A framework of distributed indexing and data


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A framework of distributed indexing and data

  1. 1. A Framework of Distributed Indexing and Data Dissemination in Large Scale Wireless Sensor Networks [Author Yingshu Li] [Presenter Harsh Achrekar]
  2. 2. Proposal <ul><li>Integrated distributed Connected dominating set Based Indexing (CBI) data dissemination scheme </li></ul><ul><ul><li>Handling of large amount of sensing data </li></ul></ul><ul><ul><li>Min. usage [ limited n/w computational resources ] </li></ul></ul><ul><ul><li>providing timely responses to queries </li></ul></ul><ul><ul><li>scalability , load balance and adaptivity during dynamic changes . </li></ul></ul>
  3. 3. Connected Dominating Set [CDS] Concept <ul><li>For a graph G(V,E), a Dominating Set S of G is a subset of V where each node in V S is adjacent to at least one node in S. </li></ul><ul><li>Connected subgraph of G </li></ul><ul><li>Nodes in C are dominators , </li></ul><ul><li>the others are dominatees . </li></ul><ul><li>Candidate for virtual backbones in WSNs. </li></ul><ul><li>Routing tasks - dominators </li></ul>
  4. 4. <ul><li>A k-hop dominating set D in G is a set of nodes such that every node in G is at most k hops away from at least one of the nodes of D. </li></ul><ul><li>Fig. (b) shows 2-hop </li></ul><ul><li>dominating set. </li></ul>k-Hop Dominating Set [k-hop DS] Concept
  5. 5. Broadcast v/s data dissemination framework <ul><li>Without CDS </li></ul><ul><ul><li>A node broadcasts the packet whenever it first receives one </li></ul></ul><ul><li>With CDS </li></ul><ul><ul><li>All dominatees only need to send packets to their closest dominator(s ) </li></ul></ul><ul><ul><li>dominators forward the packets towards their destinations. </li></ul></ul>
  7. 7. Architecture CONNECTED DOMINATING BASED INDEX (CBI) <ul><li>Storage nodes </li></ul><ul><ul><li>Sensing data is collected and stored close to sensing nodes </li></ul></ul><ul><ul><li>to decrease the communication cost </li></ul></ul><ul><ul><li>form a k-hop dominating set of the whole network . </li></ul></ul><ul><li>Index nodes </li></ul><ul><ul><li>Information of high level semantically rich data are pushed and maintained </li></ul></ul><ul><ul><li>formed by a connected m-hop dominating set . </li></ul></ul><ul><li>Queries are routed to the appropriate index nodes instead of flooding into the whole network. </li></ul><ul><li>Storage and index nodes usually do not sense raw data </li></ul>
  8. 8. A Layered Approach <ul><li>Bottom layer - sensing nodes - monitor the targets to generate raw sensing data. </li></ul><ul><li>Middle layer - storage nodes - store the high level semantically rich data. </li></ul><ul><ul><li>max. dist(sensing nodes ,storage nodes )<= k hops. </li></ul></ul><ul><ul><li>raw data need not travel across the entire n/w. </li></ul></ul><ul><li>Top Layer – index nodes - store the index information for those high level semantically rich data </li></ul><ul><ul><li>use connected m-hop dominating set as index node set to dominate the storage nodes . </li></ul></ul><ul><ul><li>According to property of connected m- hop dominating set, max. dist (storage nodes ,index nodes) <= m hops </li></ul></ul><ul><ul><li>index node set is strictly connected. </li></ul></ul>
  9. 9. Storage Nodes Determination <ul><li>Query Injection time from sink to index node </li></ul><ul><ul><li>Query_inject_time(sink ,index )<=(k+m) hops (constant) . </li></ul></ul><ul><li>construct k-hop DS of n/w - obtain storage node set </li></ul><ul><li>Randomly select a node(usually a center of n/w) as a root. Initialize BFS search on root . </li></ul><ul><li>Every/All node exchanges info with its k-hop neighbors about the level[?], degree[?] and ID[?] . {? – explain} </li></ul><ul><li>To obtain Storage node of small size </li></ul><ul><ul><li> subgraph composed by all storage nodes have small diameter </li></ul></ul><ul><ul><li> any leaf node should not be a storage node </li></ul></ul>
  10. 10. <ul><li>if (L1 < L2) or (L1 = L2 && D1 > D2) or (L1 = L2 && D1 = D2 && ID1 < ID2) </li></ul><ul><li>then ( L1,D1, ID1) >(L2,D2,ID2) </li></ul><ul><li>At first, Leaf node u =smallest (Level,Degree,ID) among its neighbors sends a DOMINATING message to its exact k-hop away Parent node v =largest (Level,Degree,ID) to request v become a dominator (storage node). </li></ul><ul><li>When v receives this DOMINATING message, it becomes Black and broadcasts a BLACK message to all of its k-hop neighbors </li></ul><ul><li>On receiving a BLACK message from its parent, u becomes Gray and broadcasts a GRAY message to all of its k-hop neighbors . </li></ul><ul><li>Next node with smallest(Level,Degree,ID) among its k-hop neighbors that have not decided its status start this procedure. </li></ul><ul><li>Terminate - root ={ Gray,Black}. </li></ul>
  11. 11. <ul><li>Node 26 ,a leaf nodes with variable (4,1,26) sends a DOMINATING message to Node 7 with largest (Level,Deg, ID) within Node 26’s 2-hop neighbors. Node 7 becomes Black and broadcasts a BLACK message. </li></ul>
  12. 12. Index Nodes Determination <ul><li>Construct connected m-hop dominating set I to dominate all storage nodes only and use all the nodes in I as index nodes. </li></ul><ul><li>Form new graph G’ of all the storage nodes and their parents in G . </li></ul><ul><li>Construct a connected m-hop dominating set . </li></ul><ul><li>- all the storage nodes should be dominatees </li></ul><ul><li>Add connectors to connect the red nodes which are index nodes. </li></ul>
  13. 13. Index Construction <ul><li>Each index node stores one copy index of its dominatees (storage nodes). </li></ul><ul><li>Flood query to all index nodes for query result </li></ul><ul><li>flood overhead is much lower </li></ul><ul><ul><li>size (index node set)< size( nodes in whole n/w) </li></ul></ul><ul><li>When a query is injected into the sink (Node 29) it is forwarded to the Storage Node 4 which stores the data for Node 29. </li></ul><ul><li>Then, the query is flooded to all the index nodes. After that, the query result is returned to the sink after getting the result. </li></ul>
  14. 14. Results