Patent ID: 11861275
Assignee: MORGAN STATE UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. A method of optimizing the number of measurements requested in a service area, comprising:
selecting a level of uncertainty; determining a set of radio transmitters that transmit signals capable of being received in the serving area; determining a metric across the service area based on at least the geometry of each of the radio transmitters within the set with respect to a plurality of randomly distributed points across the area; and determining the number of measurements required at a location within the area based on at least the metric and the level of uncertainty, wherein the step of determining the number of measurements required at a location within the area based on at least the metric and the level of uncertainty comprises:
1. Selecting an initial number of measurements as a seed particle;
2. Passing said seed particle into a parameterized function;
a. Embedding said seed particle (for current invocation of function) as one particle in a collection of randomly initialized particles;
i. where randomization of initial particles is optionally based on proximity to the seed particle;

b. Setting a cost function to be used at a current branch level, where the cost function may remain constant across all branches or set to weight one or more terms as a function of a branch level;
i. Storing all best particle solutions at each branch level within a set;
ii. Calculating the similarity of current solution to all solutions stored within said set;
iii. Optimizing based on minimizing similarity to all solutions within said set;

c. Beginning particle swarm optimization with a set maximum number of iterations, M;
i. Resetting iteration count whenever a new optimum is found;
ii. Exiting the optimization loop when n % of M iterations has occurred (accounting for the fact that the iteration counter resets on each new optimum found);

d. Repeating steps 2a-2c, K times;
e. Selecting a best particle from a set of K particles, where K may be a function of hierarchy depth;
f. If the best solution particle is different from the seed particle and the solution particle is not too close to one or more of the previously generated best solution particles across all branches; then
i. Storing the current best solution particle;
ii. Storing a current solution score as a best branch solution if the current solution score is better than previous scores at a same branch level;
iii. Recursing the function with the current best solution particle as a new seed particle, then repeat step 2 recursively;

g. If no improvement on the best solution particle was found or duplicates the best solution found,
i. Repeating step 2e recursively with the initial seed particle provided at current invocation (repeat K times).