Edge Computing and Edge AI: Pioneering the Future of Intelligent Security

In a rapidly evolving digital landscape, the need for smarter, faster, and more efficient security solutions has never been more significant. Today, bustling city centers are constantly monitored by hundreds of cameras, each capturing vast amounts of video data every second. Traditional systems struggle to process this data quickly, often used only for forensic analysis, leading to delays that could mean unattended security situations, ineffective responses, and potential vulnerabilities. A McKinsey survey revealed that 84% of telecom executives cited video monitoring as the top interest for pursuing edge-computing applications. This statistic underscores a critical industry shift towards technologies that can handle real-time data processing at the source.

Enter Edge Computing and Edge AI—technologies revolutionizing how we approach security and business operations. These innovations reduce latency, enhance performance, and enable immediate, intelligent decision-making by processing data closer to where it is generated.

Understanding Edge Computing and Edge AI

Edge Computing is a distributed computing paradigm that brings computation and data storage closer to the data sources. Instead of sending all data to a centralized server or cloud for processing—which can cause bandwidth and latency issues—edge computing processes data directly at the source, such as within security cameras.

By moving data processing to the «edge» of the network, latency is significantly reduced. This means that security applications can respond more quickly to potential threats. For instance, detecting unauthorized access or suspicious activities is crucial in surveillance systems. Edge Computing enables these real-time responses by eliminating the delays in transmitting data to and from distant servers.

Edge AI combines artificial intelligence with Edge Computing, embedding AI algorithms directly into devices, allowing real-time data analysis on-site without constant connectivity to central servers. In specific applications, transitioning from centralized processing to edge computing signifies the next stage in AI model evolution. While 2023 primarily focused on training foundational AI models, companies are expected to begin large-scale inference on these models in the following years. As low latency becomes a priority for inference in some cases, workloads are expected to move to the edge as businesses start deploying their models commercially.

With AI capabilities embedded at the edge, cameras can analyze video feeds instantaneously, identifying objects, behaviors, or anomalies as they happen. This on-device intelligence enhances responsiveness and allows immediate action—such as triggering alarms, locking doors, or notifying security personnel—without waiting for cloud-based analysis.

Pain Points and Solutions

Traditional security systems face several limitations that hinder their effectiveness in today’s dynamic environments. One of the primary issues is delayed response times. When data from security devices like cameras must be sent to centralized servers for processing, the resulting latency can prevent timely interventions. This delay can be critical, as it may allow security breaches or unauthorized activities to go undetected until too late. Additionally, these systems often incur high operational costs. The need to transmit vast amounts of data over networks requires substantial bandwidth and storage capacity, leading to increased business expenses and limited processing of collected data (in some cases, less than 25% of IoT-generated data). 

Data security risks are another significant concern with conventional setups. Transmitting sensitive information over networks to centralized locations exposes it to potential interception or cyberattacks, leading to data regulations taking center stage worldwide and more than 60 countries implementing data protection localization requirements. This vulnerability can compromise the security system and any transmitted personal or proprietary data. Scalability challenges further compound these issues. Expanding a traditional security system typically demands significant investments in additional infrastructure and can strain existing network resources, making it complex and costly to scale operations as needed.

Edge Computing and Edge AI address these pain points by bringing data processing closer to the source. By enabling immediate processing and action at the device level, they eliminate the delays in sending data to centralized servers. For example, an edge-enabled camera can analyze footage in real time and instantly alert security personnel or trigger automated responses when it detects suspicious activity. This immediacy enhances the overall responsiveness of the security system.

Another advantage of edge technologies is cost efficiency. Processing data locally reduces the need for high-bandwidth networks and extensive cloud storage solutions, lowering operational expenses. Minimizing the transmission of sensitive information across networks improves data security. Since data is processed and stored at the edge, the risk of interception or cyberattacks during transmission is significantly reduced. Enhanced system scalability is also a key benefit. Businesses can easily add more edge devices to their networks without overhauling their entire infrastructure, allowing for flexible and cost-effective growth.

Drivers for Edge AI Adoption in Security

Demand for Real-Time Responses

In security operations, every second counts. Delayed responses can lead to missed opportunities to prevent theft, vandalism, or other security breaches. Edge AI provides the ability to process and act on data instantly, ensuring that security measures are proactive rather than reactive.

Managing Data Volume and Bandwidth

High-definition video surveillance generates enormous amounts of data. Transmitting all this data to a central server for processing can strain network bandwidth and increase costs. Edge Computing reduces this burden by processing data locally and sending only relevant information or alerts over the network, optimizing bandwidth consumption.

Advancements in AI Technology

Recent advancements in AI and machine learning have made deploying sophisticated algorithms on smaller, edge-based devices possible. These technologies directly enable more accurate object recognition, attribute extraction, and behavior analysis within security systems.

The Impact of Edge AI across Industries

Retail

Real-time analysis of shopper behavior can help prevent theft and improve asset management. Edge AI-enabled cameras can gather insights on customer traffic for customized shopping experiences and alert on shopping cart theft, enhancing proactive security.

Public Surveillance

Edge Computing facilitates better public safety measures by monitoring traffic patterns, detecting accidents promptly, and managing emergency responses more effectively. This real-time data processing contributes to reduced congestion and more efficient urban planning.

Manufacturing and Logistics

Benefits obtained from Edge AI include improved equipment monitoring and safety compliance. Real-time analytics at the edge enable predictive maintenance and enhanced worker safety, reducing downtime and increasing operational efficiency.

Next Generation Vision

Hanwha Vision stands at the forefront of this technological transformation. As a vision technology and innovation leader, we are committed to integrating Edge Computing and Edge AI into our security solutions. Our ongoing efforts focus on developing intelligent devices that enhance security and provide valuable business insights across various industries. Learn more at hanwhavision.com