Modeling Mobile-Agent-based Collaborative Processing in Sensor Networks Using Generalized Stochastic Petri Nets Hongtao Du, Hairong Qi, Gregory Peterson.

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Presentation transcript:

Modeling Mobile-Agent-based Collaborative Processing in Sensor Networks 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

Rolling Rocks Random (R3) Selector Each rock (random number) has a weight between 0 and 1. Multiple transitions conflict: multi-end seesaw (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.

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 transitionsOverall transitions

Number of Tokens at Different Time Random selectorER3 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.