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Lecture 13

E.g an elephant sighting. Why Data centric Storage? Energy efficiency ... e.g. (

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Lecture 13

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    Slide 1:Data Centric Storage using GHT Lecture 13 October 14, 2004 EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor Networks

    Andreas Savvides andreas.savvides@yale.edu Office: AKW 212 Tel 432-1275 Course Website http://www.eng.yale.edu/enalab/courses/eeng460a

    Slide 2: Data centric Storage In Sensornets with GHT

    S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin and F. Yu MONET Special Issue on Sensor Networks, August 2003

    Slide 3:Overview

    Data Centric Storage Data is stored inside the network – each name corresponds to a location in space All data with the same name will be stored at the same sensor network location E.g an elephant sighting Why Data centric Storage? Energy efficiency Robustness against mobility and node failures Scalability

    Slide 4:Keywords and Terminology

    Observation ? low-level readings from sensors ? e.g. Detailed temperature readings Events ? Predefined constellations of low-level observations ? e.g. temperature greater than 75 F Queries ?Used to elicit information from sensor network

    Slide 5:Performance Metric:Total Usage /Hotspot Usage

    Use communication as a cost function for energy consumption Total Usage Total number of packets sent in the Sensor network Hotspot Usage The maximal number of packets send by a particular sensor node Costs used in the evaluation Message flooding cost O(n) Point-to-point routing cost n is the number of nodes

    Slide 6:Alternative Storage Schemes

    External Storage (ES) Events propagated and stored at an external location Local Storage (LS) Events stored locally at the detecting node Queries are flooded to all nodes and the events are sent back Data Centric Storage (DCS) Data for an event stored within the sensor network Queries are directed to the node that stores the data

    Slide 7:External Storage (ES)

    External storage event

    Slide 8:Local Storage (LS)

    event event Queries flooded at all the nodes

    Slide 9:Why do we need DCS?

    Scalability Robustness against Node failures and Node mobility To achieve Energy-efficiency

    Slide 10:Design Criterial: Scalability & Robustness

    Node failures Topology changes System scale to large number of nodes Energy Constraints Persistence (k,v) pair must remain available to queries, despite sensor node failures and changes in sensor network topology Consistency A query k must be routed correctly to a node where (k,v) pairs are stored – if these node change, then they should do this consisently Scaling in Database Size Topological generality – system should scale well on a large number of topologies

    Slide 11:Assumptions in DCS

    Large Scale networks whose approximate geographic boundaries are known Nodes have short range communication and are within the radio range of several other nodes Nodes know their own locations by GPS or some localization scheme Communication to the outside world takes place by one or more access points

    Slide 12:Data Centric Storage

    Relevant Data are stored by “name” at nodes within the Sensor network All data with the same general name will be stored at the same sensor-net node. e.g. (“elephant sightings”) Queries for data with a particular name are then sent directly to the node storing those named data

    Slide 13:Data centric Storage

    Elephant Sighting source:lass.cs.umass.edu

    Slide 14:Geographic Hash Table

    Events are named with keys and both the storage and the retrieval are performed using keys GHT provides (key, value) based associative memory

    Slide 15:Geographic Hash Table Operations

    GHT supports two operations ? Put(k,v)-stores v (observed data) according to the key k ? Get(k)-retrieve whatever value is associated with key k Hash function ? Hash the key in to the geographic coordinates ? Put() and Get() operations on the same key “k” hash k to the same location

    Slide 16:Storing Data in GHT

    Put (“elephant”, data) (12,24) Hash (‘elephant’)=(12,24) source:lass.cs.umass.edu

    Slide 17:Retrieving data in GHT

    Get (“elephant”) Hash (‘elephant’)=(12,24) (12,24)

    Slide 18:Geographic Hash Table

    Node A Node B

    Slide 19:Algorithms Used By GHT

    Geographic hash Table uses GPSR for Routing(Greedy Perimeter Stateless Routing) PEER-TO-PEER look up system (data object is associated with key and each node in the system is responsible for storing a certain range of keys)

    Slide 20:Algorithm (Contd)

    GPSR- Packets are marked with position of destinations and each node is aware of its position Greedy forwarding algorithm Perimeter forwarding algorithm A B A B

    Slide 21:GPSR: Right-Hand Rule In Perimeter Forwarding

    x y z 1 2 3

    Slide 22:Home Node and Home perimeter

    Home node: Node geographically nearest to the destination coordinates of the packet Serves as the rendezvous point for Get() and Put() operations on the same key In GHT packet is not addressed to specific node but only to a specific location Use GPSR to find the home node only perimeter mode of GPSR to find Home Perimeter Home Perimeter – perimeter that encloses the destination Start from the home node, and use perimeter mode to make a cycle and return to the home node

    Slide 23:Problems

    Robustness could be affected Nodes could move (i.d. of Home node?) Node failure can Occur Deployment of new Nodes Not Scalable Storage capacity of the home nodes Bottleneck at Home nodes

    Slide 24:Solutions to the problems

    Perimeter refresh protocol mostly addresses the robustness issue Structured Replication address the scalability issue how to handle storage of many events

    Slide 25:Perimeter refresh protocol

    Replicates stored data for key k at nodes around the location to which k hashes Stores a copy of the key value pair at each node on the home perimeter Each node on the perimeter is called a replica node How do you ensure consistency & persistence A node becomes the home node if a packet for a particular key arrives at that node The perimeter refresh protocols periodically sends out refresh packets After a time period Th generate a refresh packet that contains the data for that key Packet forwarded on the home perimeter in the same way as Get() and Put() The refresh packet will take a tour of the home perimeter regardless the changes in the network topology since the key’s insertion This property maintains the perimeter

    Slide 26:Perimeter Refresh Protocol

    How do you guard against node failures When a replica node receives a packet it did not originate, it caches the data in the refresh and sets up a takeover timer Tt Timer is reset each time a refresh from another node arrives If the timer expires the replica node initiates a refresh packet addressed to the key’s hashed location Note: That particular node does not determine a new home node. The GHT routing causes the refresh to reach a node home node

    Slide 27:Perimeter Refresh Protocol

    E F B D A C L home Replica Replica Assume key k hashes at location L A is closest to L so it becomes the home node

    Slide 28:Perimeter Refresh Protocol

    E D F C B L Replica Replica home Replica Replica Suppose the node A dies

    Slide 29:Time Specifications

    Refresh time (Th) Take over time (Tt) Death time (Td) General rule Td>Th and Tt>Th In GHT Td=3Th and Tt=2Th

    Slide 30:Characteristics Of Refresh Packet

    Refresh packet is addressed to the hashed location of the key Every (Th) secs the home node will generate refresh packet Refresh packet contains the data stored for the key and routed exactly as get() and put() operations Refresh packet always travels along the home perimeter

    Slide 31:Structured Replication

    Too many events are detected then home node will become the hotspot of communication. Structured replication is used to address the scaling problem Hierarchical decomposition of the key space Event names have a certain hierarchy depth

    Slide 32:Structured Replication

    Slide 33:Structured Replication

    A node that detects a new event, stores that event to its closest mirror this is easily computable This reduces the storage cost, but increases the query cost GHT has to route the queries to all mirror nodes Queries are routes recursively First route query to the root, then to the first level and then to the second level mirrors Structured replication becomes more useful for frequently detected events

    Slide 34:Evaluation

    Simulation to test if the protocol is functioning correctly Done in the ns-2 network simulator using an IEEE 802.11 mac This is a well known event driven simulator for ad-hoc networks Larger scale simulations for the comparative study where done with a custom simulator

    Slide 35:Comparative Study

    Simulation compares the following schemes External Storage (ES) Local Storage (LS) Normal DCS – A query returns a separate message for each detected event Summarized DCS(S-DCS): A query returns a single message regardless of the number of detected events Structured Replication DCS (SR_DCS) – Assuming an optimal level of SR Comparison based on Cost Comparison based on Total usage and Hot spot usage

    Slide 36:Assumptions in comparison

    Asymptotic costs of O(n) for floods and O( n) for point to point routing Event locations are distributed randomly Event locations are not known in advance No more than one query for each event type (Q –Queries in total) Assume access points to be the most heavily used area of the sensor network

    Slide 37:Comparison based on Hot-spot/Total Usage

    n - Number of nodes T - Number of Event types Q – Number Of Event types queried for Dtotal – Total number of detected events DQ- Number of detected events for queries

    Slide 38:DCS TYPES

    Normal DCS – Query returns a separate message for each detected event Summarized DCS – Query returns a single message regardless of the number of detected events (usually summary is preferred)

    Slide 39:Comparison Study – contd..

    Slide 40:Observations from the Comparison

    DCS is preferable only in cases where Sensor network is Large There are many detected events and not all event types queried Dtotal>>max(Dq,Q)

    Slide 41:Simulations

    To check the Robustness of GHT To compare the Storage methods in terms of total and hot spot usage

    Slide 42:Simulation Setup

    ns-2 Node Density – 1node/256m2 Radio Range – 40 m Number of Nodes -50,100,150,200 Mobility Rate -0,0.1,1m/s Query generation Rate -2qps Event types – 20 Events detected -10/type Refresh interval -10 s

    Slide 43:Performance metrics

    Availability of data stored to Queriers (In terms of success rate) Loads placed on the nodes participating in GHT (hotspot usage)

    Slide 44:Simulation Results for Robustness

    GHT offers perfect availability of stored events in static case It offers high availability when nodes are subjected to mobility and failures

    Slide 45:Simulation Results under varying Q

    Number of nodes is constant= 10000

    Slide 46:Simulation results under varying N

    Number of Queries Q =50

    Slide 47:Simulation Results for comparison of 3-storage methods

    S-DCS have low hot-spot usage under varying “Q” S-DCS is has the lowest hot-spot usage under varying “n”

    Slide 48:Conclusion

    Data centric storage entails naming of data and storing data at nodes within the sensor network GHT- hashes the key (events) in to geographical co-ordinates and stores a key-value pair at the sensor node geographically nearest to the hash GHT uses Perimeter Refresh Protocol and structured replication to enhance robustness and scalability DCS is useful in large sensor networks and there are many detected events but not all event types are Queried

    Slide 49:REFERENCES

    Deepak Ganesan, Deborah Estrin, John Heidemann, Dimensions: why do we need a new data handling architecture for sensor networks?, ACM SIGCOMM Computer Communication Review,  Volume 33 Issue 1, January 2003    Scott Shenker, Sylvia Ratnasamy, Brad Karp, Ramesh Govindan, Deborah Estrin, Data-centric storage in sensornets, ACM SIGCOMM Computer Communication Review,  Volume 33 Issue 1, January 2003 Sylvia Ratnasamy, Brad Karp, Scott Shenker, Deborah Estrin, Ramesh Govindan, Li Yin, Fang Yu, Data-centric storage in sensornets with GHT, a geographic hash table, Mobile Networks and Applications,  Volume 8 Issue 4, August 2003 Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin, John Heidemann, Fabio Silva, Directed diffusion for wireless sensor networking, IEEE/ACM Transactions on Networking (TON),  Volume 11 Issue, February 2003 R. Govindan, J. M. Hellerstein, W. Hong, S. Madden, M. Franklin, S. Shenker, The Sensor Network as a Database, USC Technical Report No. 02-771, September 2002

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