2016 Cisco IoT Networks Strategy Perspectives from Patents

Alex G. Lee (alexglee@techipm.com)

https://www.linkedin.com/today/author/2853055

Cisco announced a strategic partnership with and Ericsson to create the networks of the future. Followings show

the insights regarding Cisco strategy perspectives for developing the IoT networks of the future to make Cisco as

the IoT networks innovation leader in 2016.

Predictive Analytics for IoT networks

Predictive analytics analyzes current and historical data to make predictions about future events and trends.

Predictive analytics can apply to many IoT applications. For example, Cisco patent application US20150333992

illustrates the application of predictive analytics for managing the IoT Networks. Predictive analytics dynamically

adjusts which network nodes export or collect metrics regarding the operational state of the IoT networks and used

to make predictions regarding the IoT networks. US20150333953 illustrates another application of predictive

analytics for managing the IoT Networks. Predictive analytics allows high network reliability to be maintained

proactively for critical applications.

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Deterministic Networking for Smart Grid

Deterministic networking refers to networks that can guarantee the delivery of packets within a bounded time.

Generally, deterministic networking relates to achieving characteristics such as guaranteed delivery, fixed latency,

and jitter close to zero (e.g., micro seconds to tens of milliseconds depending on application). Deterministic

networking can provide the reliable networks that are needed for all smart grid applications. US20150023186

illustrates the deterministic wireless networks for the smart grid applications by providing for timeslot distribution

in a distributed routing protocol in a shared-media communication networks. US20150333857 illustrates the

scheduling technique for the deterministic network that reduces the presence of jitter in the finalized schedule.

Machine Learning for 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.

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 response to predicting that a network ©2015 TechIPm, LLC All Rights Reserved http://www.techipm.com/

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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.

Fog Computing

Billions of interconnected devices that are connected to the internet in the IoT (Internet of Things) will produce

astronomical amount of data to process. The amount of data can easily overload the cloud computing resources at

the back-end IT systems. With Fog (or Edge) computing, the problem can be eased by allowing smart devices (e.g.,

smartphones, PCs, set-top boxes) at the edge of the IoT networks.

US20150261876 illustrates the network environment includes multiple fog computing devices each connected with

a communication network. Fog computing pushes applications, data, and computing power (including computing

services) away from centralized points in a network to logical extremes or edges of the network. Fog computing

covers a wide range of technologies including wireless sensor and actuator networks, mobile data acquisition,

mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing. Fog computing ©2015 TechIPm, LLC All Rights Reserved http://www.techipm.com/

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devices communicate with each other using a variety of Internet protocols. Fog computing devices each host

HTML-based web browse that controls the operational features of the fog computing devices.

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Internet of Things (IoT) Business & Technology & Patent Integrated Strategy

IoT Business Insights, Business Models, Samsung/Cisco/Apple/IBM/Qualcomm IoT Strategy, Innovation Strategy,

New Products/Services Development, Platforms, R&D Insights, Networks (5G), Applications (Smart Home,

Connected Car, Smart Healthcare, Smart Grids, Big Date, Fintech), IoT UI/UX, M2M Connectivity, New IoT

Products Development, Patent Development Strategy, IoT Startup Strategy, Implications of Patent Laws to IoT

Business

Link: http://www.slideshare.net/alexglee/internet-of-things-iot-business-technology-patent-integrated-strategy

If you want the pdf copy, please send me (alexglee@techipm.com) your request with your name and affiliation.

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