RFID Middleware Design: Optimal Scheduling RFID Reader

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RFID Middleware Design Optimal Scheduling RFID
Reader Networks Based on Swarm Intelligence

• Hanning Chen

October 28nd, 2006
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Outline

• Introduction
• A brief review of PSO and B- PSO
• RFID Readers Scheduling and GPP
• Optimal Scheduling for RFID Reads networks
• Conclusions

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Introduction

• RFID middleware design
• Scheduling Problem of RFID reader networks
• construction of GPP using evolutionary algorithm
  
• Our method

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Particle Swarm Optimization (PSO)

• Particle Swarm Optimization (PSO) applies to
  concept of social interaction to problem solving.
• It was developed in 1995 by James Kennedy and
  Russ Eberhart Kennedy, J. and Eberhart, R.
  (1995). Particle Swarm Optimization,
  Proceedings of the 1995 IEEE International
  Conference on Neural Networks, pp. 1942-1948,
  IEEE Press.
• It has been applied successfully to a wide
  variety of search and optimization problems.
• In PSO, a swarm of n individuals communicate
  either directly or indirectly with one another
  search directions (gradients).
• PSO is a simple but powerful search technique.

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PSO Velocity Update Equations
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RFID Readers Scheduling and GPP

• Given a collection of RFID readers laid out in
  some manner, we can construct the associated
  conflicting graph G (V,E) where each vertex v ?
  V corresponds to a RFID reader and each edge e ?
  E indicates that those two sensors can be
  operated in parallel. In other words there are no
  constraints between these two readers. For
  example, the conflicting graph corresponding to
  the RFID reader layout of Figure a is given in
  Figure b.
• Readers in any given partition of the conflicts
  graph can read simultaneously without
  interference. Thus it makes sense to fire every
  reader in a partition when firing one reader in
  the partition.
• Now the optimal schedule can be determined by
  finding the maximum partition and partitioning
  the graph into partitions.

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RFID Readers Scheduling and GPP
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Optimal Scheduling for RFID Readers networks

• (1) Particle representation
• In our work the direct encoding scheme is
  applied to encode the individuals. The dimension
  of each particle is set as equal to the number of
  sensor reader N. Each element in the dimension
  is corresponding to the absence of particular
  readers, whose entries can only be 0 or 1. A
  bit 0 in an individual indicated the absence of
  the corresponding reads. Otherwise a bit 1 in
  an individual indicated the presence of the
  corresponding reads. For example, a particles
  current position is 001101. It denotes the 6
  reads in our system and 1 implies presence of
  that particular sensor in the clique which the
  particle is representing.
• (2)Initialization
• Initially M individuals forming the
  population should be randomly generated and each
  consists of N parameters. These individuals may
  be regard as particles in terms of PSO. In
  addition, the learning parameters, such as and ,
  inertia weight should be assigned in advance.

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Optimal Scheduling for RFID Readers networks

• (3) Fitness function design
• To evaluate the performance of an
  individual, a predefined fitness function should
  be formulated. The fitness function takes into
  account four parameters
• The f is calculated as the reciprocal of
  C as follows
• Where N is number of sensors, T is the
  transaction time of the partition, W is the
  weight attached to this group of readers.
  are the weights given to each one
  of them and the importance of each one of them
  differed.
• The transaction time for a partition can
  be calculated as
• Where is the transaction time of the ith
  member (reader) that forming the partition.C is
  the summation of all the possible conflicts that
  the members of the clique have with the nodes
  still remaining in the graph to be partitioned.It
  should be noted that the four parameters in cost
  function should be normalized this normalization
  is done after merging the pbest and the present
  vectors together.

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Optimal Scheduling for RFID Readers networks

• (4) Update dependencies and transaction time
• The velocity and position are updated
  according to Eqs above. After this step the
  individuals associated with both the dependencies
  and transactions times are updated to produce new
  best-performing individuals.
• (5) Termination condition
• The proposed algorithm is performed until
  the Fitness is small enough, or a pre-determined
  number of epochs is passed. It is expected that,
  after a certain number of iterations, all the
  reader will grouped and the optimal group can be
  obtained.

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Pseudocode for implementing our algorithm

• Begin
• Generate random population of N particles, i.e.
  the initial transaction times and conflicts
  should be given
• For each individual i1 N
• calculate fitness value ()
• end
• For each particle i 1 N
• Set pBest as the best position of particle i
• If fitness value () is better than pBest
• pBest(i)f(i)
• End
• Set gBest as the best fitness of all particles
• For each particle
• Calculate particle velocity and position
  according to Eqs.(1-4)
• End
• Check if termination is true
• End

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Conclusions and Future Work
This paper is devoted to giving a new
strategy for optimal scheduling of RFID read
networks. A swarm intelligence based algorithm,
binary particle swarm optimization is employed to
search through space for an optimization
problem. In the future work, some improved
swarm intelligence based algorithm or artifical
life methodology can be incorporated to solve the
problem of optimal scheduling of RFID read
networks. By this way, the robust and powerful
function of RFID middleware can be achieved. The
insights presented in this paper will be
certainly found to be useful in our RFID Lab. In
fact the experiment environment has been setup
and some primary results will be given. Due to
the limit of the conference date all those will
be done in our future work.
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Thanks
Email chenhanning_at_sia.cnADDRESS Shenyang
Institute of Automation, Chinese Academy of
Sciences, Shenyang, ChinaPOSTCODE 110016