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Hongtao Du, Hairong Qi, Gregory Peterson Department of Electrical and Computer Engineering

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).

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Hongtao Du, Hairong Qi, Gregory Peterson Department of Electrical and Computer Engineering

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  1. 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

  2. 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

  3. 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

  4. GSPN Model for MADSN

  5. GSPN Model of Sensor Side

  6. 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

  7. 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 Joint Entropy

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

  9. 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.

  10. 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

  11. Synthesis Procedure • Top level • Configure and interconnect re-configurable components • Register • Transition Selector • Conflict Controller Design flow Structure of the top level

  12. Conflicts Selection Comparison First 10 transitions Overall transitions

  13. Number of Tokens at Different Time Random selector ER3 selector

  14. 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.

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