Modeling Mobile-Agent-based Collaborative Processing in SensorNetworks Using Generalized

Stochastic Petri Nets

Hongtao Du, Hairong Qi, Gregory Peterson

Department of Electrical and Computer Engineering University of Tennessee, USA

Mobile-Agent-based Distributed Sensor Networks (MADSNs)

Sensors Have sensing, processing and

communication capabilities Independently sense the

environment and process data locally

Collaborate with each other to fulfill complex task

Mobile agents Dispatched from the processing

center to the sensor nodes Fuse local results during migration Perform collaborative information

processing

MADSN computing model

Generalized Stochastic Petri Net (GSPN)

GSPN Advantage: modeling features of concurrency,

synchronization and randomness. Suitable for characteristics of MADSN GSPN:= (P, T, I, O, M, SI)

P: places T: transitions

I: input arc connections O: output arc connections

M: number of tokens SI: time delay of transitions

Mobile agents in distributed sensor network 1 processing element (server) and 5 sensor nodes

GSPN Model for MADSN

GSPN Model of Sensor Side

Challenging in GSPN Modeling

Deadlock avoidance and transition selection Random selector

Our solution – ER3 transition selector Joint Entropy

Measures uncertainty of mobile agent’s migration Rolling Rocks Random Selector

Keeps fairness in transition selection

Joint Entropy

Assume the probability of a mobile agent Migration success rate: 0.9, failure rate: 0.1

Joint Entropy

denotes a mobile agent migrating to the node,

Entropy rate

Gives priority to the mobile agents with higher returning probability

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Rolling Rocks Random (R3) Selector

Each rock (random number) has a weight between 0 and 1.

Multiple transitions conflict: multi-end seesaw

OldNewWinner RRRR )1(

(a) (b)

(c) (d)

ER3 Transition Selector

: the total amount of sensor nodes

: the joint entropy

: the rock weight associated with each transition,

: the number of tokens in the input place of the transition

The transition associated with the largest will be fired.

TokensRockseNode NRRNR )(

NodeN

TokensN

RocksReR

10 RocksR

R

Field Programmable Gate Array (FPGA) FPGA

Provides faster, real-time solutions

Re-configurable components at logic level 50% more time to test and verify the code 70% or more design time reduction Reduce design risk and cost For this GSPN model

3 timed and 5 immediate transition components

Synthesis Procedure

Top level Configure and interconnect

re-configurable components

Register Transition Selector Conflict Controller

Structure of the top level

Design flow

Conflicts Selection Comparison

First 10 transitions Overall transitions

Number of Tokens at Different Time

Random selector ER3 selector

Conclusions

GSPN provides a modeling tool for mobile-agent-based sensor network.

ER3 transition selector for GSPN Maximizes the modeling efficiency Balances the queue length

Synthesizing GSPN on FPGAs is a solution for complex simulations Re-configurable components improve the implementation

efficiency. More re-configurable components will be developed.