1. Data-Centric Storage in Sensor Networks With GHT Khaldoun A. Ibrahim, kibrahi1@binghamton.edu

2. Review of Data-Centric Routing Systems such as Direct Diffusion and TAG implement data-centric routing: –“Interests” or “queries” are routed to nodes that might contain matching data and responses are routed back to the querying nodes. –Usually requires flooding the interest or query. –Appropriate for long-lived queries initiated by users from outside the network. E.g. Continuously computing aggregates over a sensor field Implementing “one-shot” queries can be inefficient, why?

3. Data-Centric Storage The data that is generated at one node is stored at another node determined by the name of the data. –Data must be named Data can be stored and retrieved by name. Generally speaking, a data-centric storage system provides primitives of the form: –put (data) and –data = get (name).

4. Key Ideas Behind Data-Centric Storage D A B C

5. The Performance of Data-Centric Storage Systems Comparing against the two extremes, External Storage,in which all events are stored at a node outside the network; and a Local Storage where each event is stored ate the node at which it is generated.

6. External Storage: The cost of accessing the event is zero, while the cost of conveying the data to this external node is non-trivial, and significant energy is expended at nodes near the external node –Appropriate if the events are accessed far more frequently than generated. Local Storage: Incurs zero communication cost in storing the data, but incurs a large communication cost – a network flood– in accessing the data. –Feasible when events are accessed less frequently than they are generated. Data-Centric Storage: lies in between, incurs non-zero cost both in storing events and retrieving them.

7. We assume asymptotic costs of O(n) message transmissions for floods and O(√n) for point-to-point routing where n is the number of nodes. D e is the total number of events, Q is the number of queries and D q is the number of events which are returned as answers for the Q queries. When does DCS become more appropriate?

8. GHT: An Overview Event names are randomly hashed to a geographic location (e.g. x, y coordinate). Assumes all nodes know the approximate geographic boundaries of the network –E.g. a rectangular area encompassing all nodes Both a Put() operation and a Get() operation on the same key k will hash k to the same location. A key–value pair is stored at the node nearest the location to which its key hashes (“Home Node”). GHT is built on top of GPSR. –Assumes nodes know their geographic location.

9. How GHT Uses GPSR: The Home Node and Home Perimeter GPSR originates packets in greedy mode, but changes them to perimeter mode when no neighbor of the forwarding node is closer to the packet’s destination than the forwarding node itself. GPSR returns a perimeter-mode packet to greedy mode when the packet reaches a node closer to the destination than that at which the packet entered perimeter mode (stored in the packet). The “Home Node” for a GHT is the node geographically nearest the destination coordinate of the packet.

10. Under GHT, the packet enters perimeter mode at the home node, why? The packet then traverses the entire perimeter that encloses the destination, before returning to the home node When a packet returns in perimeter mode to the node that originated the perimeter traversal, the corresponding event is stored at that node. D A d Greedy Forwarding Perimeter Routing

11. GHT Robustness: Perimeter Refresh Protocol PRP Every T h seconds, the home node for a key generates a refresh packet addressed to the hashed location of that key. If a refresh packet was not received at a replica after T t nodes use this as and indication of home node failure, and initiate a refresh message themselves. If the receiver of the refresh packet is closer to the specified location, it will initiate a new perimeter traversal that will pass through the old home node.

12. GHT Scaling: Structured Replication If too many events with the same key are detected, that key’s home node could become a hotspot, both for communication and storage. To mitigate this problem, SR hierarchically decomposes the geographical region, and assigns new locations to act as mirrors of the home node. for a given root r and a given hierarchy depth d, one can compute 4^d - 1 mirror images of r. –d = 0 refers to the original GHT scheme without mirror

13. A node that detects an event now stores the event at the mirror closest to its location –The storage cost at one node for one key with n detected events is reduced from O(√n) to O(√n/2^d). GHT must now route queries to all mirror nodes recursively, starting from the root then to the three level-1 mirrors and so on –a single query incurs a routing cost of O(2^d√n) as compared with O(√n) for GHT without mirrors

14. Questions?

15. References [1] R. Govindan, “Data-centric Routing and Storage in Sensor Networks,” in Wireless Sensor Networks, 2004 [2] S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin, and F. Yu, Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table, In Mobile Networks and Applications (MONET), Special Issue on Wireless Sensor Networks, 8:4, Kluwer, August 2003