Ericsson launches network services for IoT

Ericsson has launched cellular IoT software and IoT Accelerator offerings with a complete set of network services for commercial Internet of Things (IoT) networks.

These services enable service providers to efficiently address the deployment and operation of the massive number of IoT devices being introduced to LTE networks.

Applicable for Cat-M1 (also called LTE-M) and Narrow Band IoT (NB-IoT) technologies, these services include IoT network design and optimization, deployment, operation and management, and are supported by the recently expanded Support Services offering.

Peter Laurin, Head of Business Area Managed Services, Ericsson said, “We anticipate IoT devices will surpass mobile phones as the largest category of connected devices as early as 2018 and, according to Ericsson’s latest Mobility Report, there will be 18 billion connected IoT devices in 2022. This massive uptake requires a different approach to network planning, design, operations and capabilities than traditional mobile broadband networks.”

Ericsson is also introducing new IoT software features, such as Voice over LTE (VoLTE) support for Cat-M1. This will enable operators to explore new use cases in which it can be advantageous for IoT devices to support voice services, opening up opportunities to expand enterprise services to areas such as security alarm panels, remote first-aid kits, wearables, digital locks, disposable security garments, and other types of IoT-enabled applications and services.

To support network design and automation, Ericsson is introducing scenario assessment, network modelling, design development, and developmental appraisal for massive IoT networks.

To address the need for an adapted approach to management and operation of operators’ networks, Ericsson is introducing automated machine learning to its Network Operations Centers (NOCs). These tools will help operators to manage delivery cost and take a proactive approach to event and incident management. In a trial, 80% of all incidents were identified by machine learning only with no human intervention – and the root cause was identified correctly in 77% of cases.

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