A Novel Method for Formally Detecting RFID Event Using Petri Nets

SEKE 2011

Outline

• Introduction

• The Model of RFID Events

• The Model of ED-net

• ED-net models for complex events

• Detecting Complex Events

• Conclusion

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)

• RFID data are temporal, dynamic and in large volume, must be processed in real time

• RFID data are inaccurate and have implicit semantics

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.

• 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.

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 (¬)∨ ∧

The Model of ED-net• Definition (ED-net) An ED-net (Event Detection Net) is a tup

le 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;

The Model of ED-netB 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

An example of ED-net model

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

ED-net models for complex events

ED-net model for disjunction

ED-net model for aggregation

Detecting Complex EventsAlgorithm 1: Constructing ED-Net model Input: complex events set SEOutput: 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 end Build the ED-net model for event e (taking its sub-events as inputs); Extend N; end endreturn SN

Algorithm 2: Detect complex events based on ED-netInput: 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; enduntil no RFID data are received;

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

Thank You!