Connecting buildings via the Internet of Things
The Internet of Things offers engineers ripe opportunities to take the lead with clients.
The Internet of Things (IoT) is not aspirational technology. It’s here. It’s not a question of “if,” but of “how much” and “how fast.” And what it means to consulting engineers day to day. The answer could be: “A lot.”
The reason is that IoT is taking by storm many of the technologies engineers include when designing, specifying, and building projects: building and industrial automation systems, backup power, lighting, asset management, and testing and measurement.
A number of prestigious organizations already have weighed in on IoT:
- Gartner projects there will be about 25 billion connected devices by the end of this decade
- McKinsey Global Institute has reported that IoT could potentially generate an economic impact of $2.7 trillion to $6.2 trillion annually by 2025
- International Data Corp. (IDC) estimated that organizations spent $113 billion worldwide in 2013 on relevant information management, access, and analysis technologies and services.
The industrial version of IoT (IIoT) makes a good business case for consulting engineering clients. It also is good business for engineering firms to be their clients’ IIoT go-to source of expertise.
Specifying, designing, and building IIoT capabilities require skillsets already offered by engineering firms: deep understanding of facility spaces and the knowledge to connect legacy systems with new technology. That makes IIoT a low-hanging fruit. The opportunities to enable clients to achieve higher efficiencies and reliability by better managing, controlling, maintaining, diagnosing issues, and optimizing their facilities are ripe.
The overall business case for consulting engineers lies in the compelling insights produced when big data are analyzed quickly. Such insights empower clients to know what’s happening 24/7. Call it heightened situational awareness or contextual insight in real time.
It means operators have a much better understanding of system operation. They know the system can automatically self-correct a range of out-of-parameter operating conditions as it gathers and analyzes data. When it can’t, operators diagnose and act.
But how can they digest so much information when viewing, analyzing, and interpreting data quickly are essential? In a phrase, dynamic visualization, provided it’s supported by software that aggregates and analyzes data. Effectively visualizing information makes data more predictable, enhances productivity, and averts issues.
The ability to analyze, diagnose, and act quickly, from both an operator and a machine perspective, improves asset management. Operational efficiency, reliability, and predictive maintenance all benefit. Risk drops and costs fall. Critical power management systems are a case in point.
Specifying, designing, and building IIoT capabilities into critical power management systems need to accommodate such requirements as power demand, integration, lifecycle value, and security considerations.
Improving efficiency and reliability, for example, can be accomplished with more data points and faster response times, which are at the heart of IIoT.
It’s big data by nearly any definition: high-volume, high-velocity, and highly variable. It’s table stakes for higher-level insight and decision making.
The volume of data generated could be overwhelming, if not for cost-effective, high-speed processing. A single industrial machine, for instance, can produce 1 terabyte of data hourly. The exploding number of sensing devices that share data continue adding to volume. In fact, as devices with sensing and actuation capabilities become practically ubiquitous, global adoption of (IPv6) will be essential.
The variety of data often has to be combined from many sources that will almost always have different structures and meet various standards.
For critical systems, such as backup power, data is streamed in real time at speeds measured in milliseconds. It’s monitored, stored, and if it signals out-of-parameter operating conditions, displayed graphically and perhaps annunciated.
Even with high-speed processing, the growing volume and variety of data gathered for analysis would be nearly impossible to effectively manage as a whole. The solution is cluster management.
For IIoT, cluster management is a group of sensing devices on related equipment. A prime example is the coexistence of devices for building management systems, supervisory control and data acquisition, data center infrastructure management, and critical power management systems. The devices have local intelligence and compatible, two-way communication pathways, and, ideally, streamlined network topology protocols that eliminate repetitive wrapping and unwrapping of data.
Such clusters integrate legacy equipment and new technologies into an interoperable, distributed ecosystem that can be fairly autonomous and remotely controlled. A top 10 global banking firm, for example, monitors and controls a critical power management system more than 900 miles from the firm’s control center. Near-term, it plans to manage such systems globally.
Engineering considerations for IIoT clusters include compatibility across a wide range of products by multiple manufacturers, robustness, network protocols and standards, speed, remote access and control, and security. The key is to partner with an organization that has accomplished much of the legwork in developing a cohesive solution that accommodates much, if not all, of the devices in a cluster.
When presented as a business case, IIoT is an opportunity for consulting and specifying engineers to take the lead in helping clients operate their facilities more efficiently over the long term, while reducing operating costs.
Bhavesh Patel is vice president, global marketing, at ASCO Power Technologies, Florham Park, N.J.