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Knowledge Management

CHEN Xi scotor317@gmail.com 2009. 05 DIATEL E.U.I.T.T UPM . Knowledge Management. Definition. Difference between Data, Information and Knowledge. Key Point to distinguish : the level of abstraction being considered . Definition.

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Knowledge Management

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  1. CHEN Xi scotor317@gmail.com 2009. 05 DIATEL E.U.I.T.T UPM Knowledge Management

  2. Definition Difference between Data, Information and Knowledge Key Point to distinguish: the level of abstraction being considered

  3. Definition Knowledge Management refers to the conscious and systematic handling of knowledge as a resource and its target-oriented use of knowledge in organizations. Knowledge Management spans the entirety of all concepts, strategies and methods for creating an intelligent and learning scheme. In this sense, Man, organization, and technology form the three pillars of KM. ------KMForum

  4. Processing Flow Image Source: KMForum

  5. The Goal of KM • For group learning applications • For decision support & decision making • Focus of KM

  6. Features • KM is orderly and goal-directed • KM is value-added

  7. Challenges • (a)Data formats • (b)Interfaces • (c)Fuzzy customer needs • (d)Redundancy.

  8. KM in WSNs • The way to access KM in WSNs • Smart sensor nodes • Intelligent processing at an aggregator node

  9. KM in WSNs • Differences between KM in WSNs and KM in other networks. • (a)Data has high possibility to be disturbed in the communication path. Sometimes they should be disregarded and try to get the data in a different way.

  10. KM in WSNs • Differences between KM in WSNs and KM in other networks. • (b)The history of failures can be managed according to the easy failure of sensor nodes. The data from certain sensor node is not reliable should also be considered

  11. KM in WSNs • Differences between KM in WSNs and KM in other networks. • (c)There should be a tolerance level and a method of aggregation for data from different sources, because the data in some region may be similar while in other region that may be absolutely different.

  12. IoT general KM Architecture

  13. IoT general KM Architecture Example 1 Knowledge Network Cluster Network Static Node & Active Node Mobile Node & Active Node Static Node & Passive Node

  14. WSNs KM Architecture

  15. WSNs KM Architecture

  16. WSNs KM Architecture

  17. WSNs KM Architecture

  18. WSNs KM Architecture

  19. WSNs KM Architecture • Advantages: • This architecture will be robust that several lost of the nodes will not destroy the whole network. • Form a cluster but not integrated red, green and blue nodes into one bigger one will increase the reliability and lifetime of the whole network, and much easy to add or change it’s topology. Also, these much nodes can be treated as a hop, there will be multi path to reach the destination. • To form a distributed network using triangular knowledge distribution scheme can help the nodes to better manage themselves, For example, when there’s no need to run the node in full power in one small region, the regional network can lower the power consumption rate in the form of triangular network.

  20. WSNs KM Architecture • Disadvantages: • The nodes will form new knowledge from the data collected very soon, and it will cost a lot of power for nodes with same knowledge to update the new version of knowledge one by one every time, it need a scheme to choose the active nodes and regulate the time circle to update knowledge for different nodes. • When time is up for some nodes, the redeploy of new nodes will be difficult, but it’s a problem for all the wireless sensor network. • In practical environment, there may be more than three kinds of nodes deployed in one region, and if each node should be a router, the cost of each node will be higher, also, so many different packet processed in each nodes will lower the QoS.

  21. Thank you

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