We are currently living in an interconnected digital world influenced by technology. Technology is transforming our daily lives and affecting businesses tremendously worldwide. People will be the most benefitted from the technology with enhanced lifestyle and quality of public services. The governance and routine processes involved in government offices and companies also adopt new technologies for better productivity and efficient performance.Modern technology brings intelligence into each domain by providing valuable information and scientifically data-derived decisions. Intelligent infrastructure, connectivity, smart agriculture, smart cities are some of the typical results from integrated technological solutions.This article focuses on smart cities, the technologies behind smart city development, and their vulnerability to cybersecurity risks. The article closes with a short discussion on future smart cities.<h3 id="what-is-a-smart-city">What Is A Smart City?</h3>A smart city is not just a widespread and hyped-up concept among people. Many metro cities around the world are using digital technologies to make their services better for citizens. There are interconnected services provided by local governance; Radio Frequency Identification (RFID) and traffic control are the common examples of how technology makes daily transportation easy and smooth.A smart city uses technology to improve the quality of service and life of its citizen. Smart cities solely depend on technologies to improve management, services, and operations. Precise decision-making is vital for smooth and seamless processes in all smart city associated sectors, such as private, public, and voluntary sectors. Officials use technological solutions to acknowledge present situations and future changes and optimize the city functions and provide solutions accordingly.A smart city has every node connected by the technology, from transport systems, power distributions, street lights to homes. The data is collected from each node to improve its service and make citizens? lives better and easier. For example, smart energy solutions reduce the energy consumption on streets, offices, and homes.The United Nations estimates about 68% of the global population will be in urban areas by 2050. Urban populations are the cause of significant environmental, economic and societal challenges. These challenges can be reduced with intelligence technology resources without sacrificing the quality of city life.A smart city is an interconnected network of devices such as sensors, smartphones, communication networks, and the Internet of Things (IoT). These devices share data with city officials, engineers, managers, and administrators over the internet. The meaningful information is extracted from the data, and various city operations and tasks are accomplished.<h3 id="smart-city-development-technologies">Smart City Development Technologies</h3>Smart city infrastructure development has several technical and non-technical building blocks. As smart city services depend on the data, the digital infrastructure to collect, transmit, analyze, store, and share data is essential. IoT devices need a 24X7 internet connection for which robust 5G communication network infrastructure is a crucial parameter. The design, development, production, integration, and performance of each layer of this infrastructure must be fully functional with standards followed.Each smart city requirement will be different, and so the infrastructure and technologies used in building the smart city. However, each smart city infrastructure needs to overcome its weaknesses through strengths and unique features. But, some fundamental technologies remain the same in the development of smart cities, such as information and communication technology (ICT), IoT, sensors, geospatial technology, AI, blockchain, data analytics, and ML.Sophisticated sensors are used to collect data from various locations. This data is pre-processed and transmitted through wireless sensor networks to the central control room. The software algorithms based on data analytics, AI and ML process the data and provide helpful information, patterns and structures that help experts make the most accurate decisions. A real-life example of this is tracking and monitoring daily data, and traffic control signals which can be adjusted automatically based on peak traffic time.Cities are already adapting different existing technologies and making citizen?s life less complicated, but smart cities will be entirely different in terms of functioning and controlling their assets. Though technologies remain at the center of smart city development, there is a dark side of cybersecurity threats and risks from technological dependencies.<h3 id="cybersecurity-risks-associated-with-smart-cities">Cybersecurity Risks Associated with Smart Cities</h3>Smart city works on central control; and its various systems, services and departments that are controlled by central management. Automatic management is one of the attractive features of smart cities: the blessing of intelligence and precision of advanced technologies.Internet-connected devices like cameras and sensors are installed everywhere in the city that collects enormous data. The data ranges from general monitoring to suspecting activities such as unauthenticated access to private or secure assets in the city. It is then transmitted to the central control over wireless networks and cloud-based data management systems. The analysis and decision-making are carried out by tracking data points with high intelligence and capacity.Now, all that looks very simple, but there are several potholes to this concept. Though smart city features exist already, they are not yet fully functional and secured. Many technologies are involved in the interconnected network that is not entirely evolved, properly synchronized and integrated. The complexity of interlinked networks is very high. It leaves room for cybercrime such as identity theft, data &amp; asset theft, device hijacking, distributed Denial-of-Service (DoS), Man-In-the-Middle attacks, physical disruption, and ransomware.Smart cities are prone to cybersecurity risks that can disable the functioning of smart cities while putting their entire population in danger. The deeply interconnected systems that run smart cities are very complicated. Unfortunately, there are no standard guidelines and best practices available for all smart city stakeholders like governments, software providers, local businesses, security providers, energy providers, IoT device makers, communication network providers and so on. All stakeholders need to consider cybersecurity as a serious issue while allowing any new technology to be installed directly into smart city system infrastructure.Smart city has physical, communication database and interface layers, and it is prone to cyberattacks at any layer. Each layer is integrated with more usage of technologies, which leads to more cybersecurity risks. Hackers get plenty of targets to attack from a network of thousands and millions of interconnected devices. Even unimportant unauthenticated access to the infrastructure can disrupt the entire smart city system. Therefore, care must be taken on the security and privacy of the data from ground level and safeguard it from falling into the wrong people?s hands.It is not only technologies, but hackers are becoming intelligent and coming out with new techniques and kinds of attacks. That will need continuous monitoring, auditing and updating devices, authenticity and authority of actions carried out on the data.<h3 id="future-smart-cities">Future Smart Cities</h3>Smooth transportation, no traffic jam, smart garbage collection, clean streets, smart power grids, seamless connectivity, intelligent waste management, recycled resources, solar panels, and automatic devices are the perfect picture we can imagine for the future smart city. Everything in the city will be organized for citizens? comfort, reducing power consumption and reducing pollution while improving public health and economic opportunities.There is no doubt that smart city development will be a reality soon as many governments have already sanctioned intelligent city projects, and development has been started in several nations. Technologies will upgrade, and so the smart city infrastructure. Thus, smart city development will be a never-ending process. The only concern to adapting and integrating new technologies will be making sure top security, privacy and protection of the entire data.Stakeholders should take early steps in securing smart cities at the start of the planning, during development and continue emphasizing security at each stage of the development and maintenance.

Pranita Herwade

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Cybersecurity

Cybersecurity Risks Associated with Smart Cities

Today we live in a digital era where everything is getting digitized and connected to each other. As a result, modern technologies alter the way people used to live, work, commute and communicate. These technologies, like Artificial Intelligence (AI), Internet of Things (IoT), Information &amp; Communication Technology (ICT), Machine Learning (ML), Computer vision, Blockchain, and Data Analytics, are still evolving but making remarkable transformations in industries. Among these technologies, IoT has proven potential to connect the physical and virtual world on a single platform and thus gaining popularity in every sector adopting advanced technologies.<h3 id="iot">IoT</h3>IoT stands for Internet of Things. The ?thing? in IoT can be any physical device such as smartphone, computer, washing machine, wearable device, television, headphone, lamp, building, vehicle or any other possible thing that can be thought of. The IoT technology will connect any systems, devices, objects that can be connected in the near future. These things are embedded with sensors, software, and other electronic components to transfer the data. The internet interconnects these devices and makes a giant IoT network of connected ?things?. There are three relationships in the IoT network viz<blockquote>Things-things (machines to machines)</blockquote><blockquote>Things-people (devices to people)</blockquote><blockquote>People-people (people to people)</blockquote><h3 id="significant-challenges-in-building-iot-solution">Significant Challenges in Building IoT Solution</h3>The integration of several devices connected through the network is prone to vulnerabilities, threats, and risks. Some standard IoT privacy and security problems are discussed below:? Authentication and AuthorizationExisting systems and software platforms are getting upgraded with technological advancements, thus increasing the data?s security and safety. But accordingly, hackers are becoming more dangerous in attacking or hacking systems leading to cybercrimes such as identity thefts, personal information misuse and others. This is because most of the devices have weak and predictable passwords. Additionally, many sensors, devices and systems have default authorization credentials that are easy to hack through the network.? Transport Layer InsecurityIoT infrastructure has different layers of configurations and systems such as hardware, communication, application, and communication layers. Systems in these layers communicate with each other to transmit and receive data. Transport layers must be secured to prevent data leakage. Data encryption is the best solution available today to transfer data safely over the internet, but most devices fail at this step. It leads to insecure data transfer and increases security issues of IoT solutions.? Insecure User InterfaceIoT devices such as smartphones, laptops, personal computers or even web interfaces have user interfaces to manage the device as well as to make appropriate use of the data collected. However, these devices are prone to security issues such as data leakage, cross-site scripting, or identity misuse. Therefore, it is vital to maintain proper security measures while operating and handling IoT solutions.? Disregarding Privacy ConcernsThere should be safe code practices and a proper set of rules while handling business logic and services data. Furthermore, private, confidential and sensitive information regarding the organization should be kept safe to avoid possible threats like cyberattacks. Therefore, organizations and governments must implement security methods while adopting IoT solutions in sensitive data handling.? IoT Security and Privacy LawsBusinesses look for customer privacy and secure IoT devices in compliance with IoT security and privacy laws. A few rules and regulations have been passed globally, such as the California Consumer Privacy Act (CCPA), European Union General Data Protection Regulation (GDPR). These acts are dedicated to protecting consumer privacy, but there are differences in scope, requirements and penalties for noncompliance for both laws. Moreover, there is no single globally accepted rule or regulation regarding IoT solutions that make it difficult for IoT solution manufacturers to produce solutions in compliance with these acts.<h3 id="iot-data-privacy-challenges">IoT Data Privacy Challenges</h3>IoT enables uninterrupted and fast machine-to-machine communication that has already attracted several industries, governments, and enterprises. They have initiated developing and implementing IoT solutions and aim to enhance the productivity and growth of organizations. However, as IoT is an emerging technology, there are still several challenges in building IoT solutions for several applications. For example, there are no universal standards for IoT solutions. Moreover, concerns regarding data privacy and application security are not addressed yet. IoT solutions will be used to collect, store, exchange and analyze massive data efficiently and securely. Thus, it is important to implement strategies in protecting all components of IoT solutions from evolving security threats.IoT interconnects billions to trillions of intelligent things with unique identities and addresses. These things process and communicate information about the physical environment and about themselves. These IoT systems are used in different applications such as advanced building management systems, pervasive healthcare, public surveillance, innovative city services, participatory sensing applications and data acquisition. The collection of vast amounts of data through these systems about people?s private lives and personal information raises serious data privacy concerns. These issues may have unwanted consequences like a failure of new services, costly lawsuits, reputation damage and so forth.IoT-related projects have experienced colossal <a href="https://arxiv.org/ftp/arxiv/papers/1505/1505.07683.pdf">data privacy issues</a> previously, like a public boycott of the Italian retailer Benetton in 2003, the revocation of the Dutch intelligent metering bill in 2009 and the recent outcry against the EU FP7 research project INDECT. IoT is dependent on different technologies like wireless sensor networks (WSN), mobile applications &amp; platforms, web personalization and Radio Frequency Identification (RFID).IoT features are changing with the latest technology developments. Simultaneously, the number of ways of interacting with smart things are growing rapidly and making networking more complex. Upcoming features will be exciting and attentive but will aggravate data privacy issues and introduce unknown threats. These known, as well as strange privacy issues, must be considered in reference models accounting for specific entities and data flows. In addition, the perspective of existing privacy legislation regarding evolving and unique IoT solution features should be considered while building IoT solutions.Privacy is one of the most important majors for people, and it is becoming prominent with increased usage and efficiency of electronic data processing. It was first defined in 1968 by Westin as ?the right to select what personal information about the person is known to what people?, and it is still equally valid. Before electronic data usage, data privacy concerns were limited and primarily controllable. For example, it is challenging to prevent cyberattacks and hacks in electronic data over the internet. Therefore, IoT solutions should guarantee? Awareness of privacy risks by IoT solutions collecting data? Understanding and control of the use and dissemination of personal data? Individual control on collecting and processing personal information by IoT solutionsThus, enterprises should focus on the security of IoT solution components like communication channels, embedded software, data, and other various devices that are prone to data privacy concerns. Multiple-factor authentication and authorisation processes should be implemented to ensure user identity and reduce opportunities for hackers to misuse confidential user information. Additionally, network firewall, custom security strategies and token-based authentication should be implemented on all aspects of every IoT application in the solution.

Data Privacy Challenges in Building an IoT Solution

The digital world we live in today has provided easy access to remote things and makes processes faster with online services. People need to open an account with the system services such as banks, social media, companies and other related services to get authorized access. These accounts are protected with passwords having alphabets, numbers, special characters and patterns. With in-depth knowledge, hackers can crack these passwords and get unauthorized access to private accounts. Moreover, physical threats and risks were always there to private, confidential, and personal data, assets and properties. To reduce these risks and dangers, face recognition systems were introduced about 60 years ago. Face recognition has been applied practically in law enforcement, biometrics, access control, surveillance, information security and smart cards. Many organizations prefer face recognition over other technologies as it removes physical interaction with the hardware, offers security &amp; reliability, reduces maintenance cost and dependence on hardware. Moreover, it provides easy access to multiple locations and entities. Face Recognition:Face recognition is identifying a person using a biometric method based on the face image. It is a part of computer vision. Biological traits are used to identify a person. A computerized method to perform face recognition was introduced due to the limited concentration span for human eyes. It includes automatic detection and verification of a person from an image or video. It is comprised of four basic steps:<ul><li>Face detection: Locating faces in the image or video and mark them with a bounding box.</li><li>Face alignment: Normalizing the face, like geometry and photometric, to make it consistent with the database.</li><li>Feature extraction: Extracting face features that can be used to perform recognition tasks.</li><li>Recognition task: Matching the test face against multiple known faces from the prepared/ available database. </li></ul>Face recognition systems perform each step in a separate program or module or may have a single process integrated into a system. The face recognition task is broad and has specific needs of a prediction problem like face matching, similarity, and transformation. Face matching finds the best match of a given face. Face similarity checks face most similar faces, and face transformation generates new faces identical to the given face. Two primary approaches followed in face recognition are:<ul><li>Feature-based face detection</li><li>Image-based face detection </li></ul>Though face recognition is extensively researched, it still has challenges such as misalignment, illumination variation, pose variation and expression variation. To improve the accuracy and degree of precision while performing face recognition, multiple approaches are considered. One of them is deep learning! Face Detection With Deep Learning:Deep learning is capable of processing massive data and creating patterns that are applied in decision making. As a function in Artificial Intelligence and a subset of machine learning, deep learning algorithms have neural networks to learn unsupervised data. Deep learning methods leverage large datasets of faces. It allows deep neural networks to perform and outperform the face recognition capabilities of humans by learning and compacting face representations. The face detection process is challenging due to different posture and light-shielding as it has a non-aligned and forcing environment. Recent research studies found that deep learning algorithms have the potential to solve these problems. Deep learning can approximate complex functions through the incremental hidden layers to separate and differentiate face recognition tasks. Among different neural network algorithms used in face detection, multi-task cascaded convolutional neural networks are most widely used. Face Detection using Multi-Task Cascaded Convolutional Neural NetworkFace detection and alignment is based on face recognition, behavioral analysis, posture, visual impact and so forth. Previously proposed cascade face detection algorithms dropped while responding to significant face most similar faces, visual effect of face images. Existing dimension-based partitioning and merging clustering (DPM) algorithms have some defects. Still, Convolutional Neural Network (ConvNet/CNN) algorithms have given good results in visual terms like image classification, face recognition, and so forth. Previously developed frameworks had issues either in face detection or face alignment and were not capable of clearly differentiating both the tasks. Multi-task Cascaded Convolutional Networks (MTCNN) is a framework developed to solve both face detection and face alignment problems in face recognition. The name is multi-task network as it includes three models, P-Net, R-Net and O-Net, trained on three different tasks. These three models are not connected directly, but the previous stage?s output is given as an input to the next stage. It allows additional processing between the stages whenever required. The MTCNN process has three stages of convolutional networks. These networks are used to recognize faces and landmark feature locations like nose, eyes, and mouth. First, the image is taken and resized to different scales to build an image pyramid. Then it is used as an input of the three-staged cascaded network of face detection. Let?s see three stages of MTCNN in detail: <ul><li>P-Net:</li></ul>P-Net stands for the proposed network. It is a fully convolutional network (FCN). It is used to gain candidate windows and bounding box regression vectors in the face image. Using this information, the localization of boxes is predicted while detecting an object from a pre-defined class. The bounding box vectors are refined to combine overlapping regions and candidate windows are obtained as an output. <ul><li>R-Net:</li></ul>The P-Net output is fed as an input to the Refine Network (R-Net). It is a CNN network as it has a dense layer at the last stage of the framework architecture. It overlaps the candidates using non-maximum suppression (NMS). It reduces the candidate numbers and calibrates the bounding box regression too. It predicts if the input is a face or not by localizing facial landmarks from the information. It is like a binary classification problem. <ul><li>O-Net:</li></ul>The Output Network (O-Net) describes the details in the face. As an output, it provides five facial landmark positions for (right and left) eye, nose and (left and right) mouth (corner). Thus, MTCNN carries out three main tasks viz. Face classification, bounding box regression and facial landmark localization. Among the number of deep learning methods developed and demonstrated in the application of face detection, due to the capability of landmark detection, MTCNN is the most popular approach adopted. Though MTCNN architecture is complex to implement, open-source implementations of the architecture are available to train on new datasets and use pre-trained models for face detection. ConclusionThe digital version of surveillance, authorization and authentication without directly handling hardware replaces previous digital versions of identification and verification. Face recognition is one of the oldest techniques to identify a person and its importance is increasing in almost all sectors. Face detection is a computer-vision problem of identifying and localizing faces in a given image. Different algorithms and methods are researched for face detection, but deep learning based MTCNN network architecture has been the most popular face detection method. Due to cascaded models, open-source architectures, and datasets, face detection is achieved most accurately using the MTCNN library. Therefore, MTCNN has prime importance in the face recognition industry.

How to Perform Face Recognition using Deep Learning?

<h3>Introduction</h3>The digital world is based on modern technologies providing access to the internet and distant places, people, and services. Blockchain and the Internet of Things (IoT) play an essential role in reforming our future lifestyle and work culture. IoT enables us to transfer data over the internet, having a network of physical objects. Though it has connected the world with faster communication, it has security, privacy, and safety challenges. These challenges can be overcome using blockchain technology. The combination of blockchain and IoT can provide us with a safe, secure transfer of data, confidential information, and knowledge throughout the IoT network.<h3>The Working of Blockchain Technology for IoT Use Cases:</h3>The adoption of technology is increasing in all sectors to improve management, handling, and safety. IoT is already implemented in various applications, but it needs to be improved in terms of data integrity, security, flexibility, and efficiency. Blockchain provides a decentralized approach with its distributed data ledger technology. It supports the privacy, flexibility, and efficiency requirements of IoT solutions when integrated with IoT systems. Let?s see some of the Blockchain IoT use cases Applications.<h3>Blockchain IoT Use Cases Applications:</h3>Industries are transforming with the introduction of the latest technologies in their infrastructure. Though IoT has reached almost all industry sectors, its use cases with Blockchain are still limited. The blockchain IoT use cases include supply chain and logistics, automotive industry, intelligent homes, sharing economy, pharmacy industry, agriculture, and water management. The number of use cases is increasing with the extensive research and development in modern technologies worldwide.Supply Chain and Logistics:The global supply chain and logistics involve many stakeholders such as brokers, distributors, raw material providers, transportation, and sellers. Due to many layers in the supply chain, the existing supply chain and logistics systems lack transparency, efficiency, and precise management. A new approach to enhance the systems is proposed that combines IoT and blockchain technology. IoT-enabled systems collect data using sensors like motion sensors, temperature sensors, GPS, connected devices, vehicles, and so forth. This data is then safely stored in the blockchain. Only registered stakeholders can access the data and get insights into the real-time data. It improves supply chain management and creates an immutable, secure, and visible ledger to improve transparency and accountability.Automotive Industry:Automation is getting significant attention in the digital era. The automotive industry is using IoT-enabled solutions to design and develop automatic vehicles. Blockchain IoT use cases for this industry might vary from fully automated cars to automated fuel payment, smart parking, automated traffic control. It can also be used to share data on vehicles with their owners, dealers, and manufacturers. Moreover, it has applications in securely linking data among different businesses held in the automotive industry, including manufacturers, dealers, insurers, etc. Conclusively, blockchain IoT use cases in the automotive industry can be categorized as verification and process improvement, vehicle management and incentives, finance, payments, and insurance.Smart Homes:The concept of a smart home is becoming popular among many parts of the world-dominating the technology providing smartness and intelligence to machines. The remote management and control of domestic appliances, systems have been possible with IoT solutions. The existing smart home solutions have issues regarding centralized approach, privacy, and security. Blockchain technology provides a decentralized system with higher security of the smart home system. With improved safety, sensitive data can be stored in the blockchain using biometrics, facial recognition, and voice recognition.Sharing Economy:The sharing economy business models are gaining popularity in the past ten years. The operational structure of these businesses may be disrupted with the new technologies such as IoT and blockchain providing automation. Sharing economy allows companies and private persons to offer services through online platforms or share their property. Currently, Uber, Airbnb, and other businesses using shared economy are using centralized settlement services to connect consumers and providers with each other. Current sharing economy business models have characteristics such as aggregate supply/ demand, customer relationship management tools, and payment processing. Blockchain IoT has a use case in sharing economy to provide decentralized settlement and smart contracts between providers and consumers.Pharmacy Industry:The pharmacy industry is one of the largest industries in the world. It develops, manufactures, and distributes drugs. It is challenging to track drugs in the supply chain and location as well as to keep records at each passing point in the supply chain. IoT blockchain technology can provide a transparent and traceable healthcare system having pharmaceutical logs and traces. Blockchain has an immutable and timestamped distributed ledger that stores data regarding manufacturers, distributors, wholesalers, dispensers, and end customers. This data is stored securely and accessed only by authorized people. Blockchain IoT in the pharmacy industry offers the advantage of the simplified payment process, stops counterfeit drugs from invading the supply chain, and controls user access. Moreover, it has use cases in clinical trials, patient data management, R&amp;D applications, and inventory management.Agriculture:The urgent need of the agriculture sector to increase the productivity, sustainability, and quality of food has led the agriculture technology developments. Modern technologies are used to collect data, analyze it and use it in decision making. IoT-enabled solutions are making it possible to integrate different systems to make one complete farm management solution. Blockchain can provide a legitimate source of information for farmers affordably. The accurate information about the inventories, state of farms, and contracts in agriculture can be accessed through blockchain solutions. Traceability has become of prime importance for consumers. Blockchain technology keeps track and records the provenance of food, creating a trustworthy supply chain. It will help to build trust between suppliers and consumers.Water Management:Water scarcity and reduced underground water resources are the biggest problems in front of the world in the 21st century. A water management system should achieve water conservation through proper tracing of the water supply and maintenance. The tracked record needs to be stored and handled by any central authority with limited access to the data. The data should be highly secure to avoid any malpractices in the water supply management network. The water quality and safety data need to be immutable and secure. Moreover, customers should be able to get transparent reports on their water usage. Though the data can be secured on IoT devices, other requirements can be satisfied successfully only by applying blockchain technology in water management. &nbsp;<h3>Conclusion</h3>Blockchain technology is a distributed ledger that provides a decentralized infrastructure to the IoT network. It provides advantages of privacy, security, data integrity, flexibility, and efficiency to IoT applications. Integration of blockchain technology with IoT solutions has different use cases. It includes but is not limited to supply chain and logistics, automotive industry, smart homes, sharing economy, pharmacy industry, agriculture, and water management, as explored in the article.

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Business

Wondering how Blockchain can work for IoT? Find out the latest and most promising use cases with this article by Techfastly.

Top 7 Blockchain IoT Use Cases | Techfastly

Blockchain With IoT Use Cases

This article will help you understand what Edge AI means, explore its applications and a detailed analysis of Edge AI's benefits. Read on at Techfastly.com

The real-time prediction needs are increasing for smart and sustainable developments. Industry 4.0 trends having Edge AI technology has autonomous systems and smart Internet of Things (IoT) devices.

Edge AI: The Future of Artificial Intelligence

Edge AI: Everything You Must Know | Techfastly

<h2>Introduction</h2>The modern world is surrounded by technologies, and we are spending more time on our devices. It is increasing the need to bring essential computation on our devices to help businesses serve more technology users. Artificial Intelligence (AI) evolution is taking place, and cloud computing is one of its essential elements. It is also accelerating the market for edge computing, which is expected to reach 1.12 trillion by 2023.<h2>How Does Edge AI Work?</h2>Edge AI is still evolving but has a wide range of potential. It is a technique to proceed with AI algorithms on a hardware device locally without any connection for a device with Edge AI to operate. It enables devices to process data and make decisions independently. Real-time operations like data creation, decision and superfast still hassle-free actions are prime features of Edge AI. Its local data processing avoids the risk of cloud data storage and streaming, thus protecting the privacy of the data.Edge AI provides a new way of performing AI and Machine Learning (ML). Generally, models are trained for a specific task in a typical ML setting. It is programmed to find patterns and evaluate and validate the test dataset with similar properties to the trained dataset model. The cloud infrastructure with computing and data storage resources comes with data transfer challenges and at the cost of latency because of bandwidth limitations.The real-time prediction needs are increasing for smart and sustainable developments. Industry 4.0 trends having Edge AI technology has autonomous systems and smart Internet of Things (IoT) devices. The ML model with Edge AI in the edge device does not need a connection to the outside world. Though the time for training a model on a dataset and deploying it to production remains the same for cloud computing, it reduces the latency cost and maintains data safety.<h2>Importance of Edge AI</h2>As technologies are emerging with new features and advantages, they are accepted based on their role in continuing business safely, smoothly and in a protective environment. Some of the important factors of Edge AI are:<ul><li>Edge AI provides intelligence to sensors. Surveillance cameras to record and store images for hours and use them while necessary. But Edge AI algorithms allow these systems to proceed with real-time data, and detect and process suspicious activities in real-time. It provides inexpensive as well as efficient service to users.</li><li>Edge computing increases the system's real-time capacity to process images and data. For example, the Edge AI system can process traffic signs, road directions, pedestrians, and other vehicles for driverless vehicles and improve secure transportation levels.</li><li>Smartphone users are increasing exponentially. Edge AI solutions incorporated in smartphones allow analyzing the images and videos to generate audiovisual stimuli and detect scenes and spaces.</li><li>Industrial IoT is facing security issues that can be improved inexpensively with Edge AI.</li><li>The network connection speed is increasing with developments in communication technology. 5G technology is hyped but to bring it to beneficial applications; it should have great speed and very low latency in data transmission, which is possible with Edge AI.</li><li>&nbsp;</li></ul><h2>Benefits of Edge AI</h2><ul><li>Privacy: With the increased concerns of users about their private data location, companies are offering AI-enabled personalized features in their applications. It enables users to understand how their data is being collected.</li><li>Security: Cloud data is increasingly becoming sensitive due to distributed architectures. Edge AI nodes in different AI-enabled devices like phones, speakers, robots, and tablets can find suitable security mechanisms for each device.</li><li>Latency: Edge AI is chosen due to reduced latency. Both network-level and device-level services are getting more distributed, and thus latency concerns are increasing while sending data across devices and networks.</li><li>Load balancing: Edge AI is crucial to load balance multiple endpoints to maintain end-to-end application resilience.</li><li>Cost reduction: Edge AI-enabled devices, such as wearable technologies, improve user experience by reducing cost and latency. It interacts in real-time to make payments, monitor sleep patterns, and so forth. Moreover, it reduces the required bandwidth, which leads to a reduction of contracted internet service costs.</li><li>Low maintenance: Edge technology devices have automatic monitoring and do not need specialized care by AI developers or data scientists.</li><li>&nbsp;</li></ul><h2>Potential Application of Edge AI in Various Industries</h2>Though Edge AI technology is still in the development process, it has gained popularity in different sectors. It has potential applications in various industries: autonomous vehicles, smart cities, industrial manufacturing, healthcare, and the financial industry. It is also applicable in AI virtual assistants and augmented reality devices.<ul><li>Autonomous vehicles: Though we are not expecting driverless cars immediately, the automotive industry has already invested billions of dollars in the development of autonomous vehicle technology. These vehicles should operate safely and based on real-time data analysis regarding their directions, surroundings, communication with other vehicles on the road, and weather conditions. Moreover, manufacturers should receive their data regarding usage, maintenance alerts and interface with local municipal networks. New computing solutions need to be adopted by the autonomous industry to avoid interference caused to these vehicles due to the data traffic produced by personal computers, cell phones, and other connected devices. Edge computing enables autonomous vehicles to collect, process, and share data between vehicles as well as to other broad networks in real-time without latency. When driverless vehicles are combined with edge data center networks to collect and relay crucial data to emergency response services, municipalities, and auto manufacturers, they provide unparalleled reliability disabling network infrastructures. &nbsp;</li><li>Smart cities: The development of smart cities is becoming a reality with a massive sensor network collecting data on utility, critical infrastructures, services, and traffic patterns every day. The collected data is stored and analyzed to get meaningful information. It allows city management officials to solve problems faster. There are limitations to conventional cloud solutions as they respond slowly to a multitude of devices in operation outside of the network. On the other hand, edge AI allows regulating device utilities and public services while responding to conditions in near real-time. The integration of edge computing with a connected network of the IoT in smart city infrastructure has the potential to transform the way people live and utilize services in the city. &nbsp;</li><li>Industrial Manufacturing: One of the most benefited industries from the technological revolution is none other than the manufacturing sector. The integration of edge computing and data storage into industrial manufacturing equipment enables manufacturers in energy efficiency, better predictive maintenance, and reduced manufacturing cost with reliability. Edge AI promotes smart manufacturing practices. &nbsp;</li><li>Finance: The importance of customer experience is increasing with growing competition in the marketplace. Edge AI computing integrated with smartphone apps allows banking institutions to better target their services to customers. &nbsp;</li><li>Healthcare: The integration of the healthcare industry with IT solutions has faced various challenges, but edge AI offers new opportunities to healthcare professionals in delivering patient care. To provide appropriate treatment, healthcare professionals should be able to access patients' critical health information in real time.</li></ul>Edge AI-enabled medical devices gather and process patient health data and help throughout the course of treatment and diagnosis. Patients in rural areas are miles away from the nearest healthcare provider. Edge computing solutions collect, store and deliver real-time information remotely. It allows professionals to access vital medical records and process capabilities to recommend treatments.

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