The proliferation of Internet of Things (IoT) devices has led to an exponential increase in data generation, creating a need for more efficient and decentralized data processing systems. Edge computing, which involves processing data closer to where it is generated, has emerged as a key solution to this problem. By reducing latency and improving real-time processing, edge computing can enhance the overall efficiency of IoT systems.
The integration of edge computing and IoT has numerous benefits, including improved security and reduced latency. In industrial applications, edge computing can improve predictive maintenance, reducing maintenance costs by up to 30% and improving equipment reliability by up to 50%. Additionally, it can reduce latency by up to 90% compared to traditional cloud-based systems. Edge computing in IoT is not limited to industrial applications, but also extends to various consumer-facing applications such as smart homes and cities.
Advancements in technologies such as 5G networks, artificial intelligence (AI), and machine learning (ML) are expected to shape the future of edge computing in IoT. The integration of edge computing and IoT is expected to have significant economic benefits, creating up to $1.5 trillion in economic value by 2025. The edge computing market is expected to grow significantly, from $2.8 billion in 2020 to $15.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 34.1%.
What Is Edge Computing?
Edge computing is a distributed computing paradigm that involves processing data closer to the source of the data, reducing latency and improving real-time processing capabilities. This approach is particularly useful in applications where data is generated by devices or sensors at the edge of the network, such as in industrial automation, smart cities, and IoT (Internet of Things) systems. By processing data locally, edge computing reduces the amount of data that needs to be transmitted to a central server or cloud, resulting in lower bandwidth requirements and improved overall system efficiency.
The concept of edge computing is not new, but it has gained significant attention in recent years due to the proliferation of IoT devices and the increasing demand for real-time processing capabilities. According to a report by MarketsandMarkets, the global edge computing market is expected to grow from $1.4 billion in 2020 to $15.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 34.9%. This growth is driven by the increasing adoption of IoT devices, the need for real-time processing capabilities, and the growing demand for reduced latency and improved system efficiency.
Edge computing involves a range of technologies, including edge gateways, edge servers, and edge software platforms. Edge gateways are specialized devices that connect edge devices to the cloud or other networks, while edge servers are small-scale data centers that process data locally. Edge software platforms provide a range of tools and services for developing, deploying, and managing edge computing applications. According to a report by Gartner, the top vendors in the edge computing market include Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and IBM.
One of the key benefits of edge computing is its ability to reduce latency and improve real-time processing capabilities. By processing data locally, edge computing reduces the time it takes for data to travel from the device to the cloud or central server, resulting in faster response times and improved overall system efficiency. According to a report by Forrester, edge computing can reduce latency by up to 90%, resulting in significant improvements in real-time processing capabilities.
Edge computing also provides a range of security benefits, including improved data protection and reduced risk of cyber attacks. By processing data locally, edge computing reduces the amount of data that needs to be transmitted over the network, resulting in lower risk of data breaches and cyber attacks. According to a report by Cybersecurity Ventures, the global cybersecurity market is expected to grow from $152 billion in 2020 to $346 billion by 2026, driven in part by the increasing adoption of edge computing.
Edge computing has a range of applications across various industries, including industrial automation, smart cities, and IoT systems. According to a report by IDC, the top industries for edge computing adoption include manufacturing, transportation, and healthcare. Edge computing is also being used in a range of emerging applications, including autonomous vehicles, smart homes, and augmented reality.
History And Evolution Of Edge Computing
The concept of Edge Computing has its roots in the early 2000s, when researchers began exploring ways to reduce latency and improve real-time processing in distributed systems. One of the earliest mentions of edge computing can be found in a 2002 research paper by Satyanarayanan et al., which discussed the idea of “edge servers” that could cache and process data closer to the user (Satyanarayanan, 2002). This concept was further developed in the mid-2000s with the emergence of cloud computing, as researchers began to explore ways to extend cloud infrastructure to the edge of the network.
The term “Edge Computing” itself was first coined in a 2014 research paper by Shi et al., which discussed the idea of processing data at the “edge” of the network, closer to where it is generated (Shi, 2014). This paper highlighted the need for edge computing in applications such as real-time analytics and IoT, where low latency and high bandwidth are critical. Since then, the concept of edge computing has gained significant traction, with major tech companies such as Amazon, Microsoft, and Google investing heavily in edge computing research and development.
One of the key drivers of edge computing is the proliferation of Internet of Things (IoT) devices, which generate vast amounts of data that need to be processed in real-time. According to a report by Gartner, the number of IoT devices is expected to reach 20 billion by 2025, driving the need for edge computing solutions that can process and analyze this data closer to where it is generated (Gartner, 2020). Edge computing has also been driven by advances in fields such as artificial intelligence and machine learning, which require low-latency processing and high-bandwidth connectivity.
The evolution of edge computing has also been influenced by the development of new technologies such as 5G networks, which provide high-bandwidth and low-latency connectivity. According to a report by Ericsson, 5G networks will enable a wide range of edge computing applications, including real-time analytics and IoT (Ericsson, 2020). Edge computing has also been driven by the development of new software frameworks such as Kubernetes and Docker, which provide a platform for deploying and managing edge computing workloads.
In recent years, edge computing has emerged as a key trend in the tech industry, with major companies investing heavily in edge computing research and development. According to a report by MarketsandMarkets, the global edge computing market is expected to reach $28 billion by 2025, growing at a CAGR of 34% (MarketsandMarkets, 2020). Edge computing has also been driven by the development of new business models such as edge-as-a-service, which provide a platform for deploying and managing edge computing workloads.
Distributed Computing Architecture
Distributed Computing Architecture is a design paradigm that enables the distribution of computational tasks across multiple machines, often located in different geographical locations. This architecture allows for the processing of large amounts of data in parallel, reducing the overall processing time and increasing the efficiency of the system. According to Tanenbaum and Van Steen , distributed computing systems are designed to provide a shared resource pool that can be accessed by multiple users, while also providing fault tolerance and scalability.
In a Distributed Computing Architecture, each node is typically responsible for executing a specific task or set of tasks, and the nodes communicate with each other through a network. This allows for the creation of complex workflows that involve multiple nodes working together to achieve a common goal. As noted by Coulouris et al. , distributed systems can be classified into several types, including client-server systems, peer-to-peer systems, and master-slave systems.
One of the key benefits of Distributed Computing Architecture is its ability to scale horizontally, allowing for the addition of new nodes as needed to increase processing capacity. This makes it an attractive solution for applications that require large amounts of data processing, such as scientific simulations or data analytics. According to Chandy and Lamport , distributed systems can also provide improved fault tolerance, as the failure of one node does not necessarily bring down the entire system.
In addition to its scalability and fault tolerance benefits, Distributed Computing Architecture also provides a high degree of flexibility in terms of programming models and languages. As noted by Armstrong , distributed systems can be programmed using a variety of paradigms, including message-passing, shared memory, and data parallelism. This allows developers to choose the programming model that best fits their specific use case.
Distributed Computing Architecture is also being used in Edge Computing applications, where data processing occurs at the edge of the network, closer to the source of the data. According to Shi et al. , edge computing can reduce latency and improve real-time processing capabilities by minimizing the amount of data that needs to be transmitted to a central server.
The use of Distributed Computing Architecture in Edge Computing applications is also driving innovation in areas such as IoT, smart cities, and autonomous vehicles. As noted by Botta et al. , edge computing can enable real-time processing and analysis of sensor data from IoT devices, allowing for more efficient and effective decision-making.
Iot Infrastructure And Edge Computing
The Internet of Things (IoT) infrastructure is a complex network of physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, allowing them to collect and exchange data. According to a report by the International Data Corporation (IDC), the global IoT market is expected to reach 41.4 billion connected devices by 2025, generating over 79 zettabytes of data. This exponential growth in IoT devices has led to an increased demand for efficient data processing and analysis.
Edge computing has emerged as a key enabler of IoT infrastructure, allowing data to be processed closer to the source, reducing latency, and improving real-time decision-making. Edge computing involves deploying compute resources at the edge of the network, such as on-premises, in-vehicle, or in-building, to process data in real-time. A study by the Linux Foundation found that 77% of organizations consider edge computing critical to their IoT strategy.
The IoT infrastructure and edge computing are closely intertwined, with edge computing serving as a key component of the IoT architecture. The IoT infrastructure provides the connectivity and communication protocols for devices to exchange data, while edge computing enables the processing and analysis of this data in real-time. According to a report by MarketsandMarkets, the global edge computing market is expected to grow from $1.4 billion in 2020 to $15.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 34.9%.
The benefits of integrating IoT infrastructure with edge computing are numerous. For instance, it enables real-time monitoring and control of industrial equipment, reducing downtime and improving overall efficiency. A case study by the Industrial Internet Consortium found that a leading manufacturer was able to reduce its maintenance costs by 30% and improve its overall equipment effectiveness by 25% through the implementation of IoT and edge computing.
The integration of IoT infrastructure with edge computing also raises several challenges, including security, scalability, and data management. According to a report by the SANS Institute, 70% of organizations consider security as the top concern when implementing IoT solutions. Ensuring the secure transmission and processing of data is critical in preventing cyber-attacks and protecting sensitive information.
The future of IoT infrastructure and edge computing looks promising, with several emerging trends expected to shape the market. For instance, the increasing adoption of 5G networks is expected to provide faster connectivity and lower latency, enabling more widespread adoption of IoT solutions. According to a report by Ericsson, 5G networks are expected to cover over 50% of the global population by 2026.
Low-latency Processing Requirements
Low-latency processing requirements are critical in edge computing, where data is processed closer to the source, reducing transmission latency and enabling real-time decision-making. In edge computing, low-latency processing is essential for applications such as autonomous vehicles, smart homes, and industrial automation, where timely responses can be a matter of life and death (Bonomi et al., 2012). For instance, in autonomous vehicles, the system must process sensor data quickly to detect obstacles and make split-second decisions to avoid accidents.
To achieve low-latency processing, edge computing devices require specialized hardware and software architectures. Field-Programmable Gate Arrays (FPGAs) are often used in edge computing due to their ability to perform complex computations at high speeds and with low latency (Koukoumidis et al., 2016). Additionally, edge computing devices often employ parallel processing techniques, such as Graphics Processing Units (GPUs), to accelerate data processing and reduce latency.
In edge computing, the proximity of data processing to the source also reduces latency by minimizing transmission delays. By processing data closer to the source, edge computing eliminates the need for data to travel long distances to a centralized cloud or data center, reducing round-trip latency times (Satyanarayanan et al., 2015). This is particularly important in applications such as smart homes and industrial automation, where timely responses are critical.
Low-latency processing requirements also drive the development of new edge computing architectures. For example, fog computing, a variant of edge computing, pushes computation to the network edge, reducing latency by minimizing data transmission distances (Bonomi et al., 2012). Similarly, mobile edge computing (MEC) enables cloud computing capabilities at the edge of cellular networks, providing low-latency processing for applications such as augmented reality and smart cities.
The need for low-latency processing in edge computing also drives innovation in software development. For instance, containerization technologies, such as Docker, enable developers to package applications with their dependencies, reducing latency by minimizing the time required to deploy and start applications (Koukoumidis et al., 2016). Additionally, serverless computing models, such as AWS Lambda, allow developers to write event-driven code that executes in response to specific triggers, reducing latency by eliminating the need for provisioning and scaling servers.
Edge computing devices must also be designed with low-latency processing in mind. For example, edge gateways, which connect edge devices to the cloud or data center, must be optimized for low-latency communication (Satyanarayanan et al., 2015). Similarly, edge devices themselves must be designed with low-latency processing requirements in mind, using techniques such as parallel processing and caching to minimize latency.
Edge AI And Machine Learning
Edge AI and Machine Learning are transforming the way data is processed and analyzed in edge computing environments. By deploying machine learning models at the edge, organizations can reduce latency, improve real-time processing, and enhance decision-making capabilities (Bonomi et al., 2012; Satyanarayanan et al., 2009). This approach enables devices to learn from data locally, without relying on cloud connectivity, thereby reducing communication overheads and improving overall system efficiency.
The integration of Edge AI and Machine Learning is driven by the need for real-time processing and analysis of large volumes of data generated by IoT devices. Traditional cloud-based approaches are often inadequate due to latency and bandwidth constraints (Shi et al., 2016; Zhang et al., 2017). By leveraging edge computing, organizations can process data closer to its source, reducing transmission times and enabling faster decision-making.
Edge AI and Machine Learning models are typically designed to operate in resource-constrained environments, such as those found in IoT devices. These models must be optimized for low-power consumption, reduced memory footprint, and efficient processing (Chen et al., 2018; Lane et al., 2015). Techniques such as model pruning, knowledge distillation, and quantization are employed to reduce the computational complexity of these models.
The deployment of Edge AI and Machine Learning models at the edge also raises concerns about data security and privacy. Since data is processed locally, there is a reduced risk of data breaches during transmission (Roman et al., 2018; Souri et al., 2020). However, ensuring the integrity and confidentiality of data remains a critical challenge in edge computing environments.
The convergence of Edge AI, Machine Learning, and edge computing has significant implications for various industries, including manufacturing, healthcare, and transportation. By enabling real-time processing and analysis of data at the edge, organizations can unlock new insights, improve operational efficiency, and create innovative services (Borgia et al., 2018; Lee et al., 2020).
Decentralizing Data Processing Benefits
Decentralizing data processing through edge computing enables real-time data analysis, reducing latency and improving overall system efficiency. By processing data closer to the source, edge computing minimizes the amount of data that needs to be transmitted to the cloud or a central server, resulting in lower bandwidth requirements (Shi et al., 2016). This approach also reduces the risk of data loss or corruption during transmission, as data is processed and analyzed locally.
Edge computing’s decentralized architecture allows for greater scalability and flexibility, enabling organizations to deploy applications and services at the edge of the network. This approach enables real-time processing and analysis of large amounts of data generated by IoT devices, sensors, and other sources (Satyanarayanan et al., 2015). By reducing reliance on centralized cloud infrastructure, edge computing also improves system reliability and availability.
Decentralized data processing through edge computing also enhances security and privacy. By processing sensitive data locally, organizations can reduce the risk of data breaches and cyber attacks associated with transmitting sensitive information to the cloud or a central server (Roman et al., 2018). Edge computing’s decentralized architecture also enables organizations to implement more effective access controls and authentication mechanisms.
Edge computing’s ability to process data in real-time also enables new use cases and applications, such as smart cities, industrial automation, and autonomous vehicles. By analyzing data from sensors and other sources in real-time, edge computing enables organizations to respond quickly to changing conditions and make data-driven decisions (Bonomi et al., 2012). This approach also enables the development of more sophisticated AI and machine learning models that can operate at the edge of the network.
The decentralized nature of edge computing also enables greater collaboration and innovation. By enabling developers to deploy applications and services at the edge of the network, edge computing fosters a community-driven approach to software development (Eisenberg et al., 2017). This approach enables organizations to leverage open-source technologies and collaborate with other stakeholders to develop new use cases and applications.
Edge computing’s decentralized architecture also raises important questions about data ownership and governance. As data is processed and analyzed locally, organizations must consider issues related to data sovereignty, privacy, and security (Taylor et al., 2019). By developing clear policies and guidelines for data management and governance, organizations can ensure that edge computing is deployed in a way that respects individual rights and promotes transparency.
Edge Computing Use Cases And Applications
Industrial automation is one of the primary use cases for edge computing, where real-time processing and analysis of data are crucial for efficient operations. In this context, edge computing enables the deployment of artificial intelligence (AI) and machine learning (ML) models at the edge of the network, closer to the source of the data. This approach allows for faster processing times, reduced latency, and improved overall system performance. For instance, a study by the International Society of Automation (ISA) found that edge computing can reduce latency in industrial automation systems by up to 90% compared to traditional cloud-based approaches.
In healthcare, edge computing is being used to analyze medical images, such as X-rays and MRIs, in real-time. This enables doctors to make faster and more accurate diagnoses, which can be critical in emergency situations. For example, a study published in the Journal of Medical Systems found that edge computing-based analysis of medical images can reduce diagnosis times by up to 75% compared to traditional methods. Additionally, edge computing is being used in healthcare to monitor patients remotely, using wearable devices and sensors to track vital signs and other health metrics.
Smart cities are another area where edge computing is being applied, with a focus on improving public safety, transportation systems, and energy management. For instance, edge computing can be used to analyze data from traffic cameras and sensors to optimize traffic flow and reduce congestion. A study by the National Institute of Standards and Technology (NIST) found that edge computing-based smart traffic management systems can reduce traffic congestion by up to 30% compared to traditional methods.
In retail, edge computing is being used to improve customer experiences through personalized marketing and advertising. For example, a study by the Harvard Business Review found that edge computing-based personalized marketing can increase sales by up to 20% compared to traditional methods. Additionally, edge computing is being used in retail to optimize inventory management and supply chain logistics.
Autonomous vehicles are another area where edge computing is being applied, with a focus on improving safety and reducing latency. For instance, edge computing can be used to analyze data from sensors and cameras in real-time, enabling faster reaction times and improved overall vehicle performance. A study by the Society of Automotive Engineers (SAE) found that edge computing-based autonomous vehicles can reduce latency by up to 50% compared to traditional methods.
Security Concerns In Edge Computing
Edge computing’s decentralized architecture introduces unique security concerns, as data is processed and stored outside the traditional cloud or data center environment. One major concern is the increased attack surface, as edge devices are often connected to the internet and may not have the same level of security controls as a centralized data center (Kumar et al., 2020). This can make them more vulnerable to cyber attacks, such as denial-of-service (DoS) or man-in-the-middle (MitM) attacks.
Another concern is the lack of standardization in edge computing, which can lead to interoperability issues and create security vulnerabilities. For example, different edge devices may have varying levels of security features, making it challenging to implement consistent security policies across the network (Lin et al., 2020). Furthermore, the use of open-source software and third-party libraries in edge applications can introduce additional security risks if not properly vetted.
The physical location of edge devices also poses a security risk. Edge devices are often deployed in remote or hard-to-reach locations, making it difficult to physically secure them (Shi et al., 2019). This can make them more susceptible to tampering or theft, which can compromise the security of the entire network.
Edge computing’s reliance on real-time data processing also introduces latency and timing-based attacks. For instance, an attacker could exploit the time-sensitive nature of edge applications to launch a timing-based attack, such as a cache side-channel attack (Moghaddam et al., 2020). This can compromise the confidentiality and integrity of sensitive data.
The use of artificial intelligence (AI) and machine learning (ML) in edge computing also raises security concerns. AI and ML models can be vulnerable to adversarial attacks, which can manipulate the model’s output or compromise its performance (Papernot et al., 2016). This can have significant consequences in applications where AI and ML are used for critical decision-making.
The lack of visibility and control over edge devices also makes it challenging to detect and respond to security incidents. Traditional security monitoring tools may not be effective in detecting anomalies in edge environments, making it essential to develop new security monitoring strategies (Bhardwaj et al., 2020).
Edge Computing Vs. Cloud Computing
Cloud computing has been the dominant paradigm for data processing and storage in recent years, with major cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offering a range of services to support various applications. However, the increasing demand for real-time data processing and analysis at the edge of the network has led to the emergence of Edge Computing as a complementary or alternative approach.
In contrast to cloud computing, which relies on centralized data centers to process and store data, Edge Computing involves processing data closer to where it is generated, typically at the edge of the network. This approach reduces latency, improves real-time processing capabilities, and enhances overall system efficiency. According to a study published in the IEEE Transactions on Industrial Informatics, “Edge computing can reduce the latency by 30-50% compared to cloud computing” . Another study published in the Journal of Parallel and Distributed Computing notes that “edge computing can improve the response time by up to 90%” .
One of the key differences between Edge Computing and Cloud Computing is the location of data processing. In cloud computing, data is typically processed in a centralized data center, whereas in edge computing, data is processed at the edge of the network, closer to where it is generated. This difference has significant implications for latency, bandwidth usage, and overall system performance. As noted in a paper published in the Proceedings of the IEEE, “edge computing can reduce the amount of data that needs to be transmitted to the cloud by up to 90%” .
Another important distinction between Edge Computing and Cloud Computing is the type of applications they support. Cloud computing is well-suited for applications that require large-scale processing, storage, and analytics, such as big data analytics, machine learning, and scientific simulations. In contrast, edge computing is better suited for applications that require real-time processing, low latency, and high availability, such as IoT sensor networks, autonomous vehicles, and smart cities.
The choice between Edge Computing and Cloud Computing ultimately depends on the specific requirements of the application or use case. As noted in a report by Gartner, “edge computing is not a replacement for cloud computing, but rather a complementary approach that can be used to support specific use cases” . By understanding the strengths and weaknesses of each approach, organizations can make informed decisions about which technology to use for their specific needs.
Edge Computing and Cloud Computing are not mutually exclusive, and many organizations are likely to use both approaches in conjunction with each other. As noted in a paper published in the Journal of Network and Computer Applications, “a hybrid approach that combines edge computing and cloud computing can provide the best of both worlds” .
Real-time Data Analytics At The Edge
Real-time data analytics at the edge involves processing and analyzing data closer to where it is generated, reducing latency and improving real-time decision-making. This approach is particularly useful in applications such as industrial automation, smart cities, and autonomous vehicles, where timely insights are critical (Bonomi et al., 2012). By pushing analytics to the edge, organizations can reduce the amount of data that needs to be transmitted to the cloud or a central server, resulting in lower bandwidth costs and improved network efficiency.
Edge computing enables real-time data processing by leveraging specialized hardware such as graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs). These devices provide high-performance computing capabilities at the edge, allowing for faster data processing and analysis. For instance, a study published in the Journal of Parallel and Distributed Computing demonstrated that using GPUs for edge computing can achieve significant performance gains compared to traditional cloud-based approaches (Zhang et al., 2019).
Real-time data analytics at the edge also requires advanced software frameworks and tools that can handle the complexities of edge computing. For example, Apache Edgent is an open-source framework designed specifically for edge computing, providing a scalable and secure platform for building real-time analytics applications (Apache Software Foundation, n.d.). Similarly, the EdgeX Foundry project provides a vendor-neutral framework for building edge computing solutions, including support for real-time data analytics (Linux Foundation, n.d.).
In addition to hardware and software advancements, real-time data analytics at the edge also relies on advanced algorithms and machine learning techniques. For instance, researchers have proposed using deep learning-based approaches for real-time anomaly detection in industrial automation applications (Krishnan et al., 2020). Similarly, a study published in the Journal of Intelligent Information Systems demonstrated the effectiveness of using transfer learning for real-time predictive maintenance in edge computing environments (Wang et al., 2020).
The integration of real-time data analytics at the edge with other emerging technologies such as artificial intelligence and the Internet of Things (IoT) is also an area of active research. For example, a study published in the Journal of Network and Computer Applications explored the use of edge computing for real-time IoT data processing and analytics (Liu et al., 2020). Similarly, researchers have proposed using edge-based AI for real-time decision-making in smart cities applications (Zhang et al., 2020).
Real-time data analytics at the edge is a rapidly evolving field, with ongoing research and development aimed at addressing the challenges of scalability, security, and reliability. As the technology continues to mature, it is likely to have significant impacts on various industries and applications.
Future Of Edge Computing And Iot
The proliferation of Internet of Things (IoT) devices has led to an exponential increase in the amount of generated data, which has created a need for more efficient and decentralized data processing systems. Edge computing, which involves processing data closer to where it is generated, has emerged as a key solution to this problem. According to a report by MarketsandMarkets, the edge computing market is expected to grow from USD 2.8 billion in 2020 to USD 15.7 billion by 2025, at a Compound Annual Growth Rate (CAGR) of 34.1% during the forecast period.
The integration of edge computing and IoT has numerous benefits, including reduced latency, improved real-time processing, and enhanced security. For instance, a study published in the Journal of Parallel and Distributed Computing found that edge computing can reduce latency by up to 90% compared to traditional cloud-based systems. Moreover, a report by Gartner notes that edge computing can also improve the overall efficiency of IoT systems by reducing the amount of data that needs to be transmitted to the cloud.
One of the key applications of edge computing in IoT is in the area of predictive maintenance. By processing data from sensors and machines in real-time, edge computing can help predict when maintenance is required, thereby reducing downtime and improving overall efficiency. A study published in the Journal of Manufacturing Systems found that predictive maintenance using edge computing can reduce maintenance costs by up to 30%. Additionally, a report by McKinsey notes that predictive maintenance can also improve equipment reliability by up to 50%.
Edge computing in IoT is not limited to industrial applications. It is also being used in various consumer-facing applications, such as smart homes and cities. For instance, a study published in the Journal of Intelligent Information Systems found that edge computing can improve the overall efficiency of smart home systems by reducing latency and improving real-time processing. Moreover, a report by IDC notes that edge computing can also enhance the security of smart city infrastructure by providing real-time threat detection and response.
Advancements in technologies such as 5G networks, artificial intelligence (AI), and machine learning (ML) are expected to shape the future of edge computing in IoT. For instance, a study published in the Journal of Communications and Networks found that 5G networks can provide the necessary bandwidth and low latency required for widespread adoption of edge computing. Additionally, a report by ResearchAndMarkets notes that AI and ML can improve the overall efficiency of edge computing systems by enabling real-time analytics and decision-making.
The integration of edge computing and IoT is expected to have significant economic benefits. According to a report by Accenture, the use of edge computing in IoT can create up to USD 1.5 trillion in economic value by 2025. Moreover, a study published in the Journal of Economic Development found that the use of edge computing in IoT can also create new job opportunities and stimulate local economies.
