Smart Cities technologies can help power more efficient, greener, and more livable cities. The insights from Smart City devices can optimize city operations and generate significant cost savings, but challenges remain integrating with the in-place municipal systems that could benefit most. As city governments continue to increase investments into IOT technologies, Smart City decision makers should focus on solutions that emphasize local, real-time data analysis, and interoperability to maximize impact, as well as uncovering hidden insights by using Machine Learning.We've put together this infographic of Smart Cities Trends & Spend to help you navigate IOT opportunities and uncovering IOT efficiencies in your Smart City:
The Marketing team at Swim analyzed available United States Department of Transportation’s traffic project estimates to pinpoint the cost of alleviating traffic concerns in various US markets through the use of software. Swim wanted to identify the traffic optimization opportunities that would benefit from the use of Internet of Things (IOT) technologies, sensor data, and machine learning, which would also reduce traffic costs & expenditures while optimizing and alleviating traffic conditions.
The Internet of Things (IOT) has the potential to unlock deep insights about IT (Information Technology) and OT (Operational Technology) aspects of Building Management by using data and insights from Machine Learning. Integrating Building Management Systems (BMS) with IOT sensors can provide insights that identify inefficiencies, predict maintenance needs, and alert facilities managers to equipment failures in real-time. In order to maximize the benefit of IOT investments, building managers should look to IOT solutions that aggregate across all systems (HVAC, utilities, security, etc.) to deliver a complete, contextual view of current building performance.
Often when I visit enterprise users of industrial automation – for example in manufacturing or retail – I find tremendous excitement on the part of ops teams at the potential for IOT solutions to deliver value. But when I dig deeper into their requirements or ask how they would measure the value of an IOT solution, the room falls silent. Ops teams are too busy meeting delivery schedules or production targets, and they struggle to imagine the possibilities of an environment transformed by machine learning and real-time insights and control. They can’t begin to imagine how new technology could transform their organization’s productivity and make their lives easier.
Intelligent Transportation Systems (ITS) can help a city or region reduce traffic congestion and minimize infrastructure management costs. However, the flood of data from connected sensors overwhelms most architectures. ITS deployments require a solution that integrates with existing hardware, reduces data at the edge, and delivers high value data streams to city managers, relevant municipal systems, and third-party developers.We've put together this Infographic to share the trends we've noticed regarding traffic statistics and how to build smarter traffic systems for Smart Cities:
Smart Cities use data from Industrial IOT devices to better plan, manage, and operate their local policies & initiatives to improve the quality of life of citizens while reducing environmental footprints. A Smart City has digital technology embedded across all city functions. Smart City efforts use IOT technologies for efficient provisioning of infrastructure and governance ranging from transport, energy & utilities, and safety. Finding partners that can manage the deluge of real-time data from already-deployed edge devices is critical for Smart City success.We've put together this Infographic to share the trends and growth we've noticed regarding Future of Smart Cities:
In my previous post I made the case that Industrial IOT (IIOT) is not an App nor DevOps problem; It’s a data problem. There aren’t enough app and ops folk who know the language of big-data and cloud to get the job done. And there’s no way to deliver these new stacks in orgs with today’s Information Technology (IT) and Operational Technology (OT) skill-sets.
Swim ESPTM is a powerful edge-based addition to traffic deployments that transforms your Advanced Traffic Management Systems (ATMS) with machine learning. Swim ESP is designed to “plug & play” with existing traffic systems, where it delivers context-relevant insights precisely where and when they are needed, empowering traffic operators and city managers in real-time.We've put together this Infographic to share ten ways Swim ESP Boosts your Smart Traffic Systems with Machine Learning:
Industrial IOT applications must receive & react to streaming data from sensors in real-time. Businesses should be managing data at the source, directly at the edge, to turn the floods of data into actionable business decisions.
Too many brave pioneers seeking to transform the efficiency of their industrial infrastructure with new Industrial IOT (IIOT) applications face such daunting odds that their projects are doomed to failure before they even get off the ground. In this post I don’t want to focus on the reason why most projects fail. Instead, I want to give you a glimpse of a powerful new approach that has the potential to transform success rates and catalyze the adoption of new IIOT solutions.