openPR Logo
Press release

Edge Computing Offers New Approach to Building Automation

08-22-2016 01:33 PM CET | IT, New Media & Software

Press release from: pointgrab

In building automation, sensors such as motion detectors, photocells, temperature, and CO2 and smoke detectors are used primarily for energy savings and safety. Next-generation buildings, however, are intended to be significantly more intelligent, with the capability to analyze space utilization, monitor occupants’ comfort, and generate business intelligence.

To support such robust features, building-automation infrastructure requires considerably richer information that details what’s happening across the building space. Since current sensing solutions are limited in their ability to address this need, a new generation of smart sensors (see figure below) is required to enhance the accuracy, reliability, flexibility, and granularity of the data they provide.

Data Analytics at the Sensor Node

In the new era of the Internet of Things (IoT), there arises the opportunity to introduce a new approach to building automation that decentralizes the architecture and pushes the analytics processing to the edge (the sensor unit) instead of the cloud or a central server. Commonly referred to as edge computing, or fog computing, this approach provides real-time intelligence and enhanced control agility while simultaneously offloading the heavy communications traffic.

Rule-Based or Data-Driven?

The challenges associated with rich data analysis can be addressed in different ways. The conventional rule-based systems are supposedly easier to analyze. However, this advantage is negated as the system evolves, with patches of rules being stacked upon each other to account for the proliferation of new rule exceptions, thus resulting in a hard-to-decipher tangle of coded rules.

As the hard work of rule creation and modification is managed by human programmers, rule-based systems suffer from compromised performance. They have shown to be less responsive in adapting to new types of data, such as data sourced from an upgraded sensor, or a new sensor of previously unutilized data. Rule-based systems can also fail to adapt to a changing domain, e.g., a new furniture layout or new lighting sources.

These deficiencies can be readily overcome with data-driven “machine-learning” systems, which have proven to be superior tools for rich data analysis, especially when cameras are employed at the sensing layer. Machine-learning systems transfer the labor of defining effective rules from the engineers to the algorithm. As a result, the engineers are only tasked with defining the features of the raw data that hold relevant information.

Once the features have been defined, the rules and/or formulas that use these features are learned automatically by the algorithm. The algorithm must have access to a multitude of data samples labeled with the desired outcomes for this to work, so that it can properly adapt itself.

When the rules are implemented within the sensor, it runs a two-staged, repeating process. In stage one, the human-defined features are extracted from the sensor data. In stage two, the learned rules are applied to perform the task at hand.

The Deep-Learning Approach

Within the machine-learning domain, “deep learning” is emerging as a superior new approach that even alleviates engineers from the task of defining features. With deep learning, based on the numerous labeled samples, the algorithm determines for itself an end-to-end computation that extends from the raw sensor data all the way to the final output. The algorithm must discern the correct features and how best to compute them.

This ultimately fosters a deeper level of computation that’s much more effective than any rule or formula used by traditional machine learning. Typically, a neural network will perform this computation, leveraging a complex computational circuit with millions of parameters that the algorithm will tune until the right function is pinpointed.

The implications of deep learning on system engineering are profound, and the contrast with rule-based systems is significant. In the rule-based system world, and even with traditional machine learning, the system engineer requires extensive information about the domain in order to build a good system. In the deep-learning world, this is no longer necessary.

With the arrival of the IoT and the proliferation of data across the network, deep learning allows for faster iteration on new data sources and can use them without requiring intimate knowledge. When applying a deep-learning approach, the engineer’s main focus is to define the neural network’s core architecture. The network must be large enough to have the capacity to optimize to a useful computation, but simple enough so that available processing resources aren’t outstripped.

A neural network can be tailored to fit any given time budget to the maximum threshold to ensure maximum exploitation of the processing power. If the computational budget rises and there’s more time to run the calculation, a larger network can be assessed utilizing the new budget.

Once the architecture is defined, it stays fixed while the parameters of the neural network are tuned. This process can take days and even weeks for even the highest performance machines. However, the computation itself, extending from raw inputs to output, takes a fraction of a second, and it will remain exactly the same throughout the process.

The scalability and flexibility of deep learning distinguish it as a powerful approach for a real-time system like a smart sensor in the continuously changing environment of commercial buildings. Another advantage of using neural networks is that they’re extremely portable, and can be very easily built and customized using available software libraries. This allows the neural network to run the same network on different types of devices.

Moreover, such portability allows for quick turnarounds between the learning sessions, which typically use powerful machines. On top of that, engineers can observe how the neural network behaves when it’s deployed on embedded processors.

This article was originally published in our sister publication electronic design.

For more information about Edge Computing, visit:

PointGrab provides smart sensor solutions to the building automation industry. The company applies its superior deep-learning technology to the building automation ecosystem, where opportunities to gather data are abundant, but efficient, real-time analytics are lacking.

4 Haharash st. Hod Hasharon

This release was published on openPR.

Permanent link to this press release:

Please set a link in the press area of your homepage to this press release on openPR. openPR disclaims liability for any content contained in this release.

You can edit or delete your press release Edge Computing Offers New Approach to Building Automation here

News-ID: 357472 • Views: 288

More Releases from pointgrab

Edge-Analytics Sensors For Smart Buildings
Smart sensors are crucial devices for enabling next-generation building automation, providing the actionable data that optimize building operations, conserve energy and boost workplace intelligence. With the mainstream adoption of IoT impending in almost every conceivable location, buildings too are becoming increasingly connected. Office spaces and commercial buildings are positioned to be connected through a network of IoT smart sensors that will help facility managers save time and money by improving space

More Releases for Data

Weather Station Data Logger Collects Meteorological Data
Complete Weather monitoring using an Intelligent Universal Data Logger A construction company needed to collect meteorological data to study the effects of building materials such as roofing tile. CAS DataLoggers provided the dataTaker DT-85 for an environmental monitoring solution. Long-term monitoring of a variety of weather data is required to determine how the products perform under harsh environmental conditions. The field engineer desired a single recording device to connect with
How to turn Data into Smart Data
Both enterprises and individuals have to process some kind of data every day, whether it is a short message, a notification, a piece of news, statistics, a video, etc. If we accumulate all the data acquired in a month, the amount guarantees to shock anyone. Turning data into smart data As we go through the daily pile of data generated, not only must we record it, we also need to make sense
Data Quality and Data Governance Solution Market
In the enterprise data management ecosystem, data quality is a broad term which refers to the quality, integrity, and consistency of data and/or process etc. Data quality also implies the degree of data accuracy and consistency. On the other hand, data governance focusses on the management of data assets by assigning authority, control, and responsibility of data and encompasses three key areas: people, process, and technology. Data quality and data governance
B2B Data Matching Services | Leo Data Services
B2B Data Matching Services - Revolutionary Marketing Campaigns In today's business scenario, managing successfully B2B Data Matching for prospect customer can be a challenge even the most capable organizations. Business to Business Marketing services from Leo Data Services are always customized to meet the needs of the business prospects. We make sure that our clients have quick, reliable access to their data through several possible interface methods that cover everything from data
B2B Data Services - Data Management - Data Providers
When it comes to Online Marketing, B2B Data Services is a well-known name for maintaining reputation among Digital Marketing competitors. It is a one stop shop for start-ups and enterprises looking for cost cutting and out of box solutions in the field of Data Management and services related to Campaign Marketing, Mobile Viral Marketing, Social Media, Search Engine Optimization, Email Marketing Campaigns and Internet Marketing. Sharing space among the well-established
Portable WiFi Data Loggers Record Data Anywhere!
Grant Squirrel Series Offers Reliable Standalone Operation wifi_dataloggersIf you need a device to collect data in the field, in the lab, or on the factory floor, CAS DataLoggers can provide your application with Portable WiFi Data Loggers from Grant Instruments. Common parameters such as Temperature, Humidity, Voltage, Current, Pressure and Force can all be measured and stored using Grant Squirrel series data loggers. These handheld data loggers have internal batteries allowing them