Event query processing based on data-centric storage in wireless sensor networks Longjian Guo, Yingshu Li, and Jianzhong Li IEEE GLOBECOM Technical Conference (GLOBECOM 2006)

Outline  Introduction  Event formulation & storage  Event query processing  Simulation  Conclusion

Introduction  More interested in the events  Event queries more complex queries with multiple attributes  In Data-centric storage an event stores at nearest to hashing position where the Home Node should be to save more energy

Event formulation  Observation attribute A i (i=1 … n) refers to an attribute a sensor observes  Round a constant of time during which a sensor samples a value  Observation zone The monitored area divided into m*n grids Each grid is an observation zone

Event formulation  Event type A group of pre-defined observation attribute ET i (A i 1, A i 2, …,A ij ) is an event type fire= ET(Tmpt, Smk, Hudit) = (Tmpt>t1) ∧ (Smk>s2) ∧ (Hudit<h3)  Event Presented e(ET, g, t)  Event query Presented by Q{[t 1, t 2 ], ET i }

Preliminaries Red-ET 1 Green-ET 2 Blue-ET Some nodes to observe the attributes A i1,A i2, …, A ij are called the observation nodes of ET i Within each observation zone, every node S ij broadcasts a message (ET i, ID, pos.) j= (k mod k i )+1, k th round A node S ij is selected from S i1, S i2, …,S iki as the event fusion node Event fusion node Checks whether the event has happened using the predicate defined for event type

Event storage  The event storage into three categories: External storage  The sink is the event storage node  An event fusion node route the events to sink Local storage  The event storage node is an event fusion node Data centric storage  A hash function to position (x, y)  The nodes nearest to that position will storage the event  How to save more energy

Center Mapping Data Centric Storage (CM-DCS) Z1Z1 Z2Z2 Z3Z3 Z4Z4 Z5Z5 Z6Z6 Z7Z7 Z8Z8 Z9Z9 Z 10 Z 11 Z 12 Z 13 Z 14 Z 15 Z 16 ET 1 ET 2 ET 3 ET 4 Event storage node ET 1 → Z 6 ; ET 2 → Z 7 ; ET 3 → Z 10 ; ET 4 → Z 11

Center Mapping Data Centric Storage (CM-DCS)  Lemma 1 ： The total energy consumption  Routing events from an event fusion node to an storage node  Proportional to the distance between the event fusion node and the event storage node

Center Mapping Data Centric Storage (CM-DCS)  Lemma 2: If observation nodes are distributed uniformly The position of the event fusion node is the center of the observation zone (a, b) (c, d) Two dimensional uniform distribution U(a, c, b, d) The expectation of X f and Y f : E( X f )=0.5(a+c) E(Y f )=0.5(b+d) (E(X f ), E(Y f ))

Center Mapping Data Centric Storage (CM-DCS)  Theorem 1: If nodes are distributed uniformly The event storage nodes are located near the center of the sensor network  The energy consumption for routing events from an event fusion node to an event storage node is minimizedFormula: Fusion node Storage node Fusion node:(X 1, Y 1 ),(X 2,Y 2 ), … (X i,Y i ) Storage node:(X,Y)

Time-stamped vector-based storage strategy Z1Z1 Z2Z2 Z3Z3 Z4Z4 Z5Z5 Z6Z6 Z7Z7 Z8Z8 Z9Z9 Z 10 Z 11 Z 12 Z 13 Z 14 Z 15 Z 16 T1: T1: T2: ET: fire T3: T4: Query: ET: fire T: 3~4 T4: T3: OR result: Storage space reached the upper bound

Event query processing based on CM- DCS  Composed of four phases Phase 1 Phase 1  Deciding the routing destination Phase 2 Phase 2  Routing query Q{[t1, t2], ETi} Phase 3 Phase 3  Answering query Q  ComputesI p =  Computes I p = Phase 4 Phase 4  Routing the query result back to the sink Z1Z1 Z2Z2 Z3Z3 Z4Z4 Z5Z5 Z6Z6 Z7Z7 Z8Z8 Z9Z9 Z 10 Z 11 Z 12 Z 13 Z 14 Z 15 Z 16 ET 1 → Z 6 ; ET 2 → Z 10 ; ET 3 → Z 11 ; ET 4 → Z 7 sink

Event query processing based on Local storage  Composed of four phases Phase 1 Phase 1  Query dissemination  A routing tree rooted at sink Phase 2 Phase 2  Collection of children ’ s IDs  Node p broadcasts information to its neighbors Phase 3 Phase 3  Combination of query results Phase 4 Phase 4  Routing the query result back to the sink

Simulation

Simulation — Effect of node density on energy consumption The sink locates close to (0,0), prob=0.5, W=4, N q =5.

Simulation — Effect of the number of sensors on energy consumptions Node density is fixed at 8nodes/314m 2, prob=0.5 W=4, Nq=5.

Simulation — Effect of the number of event types on energy consumptions The monitor area is 127*127, N=720, prob=0.5, Nq=5.

Simulation — Effect of the number of queries on energy consumptions The monitor area is 127*127, N=720, prob=0.5, W=4.

Simulation — Effect of event occurring probability on energy consumptions The monitor area is 127*127, N=720, prob=0.5, W=4.

Conclusion  Propose a Center Mapping Data Centric Storage (CM-DCS)  Considering some important factors Node density The number of sensors, event types, and queries Event occurring probability  Design two distributed event query processing algorithms