Powering Smarter Operations in Buildings
with IoT, Edge AI and Private Networks

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The work-from-home era during the pandemic revealed some interesting trends for businesses—most notably, the reality that maintaining buildings, campuses and facilities is expensive. These building-related expenses often rank among the top cost categories for organizations, which include anything from cleaning and repairs to staffing and security, as well as energy and lease payments. After the lockdowns, a handful of companies became fully remote, abandoning physical offices entirely to eliminate these expenses. However, most organizations do not have that same luxury and must find creative ways to reduce building-related expenses. Besides cutting costs, other challenges abound because of legacy systems and manual processes inside factories, warehouses, schools, office campuses and other facilities. These challenges range from energy waste and equipment downtimes to poor indoor air quality and rising security threats.

Recognizing these challenges, many building managers are looking to transform their facilities and places of work into smart buildings or structures with automated functions based on owner or manager specifications. At the heart of a smart building are technologies like the Internet of Things (IoT), edge artificial intelligence (AI) and private connectivity solutions. Smart infrastructure redefines how companies manage and optimize physical environments, boosting efficiency and providing cost savings. These technologies also allow organizations to digitize routine processes, automate critical systems and generate real-time insights that drive down operational costs while improving safety, comfort and resource use. This article will explore the three technologies driving the smart building revolution and some of the most notable use cases they enable.

The 3 Core Technologies of Smart Buildings: IoT, Edge AI and Private Networks

In a smart building, connected IoT sensors, devices and systems monitor and manage building operations, such as lighting, HVAC, security and energy usage. These IoT devices collect real-time data, enabling automation and more efficient decision-making. Users can automate and simplify time-consuming tasks using IoT-connected technologies; these tasks can get scheduled in advance, remotely adjusted or rescheduled, and monitored via related technologies. While IoT is certainly the most well-known and prevalent technology within smart buildings, others work in conjunction with it, allowing managers to achieve even more impressive results.

Microcontroller-based edge AI, for example, is empowering smart buildings to get even smarter. Edge AI refers to the shift of AI to devices (like sensors and IoT systems) at the network’s edge. Moving AI to the edge enables local data processing; for example, a facial recognition camera using AI and machine learning algorithms at the edge will perform data compilation locally on the device rather than in a remote server. Other examples of edge AI include sensor analysis, sensor fusion and event detection via audio. Analyzing data closer to its source eliminates the need to send large amounts of data to a cloud server. As a result, edge AI enables local decisions and faster responses while consuming significantly less power and requiring less runtime than a traditional microcontroller. Moreover, innovative building applications that use edge AI can still react to environmental changes even if communications are down.

The other noteworthy technology trend supporting smart building use cases is private networks—more specifically, 5G standardization, which allows companies to deploy dedicated cellular resources. A private network is a dedicated, wireless network separate from the wide-area macro network. This separation from public infrastructure enables greater data security and control, which is especially helpful for large smart buildings or campuses that must manage hundreds or thousands of IoT devices. Private networks offer greater resilience and flexibility, including higher bandwidth connectivity and data throughput, than public networks or infrastructure. Likewise, private networks are customizable, meaning building managers can tailor them to meet specific latency, coverage and reliability requirements.

Improving Energy and Resource Conservation

IoT, edge AI and private networks are crucial in helping businesses use energy and other resources more efficiently. For example, heating and cooling are responsible for a substantial proportion of total building energy consumption, thus accounting significantly toward annual electricity bills. Managers can optimize HVAC to reduce their electric bill by using IoT sensors and thermostats connected to a private network and supported by edge AI for rapid, automated decision-making. For example, IoT sensors working with edge AI can automatically adjust heating, cooling, and ventilation based on actual demand rather than fixed schedules, such as changing temperatures in unoccupied zones or pre-cooling areas during off-peak hours when electricity rates are lower. Similarly, IoT and edge AI-powered occupancy management systems can