Edge Computing in IoT: What It Is and How to Use It Successfully

Edge computing in the Internet of Things (IoT) refers to the practice of processing and analyzing data at the edge of the network, near the source of data generation, rather than sending it to a centralized cloud or data center. This approach helps to reduce latency, improve real-time decision-making, and decrease bandwidth consumption. To use edge computing successfully in IoT, it is important for organizations to consider several key factors—like having clear objectives and strong security protocols—and to follow proven strategies to maximize benefits like improved efficiency, reduced costs, and enhanced customer experiences.

IoT devices versus edge devices

An IoT device is a physical device or sensor that connects to the internet and collects data. It is typically designed to gather information from the physical world and send it to a centralized location, such as the cloud, for further processing and analysis. IoT devices are often resource-constrained and have limited computing power, memory, and storage. Edge devices are located at the edge of a network and act as intermediaries between IoT devices and the cloud. They can perform local data processing, filtering, and analytics, run applications or algorithms, and have more computational capabilities, storage capacity, and processing power than typical IoT devices.

Since edge computing in IoT brings processing power closer to data sources, the combination enables real-time insights and more efficient operations for organizations. Local processing reduces the time to send data to a distant cloud, crucial for real-time applications like autonomous systems, and sends only relevant information to the cloud, optimizing bandwidth and reducing costs.

Edge computing distributes computational load, reducing dependence on a single cloud entity, and edge devices continue operating in the case of disrupted cloud connections, which provides enhanced reliability. Because it processes sensitive IoT data locally, privacy and security are improved, reducing data breach risks.

Driving forces and benefits

IoT applications like autonomous vehicles and real-time monitoring require immediate decision-making. Edge computing reduces latency, enabling faster response times and real-time analytics while optimizing bandwidth requirements and easing the burden on the network, since no internet connectivity is required. Processing data at the edge also reduces costs associated with transmitting and storing large volumes of data in the cloud, decreasing reliance on high-capacity cloud infrastructure.

Relying on a centralized cloud introduces a single point of failure. Edge computing distributes the computational load across edge devices, servers, and gateways, ensuring uninterrupted functionality. It addresses privacy and security concerns by eliminating transmission of sensitive information over the network, reducing the risk of breaches and ensuring compliance with data protection regulations. Edge computing provides speed and agility in IoT deployments by offering dynamic and distributed computing resources, facilitating scalable IoT applications.

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Determine suitability

Determining whether edge computing is the right approach for an IoT deployment requires careful consideration of various factors such as:

• Analyze data volume, bandwidth considerations, and network infrastructure. Evaluate the volume of data generated and the bandwidth available for transmitting that data to the cloud. Determine if the data volume is high and if transmitting it to the cloud poses challenges in terms of bandwidth limitations, cost, or network congestion.
• Assess latency requirements. Determine if the IoT application demands real-time or near-real-time responses. If the application requires immediate decision-making, faster response times, or low-latency interactions, edge computing can reduce latency by processing data locally.
• Consider reliability and resiliency. Evaluate the criticality of the IoT application and the impact of potential cloud connectivity disruptions to choose devices or gateways that meet the requirements, including power and storage capacity.
• Evaluate data privacy and security needs. Assess the sensitivity of the IoT data and privacy requirements. Implement robust security protocols and measures, including encryption, authentication mechanisms, and secure firmware updates for edge devices.
• Consider scalability and manageability. Confirm the scalability requirements. Verify if the system needs to handle numerous IoT devices and if edge devices can be easily managed, maintained, and scaled. Consider deployment infrastructure, device management frameworks, remote management capabilities, and interoperability standards and protocols.
• Cost-benefits analysis. Conduct a cost-benefit analysis of edge computing versus cloud-based solutions. Evaluate factors such as hardware costs, maintenance expenses, data transmission costs, and potential savings, considering the return on investment (ROI) and total cost of ownership (TCO). A hybrid approach combining edge computing and cloud processing may offer the best of both worlds in certain scenarios, leveraging the strengths of each to achieve optimal performance, efficiency, and cost-effectiveness.

It may also be beneficial to conduct proof-of-concept experiments or engage with experienced consultants to validate the feasibility and benefits of edge computing for the particular use case.

Development and integration

To develop and integrate an effective edge computing program, clearly define the objectives and use cases by identifying the specific problems or challenges that edge computing can address, outlining the expected outcomes, and determining suitability. Prioritize data privacy and security and adhere to data governance practices, ensuring compliance with regulations regarding data protection, storage, and usage.

Design for scalability, flexibility, management, and monitoring when developing or adapting applications. Deploy effective tools like remote device management, performance monitoring, and real-time analytics to identify and address potential issues; implementing proactive alerting and troubleshooting mechanisms will maintain the overall system performance and reliability. Foster collaboration among stakeholders, including IT teams, operations, and domain experts, and encourage knowledge sharing of lessons learned and best practices across teams to drive continuous improvement.

Mitigating challenges and limitations

Edge devices often have limited computing power, memory, and storage compared to cloud servers. This can pose challenges when performing resource-intensive tasks or running complex algorithms, but optimizing algorithms and leveraging efficient data processing techniques resolve this limitation. As the number of IoT devices and data volume increases, scaling edge computing deployments may require the management of many devices, ensuring their synchronization, and maintaining the overall system performance. Adopting efficient device management frameworks, scalable architectures, and edge orchestration solutions can help address scalability challenges.

Ensuring software updates, security patches, and device health monitoring can be complex because edge devices are typically distributed across various locations. Implement robust solutions like over-the-air updates to streamline management and maintenance processes. Deploying and managing edge devices can introduce additional costs and complexities compared to cloud-based solutions, so adopting cost-effective hardware options, using efficient resources, and implementing proper cost management strategies are essential.

Edge devices, being more distributed and closer to the physical world, may face increased security risks, so balancing the need for real-time processing with ensuring data privacy rights and compliance with regulations remains a challenge. Utilizing encryption, secure communication protocols, and authentication mechanisms can help mitigate security risks. In hybrid architectures, the complexity of ensuring seamless integration, data flow, and interoperability between edge devices and cloud platforms can be mitigated by adopting standardized protocols and leveraging edge-to-cloud integration frameworks.

Emerging trends and technologies

The rollout of 5G networks will revolutionize edge computing and IoT by offering significantly faster data transfer speeds, lower latency, and higher device density. This enables real-time applications and supports the proliferation of connected devices. Edge artificial intelligence (AI) and machine learning (ML) integration are gaining traction as edge devices are becoming more powerful, allowing for on-device AI decision-making. These technologies also allow for real-time analytics, faster insights, and reduced dependency on cloud processing.

Federated learning is an approach where ML models are trained collaboratively across distributed edge devices without centralized data aggregation, preserving privacy and allowing edge devices to learn and improve models while keeping data locally. Edge-as-a-Service (EaaS) models are emerging, providing organizations with cloud-like capabilities at the edge. EaaS offerings provide leverage with edge computing resources, such as processing power and storage, without the need for upfront infrastructure investments.

Digital twin technology, where virtual representations of physical assets or systems are created, enables real-time monitoring, simulation, and predictive analysis of physical assets, improving performance, maintenance, and operational efficiency. Organizations can leverage digital twin at the edge to optimize asset management and decision-making.

The real-time reporting provided by edge computing is a critical factor that sets apart competitors in various industries. General Electric implemented edge devices within its turbines, allowing for quicker responses to changing wind conditions and improving the efficiency and reliability of renewable energy generation. Many companies use edge computing in their autonomous vehicles and healthcare providers include it in patient monitoring devices, enabling faster response times and early detection of deteriorating conditions. Edge computing has spread throughout other industries like manufacturing, retail, and agriculture, and its capabilities are ever expanding. Companies that implement edge computing can gain a competitive advantage in a hyperconnected world.

About the Author

Amrit Bhatia is a seasoned IT professional with extensive experience in IT and program management. She has held positions at renowned multinational companies, including Amazon Web Services (AWS), HERE Technologies, Grainger, GE-GENPACT, and IBM. Currently, she is contributing her expertise to the development of cutting-edge cloud computing and IoT solutions. Amrit holds a master’s degree from Keller Graduate School of Management in Chicago, IL. For more information, contact Amrit at amrit.bhatia336@gmail.com or through LinkedIn.