Recently Cisco and Ericsson announced a strategic partnership to create the networks of the future. Their partnership will lead to the synergy in developing innovative networks for connecting billions of the internet of things (IoT) devices considering their complementary efforts in the IoT innovation activities. Because patents can provide insights regarding the state of the art and technical details of innovation in a company, one can have insights regarding the future Cisco + Ericsson IoT networks based on each company’s patents for the IoT networks.

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Future Cisco + Ericsson Internet of Things (IoT) Networks Insights from Patents
Alex G. Lee (alexglee@techipm.com)
https://www.linkedin.com/today/author/2853055
Recently Cisco and Ericsson announced a strategic partnership to create the networks of the future. Their
partnership will lead to the synergy in developing innovative networks for connecting billions of the internet of
things (IoT) devices considering their complementary efforts in the IoT innovation activities. Because patents can
provide insights regarding the state of the art and technical details of innovation in a company, one can have
insights regarding the future Cisco + Ericsson IoT networks based on each company’s patents for the IoT networks.
Cisco is developing the intelligent autonomous IoT networks exploiting the machine learning (ML). The ML is
concerned with the design and the development of algorithms that take as input empirical data (such as network
statistics and performance indicators), and recognize complex patterns in these data. In general, these patterns are
then used to make decisions automatically.
US20150195216 illustrates the use of the ML in order to estimate the behavior of the communication channels
based on prediction, and then, to select the appropriate transmission strategy in the multi-hopping networks.

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US20150195192 illustrates the use of the ML in order to predict whether a network element failure is relatively
likely to occur based on the collected and analyzed network metrics. In responseto predicting that a network
element failure is relatively likely to occur, traffic in the network is rerouted in order to avoid the network element
failure before it is likely to occur. US20150195185 illustrates the use of the ML in order to improve QoS
dynamically using selective tracking of packet retransmissions. US20150188935 illustrates the use of the ML in
order to mitigate the attack. A node that receives network traffic data predicts a probability that the network nodes
are under attack based on the network traffic data. The node then decide to mitigate a predicted attack by
instructing nodes to forward network traffic on an alternative route without altering an existing routing topology of
the network to reroute network communication around the nodes under attack.
Machine to Machine (M2M) communications involve the communication (using wired or wireless means, or a
combination of both) between two machines without human intervention. Ericsson is developing the seamless
M2M networks with high mobility and reduced latency.
US20150319771 illustrates the M2M networking of the IoT device at low costthat complies with modern cellular
communication standards while having low power consumption. US20150078327 illustrates the mobility-based
radio resource assignment methods. US20150305054 illustrates the method for power optimized transmission
scheduling in an energy harvesting M2M device. US20150249901 illustrates the M2M services enablement
architecture using a cellular access networks.

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Consequently, the future Cisco + Ericsson IoT networks will connectbillions of the IoT devices with the capability
of autonomous management of the networks, high mobility and reduced latency for various IoT applications.