diff --git a/iPDC/inc/Dynamic_time_warping.h b/iPDC/inc/Dynamic_time_warping.h index 9ee0ea3..09e945e 100644 --- a/iPDC/inc/Dynamic_time_warping.h +++ b/iPDC/inc/Dynamic_time_warping.h @@ -2,4 +2,6 @@ /*pavan changes*/ int DTWfreqDistance(struct data_frame *df); -int DTWvolDistance(struct data_frame *df); \ No newline at end of file +int DTWvolDistance(struct data_frame *df); + +int DTWfreqvolDistance(struct data_frame *df); \ No newline at end of file diff --git a/iPDC/inc/Kmeans2.h b/iPDC/inc/Kmeans2.h index ff856f7..8a5d908 100644 --- a/iPDC/inc/Kmeans2.h +++ b/iPDC/inc/Kmeans2.h @@ -1,3 +1,4 @@ int *getRandoms(int lower, int upper, int count); -long double distance(struct Point* A, struct Point* B); -bool Kmeans2(struct data_frame *df); \ No newline at end of file +long double distance(struct Point *A, struct Point *B); +bool Kmeans2(struct data_frame *df); +long double distance2(struct centroid *A, struct Point *B); \ No newline at end of file diff --git a/iPDC/src/Attack_detect.c b/iPDC/src/Attack_detect.c index 783c9da..c37aa69 100644 --- a/iPDC/src/Attack_detect.c +++ b/iPDC/src/Attack_detect.c @@ -76,6 +76,7 @@ gboolean attack_detect_freq(struct data_frame *df) printf("avg freq: %Lf\n", temp->AVERAGE_OF_FREQUENCY); return TRUE; } + break; } previous = temp; temp = temp->next; @@ -174,6 +175,7 @@ gboolean attack_detect_vol(struct data_frame *df) printf("avg vol: %Lf\n", temp->AVERAGE_OF_VOLTAGE); return TRUE; } + break; } previous = temp; temp = temp->next; diff --git a/iPDC/src/Dynamic_time_warping.c b/iPDC/src/Dynamic_time_warping.c index ef4cc93..3db8a29 100644 --- a/iPDC/src/Dynamic_time_warping.c +++ b/iPDC/src/Dynamic_time_warping.c @@ -137,6 +137,7 @@ int DTWfreqDistance(struct data_frame *df) free(DTW); temp->count_track1 = 1; } + return temp->result; break; } previous = temp; @@ -154,7 +155,6 @@ int DTWfreqDistance(struct data_frame *df) previous->next = bring; return 1; } - return temp->result; } } @@ -293,6 +293,7 @@ int DTWvolDistance(struct data_frame *df) free(DTW); temp->count_track1 = 1; } + return temp->result; break; } previous = temp; @@ -310,7 +311,6 @@ int DTWvolDistance(struct data_frame *df) previous->next = bring; return 1; } - return temp->result; } } diff --git a/iPDC/src/Kmeans2.c b/iPDC/src/Kmeans2.c index 927a16d..cc32a52 100644 --- a/iPDC/src/Kmeans2.c +++ b/iPDC/src/Kmeans2.c @@ -13,12 +13,23 @@ struct Point long double minDist; // default infinite dist to nearest cluster }; +struct centroid +{ + long double x, y; + long double max_radius; + unsigned long long int count; +}; + struct Kmeans2 { int idcode; int count; struct Point *P; struct Kmeans2 *next; + struct centroid *C; + int no_of_clusters; + int isIntialized; + bool result; }; struct Kmeans2 *head_of_kmeans2 = NULL; @@ -28,6 +39,11 @@ long double distance(struct Point *A, struct Point *B) return (((A->x - B->x) * (A->x - B->x)) + ((A->y - B->y) * (A->y - B->y))); } +long double distance2(struct centroid *A, struct Point *B) +{ + return (((A->x - B->x) * (A->x - B->x)) + ((A->y - B->y) * (A->y - B->y))); +} + int *getRandoms(int lower, int upper, int count) { srand(time(0)); @@ -70,8 +86,10 @@ bool Kmeans2(struct data_frame *df) head_of_kmeans2 = (struct Kmeans2 *)malloc(sizeof(struct Kmeans2)); head_of_kmeans2->idcode = to_intconvertor(df->idcode); head_of_kmeans2->count = 0; + head_of_kmeans2->isIntialized = 0; head_of_kmeans2->next = NULL; head_of_kmeans2->P = NULL; + head_of_kmeans2->result = true; return true; } else @@ -82,12 +100,135 @@ bool Kmeans2(struct data_frame *df) { if (temp->idcode == to_intconvertor(df->idcode)) { - printf("count: %d\n",temp->count); + printf("count: %d\n", temp->count); if (temp->count == 0) { - temp->P = (struct Point *)malloc(sizeof(struct Point) * 500); + if (temp->isIntialized == 0) + temp->P = (struct Point *)malloc(sizeof(struct Point) * 500); + else + temp->P = (struct Point *)malloc(sizeof(struct Point) * 100); } - if (temp->count != 500) + if (temp->isIntialized == 1 && temp->count != 100) + { + float CURR_FREQ; + if (df->dpmu[0]->fmt->freq == '0') + { + CURR_FREQ = 50 + to_intconvertor(df->dpmu[0]->freq) * 1e-3; + } + else + { + CURR_FREQ = decode_ieee_single(df->dpmu[0]->freq); + } + float CURR_vol; + if (df->dpmu[0]->fmt->phasor == '0') + { + if (df->dpmu[0]->fmt->polar == '0') + { + unsigned char s1[2]; + unsigned char s2[2]; + strncpy(s1, df->dpmu[0]->phasors[0], 2); + strncpy(s2, df->dpmu[0]->phasors[0] + 2, 2); + long double v1 = to_intconvertor(s1); + long double v2 = to_intconvertor(s2); + CURR_vol = sqrt((v1 * v1) + (v2 * v2)); + } + else + { + unsigned char s1[2]; + strncpy(s1, df->dpmu[0]->phasors[0], 2); + CURR_vol = to_intconvertor(s1); + } + } + else + { + if (df->dpmu[0]->fmt->polar == '0') + { + unsigned char s1[4]; + unsigned char s2[4]; + strncpy(s1, df->dpmu[0]->phasors[0], 4); + strncpy(s2, df->dpmu[0]->phasors[0] + 2, 4); + long double v1 = decode_ieee_single(s1); + long double v2 = decode_ieee_single(s2); + CURR_vol = sqrt((v1 * v1) + (v2 * v2)); + } + else + { + unsigned char s1[4]; + strncpy(s1, df->dpmu[0]->phasors[0], 4); + CURR_vol = decode_ieee_single(s1); + } + } + temp->P[temp->count].x = CURR_FREQ; + temp->P[temp->count].y = CURR_vol; + temp->P[temp->count].cluster = -1; + temp->P[temp->count].minDist = __DBL_MAX__; + temp->count++; + + bool result = false; + for (int i = 0; i < temp->no_of_clusters; i++) + { + long double dist = distance2(&temp->C[i], &temp->P[temp->count - 1]); + if (dist <= temp->C[i].max_radius && dist < temp->P[temp->count - 1].minDist) + { + temp->P[temp->count - 1].cluster = i; + temp->P[temp->count - 1].minDist = dist; + result = true; + } + } + if (result == false) + temp->P[temp->count - 1].cluster = -1; + temp->result = result; + } + else if (temp->isIntialized == 1) + { + int *nPoints = (int *)malloc(sizeof(int) * temp->no_of_clusters); + long double *Sumx = (long double *)malloc(sizeof(long double) * temp->no_of_clusters); + long double *Sumy = (long double *)malloc(sizeof(long double) * temp->no_of_clusters); + + for (int i = 0; i < temp->no_of_clusters; i++) + { + nPoints[i] = 0; + Sumx[i] = 0; + Sumy[i] = 0; + } + + for (int i = 0; i < 100; i++) + { + if (temp->P[i].cluster != -1) + { + nPoints[temp->P[i].cluster]++; + Sumx[temp->P[i].cluster] += temp->P[i].x; + Sumy[temp->P[i].cluster] += temp->P[i].y; + } + } + + for (int i = 0; i < temp->no_of_clusters; i++) + { + temp->C[i].x = ((temp->C[i].count * temp->C[i].x) + Sumx[i]) / (temp->C[i].count + nPoints[i]); + temp->C[i].y = ((temp->C[i].count * temp->C[i].y) + Sumx[i]) / (temp->C[i].count + nPoints[i]); + temp->C[i].count += nPoints[i]; + } + + for (int i = 0; i < temp->no_of_clusters; i++) + { + for (int j = 0; j < 100; j++) + { + if (temp->P[j].cluster == i) + { + long double dist = distance2(&temp->C[i], &temp->P[j]); + if (temp->C[i].max_radius < dist) + temp->C[i].max_radius = dist; + } + } + } + + temp->count = 0; + free(temp->P); + free(Sumx); + free(Sumy); + free(nPoints); + } + if (temp->count != 500 && temp->isIntialized == 0) { float CURR_FREQ; if (df->dpmu[0]->fmt->freq == '0') @@ -143,7 +284,7 @@ bool Kmeans2(struct data_frame *df) temp->P[temp->count].minDist = __DBL_MAX__; temp->count++; } - else + else if (temp->isIntialized == 0) { int no_of_clusters = 5; int epochs = 20; @@ -199,33 +340,68 @@ bool Kmeans2(struct data_frame *df) Centroids[i].y = Sumy[i] / nPoints[i]; } + if (epochs == 0) + { + int count_centroids = 0; + for (int i = 0; i < no_of_clusters; i++) + { + if (!isnanl(Centroids[i].x)) + count_centroids++; + } + + temp->no_of_clusters = count_centroids; + temp->C = (struct centroid *)malloc(sizeof(struct centroid) * count_centroids); + int track = 0; + for (int i = 0; i < no_of_clusters; i++) + { + if (!isnanl(Centroids[i].x)) + { + temp->C[track].x = Centroids[i].x; + temp->C[track].y = Centroids[i].y; + temp->C[track].max_radius = 0; + temp->C[track].count = nPoints[i]; + for (int j = 0; j < 500; j++) + { + if (temp->P[j].cluster == i) + { + long double dist = distance2(&temp->C[track], &temp->P[j]); + if (temp->C[track].max_radius < dist) + temp->C[track].max_radius = dist; + } + } + track++; + } + } + } + free(nPoints); free(Sumx); free(Sumy); } temp->count = 0; - FILE *fp; - fp = fopen("kmeans.txt", "a"); + // FILE *fp; + // fp = fopen("kmeans.txt", "a"); - for (int i = 0; i < 500; i++) - { - fprintf(fp, "%Lf, %Lf, %d\n", temp->P[i].x, temp->P[i].y, temp->P[i].cluster); - } - fprintf(fp, "\n\n"); + // for (int i = 0; i < 500; i++) + // { + // fprintf(fp, "%Lf, %Lf, %d\n", temp->P[i].x, temp->P[i].y, temp->P[i].cluster); + // } + // fprintf(fp, "\n\n"); - for (int i = 0; i < no_of_clusters; i++) - { - fprintf(fp, "%d : %Lf, %Lf\n", i, Centroids[i].x, Centroids[i].y); - } + // for (int i = 0; i < no_of_clusters; i++) + // { + // fprintf(fp, "%d : %Lf, %Lf\n", i, Centroids[i].x, Centroids[i].y); + // } - fprintf(fp, "\n\n"); + // fprintf(fp, "\n\n"); - fclose(fp); + // fclose(fp); free(Centroids); free(temp->P); + temp->isIntialized = 1; } - return true; + return temp->result; break; } previous = temp; @@ -237,7 +413,9 @@ bool Kmeans2(struct data_frame *df) bring->idcode = to_intconvertor(df->idcode); bring->count = 0; bring->next = NULL; + bring->isIntialized = 0; bring->P = NULL; + bring->result = true; previous->next = bring; return true; } diff --git a/iPDC/src/map_vis.c b/iPDC/src/map_vis.c index b462262..c9c364b 100644 --- a/iPDC/src/map_vis.c +++ b/iPDC/src/map_vis.c @@ -129,13 +129,13 @@ gboolean update_images(gpointer* pars){ } }else if(curr_measurement == 3){ if(algorithm==0 && dimmension == 0){ - if (!attack_detect_vol(df)){ + if (!attack_detect_freq(df)){ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); }else{ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); } }else if (algorithm==0 && dimmension == 1){ - if (!attack_detect_freq(df)){ + if (!attack_detect_vol(df)){ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); }else{ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); @@ -161,19 +161,23 @@ gboolean update_images(gpointer* pars){ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); } }else if (algorithm==2 && dimmension == 0){ - if(!DTWvolDistance(df)){ - vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); - }else{ - vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); - } - }else if (algorithm==2 && dimmension == 1){ if(!DTWfreqDistance(df)){ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); }else{ vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); } + }else if (algorithm==2 && dimmension == 1){ + if(!DTWvolDistance(df)){ + vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); + }else{ + vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); + } }else if (algorithm==2 && dimmension == 2){ - + if(!DTWfreqvolDistance(df)){ + vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image); + }else{ + vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_green_image); + } } } }