short-range radio connections. He ultimately created the Bluetooth protocol. Named after the renowned Viking king who united Denmark and Norway in 958 AD, the Bluetooth protocol was designed to replace RS-232 telecommunication cables using short range UHF radio waves between 2.4 and 2.485 GHz.

In 1998, he helped create the Bluetooth Special Interest Group, driving the standardization of the Bluetooth radio interface and obtaining worldwide regulatory approval for Bluetooth technology. To this day, Bluetooth SIG publishes and promotes the Bluetooth standard as well as revisions.

BLUETOOTH REACHES CONSUMERS

In 1999, Ericsson introduced the first major Bluetooth product for consumers in the form of a hands-free mobile headset. The headset won the “Best of Show Technology” award at COMDEX and was equipped with Bluetooth 1.0.

Each iteration of Bluetooth has three main distinguishing factors:

  • Range
  • Data speed
  • Power consumption

The strength of these factors is determined by both the modulation scheme and data packet employed. As you might imagine, Bluetooth 1.0 was far slower than the Bluetooth we’ve become accustomed to in 2021. Data speeds capped at 1Mbps with a range up to 10 meters. While we use Bluetooth to listen to audio on a regular basis today, it was hardly equipped to handle music and primarily designed for wireless voice calls.

THE BLUETOOTH EVOLUTION

The Bluetooth we currently enjoy in 2021 is version 5. Over the years, Bluetooth’s range, data speed, and power consumption have increased dramatically.

In 2004, Bluetooth 2.0 focused on enhancing the data rate, pushing from 0.7Mbps in version 1 to 1-3Mbps while increasing range from 10m to 30m. Bluetooth 3.0 increased speeds in 2009, allowing up to 24Mbps.

In 2011, Bluetooth 4.0 introduced a major innovation in BLE (Bluetooth Low Energy). BLE is an alternate Bluetooth segment designed for very low power operation. It enables major flexibility to build products that meet the unique connectivity requirements of their market. BLE is tailored toward burst-like communications, remaining in sleep mode before and after the connection initiates. The decreased power consumption takes IoT devices like industrial monitoring sensors, blood pressure monitoring, and Fitbit devices to the next level. These devices can employ BLE to run at 1Mbps at very low power consumption rates. In addition to lowering the power consumption, Bluetooth 4.0 doubles the typical maximum range from 30m in Bluetooth 3.0 to 60m.

BLUETOOTH 5

Bluetooth 5 is the latest version of the technology. Bluetooth 5 doubles the bandwidth by doubling the speed of transmission. In addition, it quadruples the typical max range, bringing it up to 240m. Bluetooth 5 also introduces Bluetooth Low Energy audio, which enables one device to share audio with multiple other devices.

CONCLUSION

Bluetooth is a game-changing technology which stands to revolutionize more than just audio. IoT devices, health tech, and more stand to improve as the Bluetooth SIG continues to upgrade the protocol. After thirty years of improvement, the possibilities remain vast for savvy developers to take advantage of the latest Bluetooth protocols to build futuristic wireless technologies.

Cloud-Powered Microdroid Expands Possibilities for Android App Developers

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Android developers have a lot to look forward to in 2021, 2022, and beyond. Blockchain may decentralize how Android apps are developed, Flutter will see increased adoption for cross-platform development, and we expect big strides in AR and VR for the platform. Among the top trends in Android development, one potential innovation has caught the attention of savvy app developers: Microdroid.

Android developers and blogs were astir earlier this year when Google engineer Jiyong Park announced via the Android Open Source Project that they are working on a new, minimal Android-based Linux image called Microdroid.

Details about the project are scant, but it’s widely believed that Microdroid will essentially be a lighter version of the Android system image designed to function on virtual machines. Google is preparing for a world in which even smartphone OS’s require a stripped-down version that can be run through the cloud.

Working from a truncated Linux, Microdroid will pull the system image from the device (tablet or phone), creating a simulated environment accessible from any remote device. It has the ability to enable a world in which users can access Google Play and any Android app using any device.

What does this mean for developers?

Microdroid will open up new possibilities for Android apps in embedded and IoT spaces which require potentially automated management and a contained virtual machine which can mitigate security risks. Cloud gaming, cloud computing—even smartphones with all features stored in the cloud—are possible. Although we will have to wait and see what big plans Google has for Microdroid and how Android developers capitalize on it, at this juncture, it’s looking like the shift to the cloud may entail major changes in how we interact with our devices. App developers are keen to keep their eyes and heads in the cloud.

Although no timeline for release has been revealed yet, we expect more on Microdroid with the announcement of Android 12.

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

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

WHAT IS LIDAR?

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

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

WHY IS LIDAR IMPORTANT?

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

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

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

SUPERCHARGING APPLE DEVICES

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

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

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

BIG INVESTMENTS BY AUTOMOTIVE COMPANIES

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

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

LIDAR DRIVING ENVIRONMENTAL APPLICATIONS

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

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

CONCLUSION

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

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