RFID Middleware Design: Optimal Scheduling RFID

Reader Networks Based on Swarm Intelligence

Hanning Chen

October 28nd, 2006

Outline

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

networks Conclusions

Introduction

RFID middleware design Scheduling Problem of RFID reader

networks construction of GPP using evolutionary

algorithm Our method

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.

PSO Velocity Update Equations

newid

oldid

newid

idgdididoldidi

newid

vxx

xprandcxprandcvwv

)()( 2211

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.

RFID Readers Scheduling and GPP

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 particle’s 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 generate

d 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.

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.

Optimal Scheduling for RFID Readers networks

(4) Update dependencies and transaction time The velocity and position are updated according to Eqs a

bove. 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.

Pseudocode for implementing our algorithm Begin;Generate random population of N particles, i.e. the initial transaction times and co

nflicts should be given;For each individual i=1: 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

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.

ThanksEmail: chenhanning@sia.cnADDRESS: Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, ChinaPOSTCODE: 110016