Dear Community:
Can any one suggest me about the use of "Genetic Algorithm" Optimization. Is this a good start for finding the positioning of sensor nodes in indoor localization ?
I agree that GA is suitable for the solution of this task best of all. However, in the beginning it is necessary to make the formal problem definition. It is necessary to answer questions: (1) what will be variables? (2) what will be goal function? It is good if variables are the discrete or even Boolean, but it is not mandatory. Variables will form a basis of creation of chromosomes in GA. Goal function will be a prototype for fitness function in GA. Remaining details of algorithm implementation you can be found in any manual on GA.
GA may work very fast, but it does not give any performance guarantees. Personally, I prefer other methods for WSN localization.
In my experience, GA is the best for combinatorial optimization problems. I think PSO algorithm (or even HS) is a better choice for exploring real-valued search spaces.
Genetic algorithm (GA) is used for optimizing the sensor node distribution:
We define position area sensing parameter. Firstly object area is to be discrete by some precision; a point represents a little local area. According to geographical environment parameter and corresponding radio propagation model, every node is assigned a sensing parameter; different sensing parameter means that the sensor node at this point has different available sensing range. Optimal node translates into that how to gain the best coverage in the object area.
According to this problem, genetic algorithm by using the code mapping of optimal sensor node distribution is used:
Aimed at the problem of sensor node distribution in wireless sensor network, we adopt a coding scheme based on node coordinate. Sensor node position in object area is replaced by its coordinate. Using GPS, the sensor network can be organized by the translating from node coordinate to longitude and latitude. These gene expressions match the practice project and can reflect applied environment peculiarity of sensor node intuitively. If every chromosome of the individual is composed of N genes in Genetic algorithm solution space, every gene represents the position of a sensor node.
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