1. A Novel Method for Formally Detecting RFID Event Using Petri Nets SEKE 2011

2. Outline Introduction The Model of RFID Events The Model of ED-net ED-net models for complex events Detecting Complex Events Conclusion

3. Introduction – RFID data An RFID system : RFID tags, readers, middleware and application software. Middleware plays the primary role in RFID data management. RFID observation is of some form (epc, reader, timestamp)

4. RFID data are temporal, dynamic and in large volume, must be processed in real time RFID data are inaccurate and have implicit semantics

5. Introduction – Complex event Originated from active database. Detecting methods for RFID complex events RCEDA / SASE / QDDCatt etc. But lack formal semantics to descript complex events, which may bring ambiguity and confusion in expressing and understanding RFID complex events.

6. So, we propose a Petri net-based method named ED-net for the description of complex events in RFID which is convenient for describing temporal and parameterized constraints with locality property, token-flow mechanism and combinability.

7. The Model of RFID Events E: an event type E: an event instance t_begin(e): the start time of e t_end(e): the end time of e interval(e): t_end(e) − t_begin(e) dist(e1,e2): t_end(e2) − t_ end (e1) OR( ∨ ), AND( ∧ ), and NOT (¬)

8. The Model of ED-net Definition (ED-net) An ED-net (Event Detection Net) is a tuple N = (Σ, P, T, A, C, G, B, E) Σ is a finite set of non-empty types, called color sets; P is a finite set of non-empty places; T is a finite set of non-empty transitions; A is an arc set, A ⊆ P × T ∪ T × P; C is a color function, C : P→Σ; G is a guard function, G : T→{Expr}, where Expr is a boolean expression;

9. The Model of ED-net B is a body function, B : T→{Stat}, where Stat is a group of assignment operations; E is an arc function, E : A→{AExp}, where AExp is an expression whose value is a multi-set. The form of AExp could be: AExp : ＝ m′c|n′v|m′c + AExp|n′v + Aexp where m and n are positive integers, c is a constant value of a specific color, and v is a variable.. The sign ‘+’ means addition of two multi-sets

10. An example of ED-net model

11. Dynamic behavior of ED-net Check if a transition could be fired under current marking according to the firing rules introduced later. More than one transition may be enabled in one step, we randomly choose one to occur. Do the assignment operations according to the body of the transition, calculating the start and end time of the complex event

12. ED-net models for complex events ED-net model for disjunction

13. ED-net model for aggregation

14. Detecting Complex Events Algorithm 1: Constructing ED-Net model Input: complex events set SE Output: ED-Net model N foreach complex event e in SE do if e has not been modeled in N then Get e’s sub-events set CE; foreach sub-event ce in CE do if ce has been modeled in N then Add a place p’ as a copy of place p; else Build ce’s ED-net model; Extend N with ce’s model; end Build the ED-net model for event e (taking its sub-events as inputs); Extend N; end return SN

15. Algorithm 2: Detect complex events based on ED-net Input: ED-net model N = (Σ,P,T,A,C,G,B,E) M:Current marking of N; Enabled transition set Te = ∅ ; Repeat foreach t in T are marked do repeat Find b for variables related to t until G(t) is true under binding b; Add transition t to Te; foreach t in Te do Fire t; Evaluate output arc expressions of t foreach place p in t do M′(p)= M(p) − E(pt); end foreach place p in t do M′(p)= M(p) + E(tp); if p is a complex event Send M to applications; end foreach place p in P − (t ∪ t) do M′(p)= M(p); end Change current marking M to M′; Remove transition t from Te; end until no RFID data are received;

16. Conclusion ED-net offers unified ways for describing both temporal and parameterized constraints of events and defining rules for calculating attributes of complex events. all complex events detected in one model; common sub-events are detected only once; improving efficiency

17. Thank You!