Edge Computing Market Trends

• Rising number of smart city projects: Rapid expansion of smart city projects to cater to the increasing urbanization across the globe is expected to support market growth in the coming years. According to the United Nations survey, around 55% of the global population is currently living in urban areas and is expected to reach 68% by the end of 2050. This can result in massive deployment of IoT devices such as surveillance cameras, smart meters, and traffic sensors. The demand for edge computing products and services is expected to increase due to the rising need for real-time data processing in these smart city projects. According to a report, over 15 billion connected devices are currently active in urban environments, and the number is expected to rise to 30 billion by 2030.
   
• High usage of edge computing in the healthcare sector: Edge computing is significantly revolutionizing the healthcare sector by enabling real-time data processing and enhancing patient care and operational efficiency. Some of the key applications of edge computing in healthcare include remote patient monitoring, telemedicine, medical imaging, along with resource management and staff scheduling. In August 2024, Caregility Corporation announced the launch of the first-ever edge-based computer vision AI, iObserver, with fall risk detection capability. The product uses computer vision to analyze information, detect fall risks, and alert caregivers immediately. Moreover, it offers a cost-effective approach and scalable solution catering to the need for advanced solutions in the healthcare fraternity.

Challenges

• High initial investments and security challenges: Deploying edge infrastructure such as edge servers, AI chips, and sensors requires substantial capital investments. However, this can lead to its lower adoption, especially in small and medium-sized enterprises with limited investment cap. In addition, most of the dispersed edge nodes are vulnerable to physical tampering, data breaches, and cyberattacks. This can restrain organizations with sensitive data from adopting these solutions.