Data centric Storage In Sensor networks Based on Balaji Jayaprakash’s slides

Overview of the Seminar Introduction Keywords and Terminology Existing Schemes Why Data centric Storage? Assumptions Geographic Hash table Comparitive Study Conclusion

Introduction Sensornet ♦ A distributed network comprised of a large number of small sensing devices equipped with Computation Communication Storage ♦ Great volume of data Data Dissemination Algorithm ♦ Energy efficient ♦ Scalable ♦ Self-organizing

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

Total Usage /Hotspot Usage Total Usage Total number of packets sent in the Sensor network Hotspot Usage The maximal number of packets send by a particular sensor node

Existing schemes for Storage External Storage (ES) Local Storage (LS) Data Centric Storage (DCS)

External Storage (ES) External storage event

Local Storage (LS) event

Why do we need DCS? Scalability Robustness against Node failures and Node mobility To achieve Energy-efficiency

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

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

Data centric Storage Elephant Sighting source:lass.cs.umass.edu

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

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

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

Retrieving data in GHT Get (“elephant”) Hash (‘elephant’)=(12,24) (12,24)

Geographic Hash Table Node A Node B

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 )

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

Home Node and Home perimeter In GHT packet is not addressed to specific node but only to a specific location, hence only perimeter mode is used The packet will traverse the entire perimeter that encloses the destination before being consumed at the home node (the node closest to destination)

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

Solutions to the problems Perimeter refresh protocol Structured Replication

Perimeter refresh protocol Replicates stored data for key k at nodes around the location to which k hashes, and ensures that one node is chosen consistently as the home node for that “K” –consistency & persistence By hashing keys, GHT spreads storage and communication load between different keys evenly throughout the sensornet

Perimeter Refresh Protocol E F B D A C L E D F C B L home Replica home Replica

Time Specifications Refresh time (T h ) Take over time (T t ) Death time (T d ) General rule T d >T h and T t >T h In GHT Td=3Th and Tt=2Th

Characteristics Of Refresh Packet Refresh packet is addressed to the hashed location of the key Every (T h ) 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

Structured Replication Too many events are detected then home node will become the hotspot of communication. Hierarchical decomposition of the key space Structured replication reduces the cost of storage and is useful for frequently detected events.

Comparative Study Comparison based on Cost Comparison based on Total usage and Hot spot usage

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

Comparison based on Cost CostExternal storage (ES) Local storage (LS) Data-centric storage Cost for Storage O(n) 0 Cost for query 0 O(n) Cost for Response 0 O(n)

Comparison based on Hot-spot/Total Usage n - Number of nodes T - Number of Event types Q – Number Of Event types queried for D total – Total number of detected events D Q - Number of detected events for queries

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)

Comparison Study – contd.. ESLSDCS Total Hot spot

Observations from the Comparison DCS is preferable only in cases where Sensor network is Large There are many detected events and not all even types queried D total >>max(D q, Q)

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

Simulation Setup ns-2 Node Density – 1node/256m 2 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

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

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

Simulation Results under varying Q Number of nodes is constant= 10000

Simulation results under varying N Number of Queries Q =50

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”

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

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 2003Dimensions: why do we need a new data handling architecture for sensor networks? Data-centric storage in sensornets 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 2003Data-centric storage in sensornets with GHT, a geographic hash table 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 2003Directed diffusion for wireless sensor networking R. Govindan, J. M. Hellerstein, W. Hong, S. Madden, M. Franklin, S. Shenker, The Sensor Network as a Database, USC Technical Report No , September 2002 The Sensor Network as a Database