An adaptive framework of multiple schemes for event and query distribution in wireless sensor networks Vincent Tam, Keng-Teck Ma, and King-Shan Lui IEEE Consumer Communications and Networking Conference (CCNC ’ 07)

Outline  Introduction  Adaptive framework using multiple schemes  Simulation  Conclusion

Approaches of getting information  Push-based approach  Floods to all nodes  No query  Pull-based approach  Stores locally  Floods the network with a query  Data-centric storage (DCS) Depends on the type information  Computed using a globally hash function

An important issue of DCS  Where to keep event information Keeping information in the center  Increase the workload of the nodes  Varying query/event ratio Affect the performance of various data dissemination schemes  An adaptive framework Flexibly switch from one scheme to another Based on the results evaluated by cost

The cost analysis of local storage The total message cost for Local Storage (LS): n: the number of nodes Q: the number of queries D q :the number of events for queries D total : the total number of events The message overheads of flooding to n nodes The average message cost to answer all the queries

The cost analysis of data-centric storage The total message cost for Data-Centric Storage (DCS): n: the number of nodes Q: the number of queries D q :the number of events for queries D total : the total number of events The average message cost for routing the queries to hashed nodes The average message cost associated with the storage of events being detected The average message cost to answer all the queries

Cost Analysis implying GHT outperforms the LS with a small overall message overheads LS excelled the GHT with a small overall message overheads In many real-world applications of wireless sensor networks, such of (query : event) can be dynamically changing over time

Adaptive framework using multiple schemes  Initially, framework starts with LS Relatively few events detected in the beginning  When event is detected Stores in detecting node  When a query is generated Route to home node (50, 89) Sink Get( “ lion ”, data) (50,89)=Hash( “ lion ” )

Adaptive framework using multiple schemes  Upon a query is received Compute the cost of LS and GHT LS is higher than GHT  From LS to GHT GHT is higher than LS  From GHT to LS  Any node with the locally stored event Reply to the home node  Home node update the count for events being detected  Relay the event back to query node

Switching from LS to GHT Sensor node home node Location Mapping location for lion Firstly setting the GHT flag in the next arriving query as still under the LS mode Sink Get( “ lion ”, data) (50,89)=Hash( “ lion ” ) Each node reset itself to the GHT mode

After switching from LS to GHT Sensor node home node Location Mapping location for lion Put(“lion”, data) (50,89)=Hash(“lion”) Sink Get(“lion”) (50,89)=Hash(“lion”)

Switching from GHT to LS Sensor node home node Location Mapping location for lion Firstly switch itself to LS mode after answering the query Put(“lion”, data) (50,89)=Hash(“lion”) Set LS flag embedded in the ACK packets for all the future events being detected

Switching from GHT to LS Sensor node home node Location Mapping location for lion Sink Get( “ lion ”, data) (50,89)=Hash( “ lion ” ) Each node reset itself to the LS mode

After switching from GHT to LS Sensor node home node Location Mapping location for lion Put(“lion”, data) (50,89)=Hash(“lion”) Sink Get(“lion”) (50,89)=Hash(“lion”)

A list of the recently events and queries  Each home node maintain a list of the recently received events and queries Compute the number of events or queries  Each entry is time-stamped A sliding window filter out those expired events or queries Sliding window size (time) Past data Future data

Simulation

Conclusion  The query-to event ration Can be changing over time  An adaptive frame work Flexibly switch from one scheme to another Based on the message cost  To minimize the overall message overheads