modeling mobile-agent-based collaborative processing in sensor networks using generalized stochastic...
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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
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.
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