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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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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
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
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
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
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 Design flow Structure of the top level
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.