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Description
Actcast is a cutting-edge edge-AI IoT platform service that seamlessly connects real-world events and data to the Internet by executing deep learning inference on edge devices, which facilitates immediate sensing, analysis, and assimilation of physical data with online systems while minimizing both data transfer expenses and privacy concerns. By leveraging edge computing, it allows for the execution of deep learning models directly on affordable hardware like Raspberry Pi, transforming raw inputs from sensors and cameras into meaningful, semantic information that can be relayed to web services or applications. The platform is designed to support the deployment, remote management, and monitoring of IoT applications, referred to as "Acts," across a variety of devices, offering developers essential tools such as an SDK and command-line interface for creating, packaging, and deploying applications within Docker containers that analyze input and deliver condensed outputs. Furthermore, Actcast features capabilities for organizing device groups, setting up triggers and webhooks for event notifications, and managing updates and device statuses through a unified dashboard, ensuring a more streamlined and efficient IoT experience. This comprehensive approach not only enhances operational efficiency but also improves the scalability of IoT solutions.
Description
Set up the IoT simulator to transmit real-time messages that can either fall within a specific range or derive from a random assortment of values, taking into account both time and client factors. The system should replicate dynamic messages in two formats, TEXT and JSON, closely mimicking the functionality of actual IoT devices. It will facilitate the transmission of four distinct types of dynamic values, including system variables like timestamps and client identifiers, as well as random, range-based, linear, and constant values, allowing for diverse data publication in each sequence to effectively align with genuine device behavior. Users can configure IoT events by uploading a predefined dataset via a CSV file using the CSV Importer feature. Each command dispatched to the edge device or IoT endpoint will generate a response from the device upon completing the requested action. Additionally, users can create realistic scenarios with automatic responses through Behavior Simulation, an engine designed to reply to subscribers based on the messages and topics received. Once a simulation request is initiated, the virtual connected devices commence data publication to the endpoint, ensuring seamless interaction within the simulated environment. This comprehensive setup enhances the realism of IoT simulations, bridging the gap between theoretical modeling and practical application.
API Access
Has API
API Access
Has API
Integrations
Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
Raspberry Pi OS
Salesforce
Slack
X (Twitter)
Integrations
Amazon Web Services (AWS)
Google Cloud Platform
Microsoft Azure
Raspberry Pi OS
Salesforce
Slack
X (Twitter)
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$599 per year
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Actcast
Founded
2018
Country
United States
Website
actcast.io
Vendor Details
Company Name
Bevywise Networks
Founded
2016
Country
United States
Website
www.bevywise.com/iot-simulator/
Product Features
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization
Product Features
IoT
Application Development
Big Data Analytics
Configuration Management
Connectivity Management
Data Collection
Data Management
Device Management
Performance Management
Prototyping
Visualization