def __init__(self, settings):
self.settings = settings
filename_beacons = self.settings.DATA_FILE_PATH + self.settings.BEACON_PLACEMENT_FILE
# loading locations of BLE beacon devices
self.beacon_list = {}
file_beacons = open(filename_beacons, "r")
next_beacon_id = 0
for line in file_beacons.readlines():
elements = line.strip().split(",")
if int(elements[3]) == 1:
beacon_id = next_beacon_id
next_beacon_id += 1
x_pos = int(elements[1])
y_pos = int(elements[2])
self.beacon_list[beacon_id] = (x_pos, 24-y_pos)
filename_walls = self.settings.DATA_FILE_PATH + "walls.csv"
# loading walls
self.list_walls = []
file_walls = open(filename_walls, "r")
for line in file_walls.readlines():
elems = line.strip().split(",")
self.list_walls.append(((int(elems[1]), 24-int(elems[2])), (int(elems[3]), 24-int(elems[4]))))
# loading an external Java class
self.policy_calculator = PolicyCalculator()
class TrajectoryIdentification:
def __init__(self, settings):
self.settings = settings
filename_beacons = self.settings.DATA_FILE_PATH + self.settings.BEACON_PLACEMENT_FILE
# loading locations of BLE beacon devices
self.beacon_list = {}
file_beacons = open(filename_beacons, "r")
next_beacon_id = 0
for line in file_beacons.readlines():
elements = line.strip().split(",")
if int(elements[3]) == 1:
beacon_id = next_beacon_id
next_beacon_id += 1
x_pos = int(elements[1])
y_pos = int(elements[2])
self.beacon_list[beacon_id] = (x_pos, 24-y_pos)
filename_walls = self.settings.DATA_FILE_PATH + "walls.csv"
# loading walls
self.list_walls = []
file_walls = open(filename_walls, "r")
for line in file_walls.readlines():
elems = line.strip().split(",")
self.list_walls.append(((int(elems[1]), 24-int(elems[2])), (int(elems[3]), 24-int(elems[4]))))
# loading an external Java class
self.policy_calculator = PolicyCalculator()
# calculate the number of combinations (nCr)
def ncr(self, n, r):
r = min(r, n-r)
if r == 0: return 1
numer = reduce(op.mul, xrange(n, n-r, -1))
denom = reduce(op.mul, xrange(1, r+1))
return numer//denom
# radio reception model; should be replaced by more realistic one
def beaconReceptionProbability(self, distance):
if distance < self.settings.RADIO_RANGE:
return self.settings.BEACON_RECEPTION_PROBABILITY
else:
return 0.001 # very small value; intending to avoid zero-division
# check the presence of line-of-sight between a client and a beacon device
def checkLOS(self, node_pos, neighbor_pos, walls):
x1 = node_pos[0]
y1 = node_pos[1]
x2 = neighbor_pos[0]
y2 = neighbor_pos[1]
for wall in walls:
x3 = wall[0][0]
y3 = wall[0][1]
x4 = wall[1][0]
y4 = wall[1][1]
ta = (x3-x4)*(y1-y3)+(y3-y4)*(x3-x1)
tb = (x3-x4)*(y2-y3)+(y3-y4)*(x3-x2)
tc = (x1-x2)*(y3-y1)+(y1-y2)*(x1-x3)
td = (x1-x2)*(y4-y1)+(y1-y2)*(x1-x4)
if ta * tb < 0 and tc * td < 0:
return False
return True
# main function for trajectory identification
def trajectory_identification(self, ground_truth_trajectory, consider_walls=True):
# just some aliases
TIME_STEP = self.settings.TIME_STEP
SIMULATION_TIME = self.settings.SIMULATION_TIME
WINDOW_SIZE = self.settings.WINDOW_SIZE
output_filename = self.settings.OUTPUT_FILE_PATH + self.settings.OUTPUT_FILE_NAME
output_file = open(output_filename, "w")
beacon_list = self.beacon_list
output_file.write("n_nodes=%d, consider_walls=%s, n_beacons=%d, beacon_reception_probability=%f, likelihood_threshold=%f, random_seed=%d, time_step=%d, window_size=%d, " % (self.settings.N_NODES, consider_walls, len(beacon_list), self.settings.BEACON_RECEPTION_PROBABILITY, self.settings.LIKELIHOOD_THRESHOLD, self.settings.RANDOM_SEED, self.settings.TIME_STEP, self.settings.WINDOW_SIZE))
output_file.write("k=")
for k in self.settings.PARAM_K:
output_file.write(" %d" % k)
output_file.write(", w_max=")
for w_max in self.settings.MAX_SET_SIZE:
output_file.write(" %d" % w_max)
output_file.write("\n")
if consider_walls:
list_walls = self.list_walls
print list_walls
else:
list_walls = []
beacon_reception_probability = HashMap()
for trajectory_id in ground_truth_trajectory.keys():
beacon_reception_probability.put(trajectory_id, HashMap())
for beacon_id in beacon_list.keys():
beacon_reception_probability.get(trajectory_id).put(beacon_id, HashMap())
# set of beacons that are received by each client
received_beacons = {}
for trajectory_id in ground_truth_trajectory.keys():
received_beacons[trajectory_id] = set([])
decryptability_table = {}
for current_time in range(0, SIMULATION_TIME, TIME_STEP):
param_id = (current_time/TIME_STEP) % len(self.settings.PARAM_K)
k = self.settings.PARAM_K[param_id]
max_size_w = self.settings.MAX_SET_SIZE[param_id]
print "time=", current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), ", k=", k
for trajectory_id in ground_truth_trajectory.keys():
# simulating wireless communication between BLE beacons
for beacon_id in beacon_list.keys():
if self.checkLOS(ground_truth_trajectory[trajectory_id][current_time], beacon_list[beacon_id], list_walls):
distance = math.sqrt((ground_truth_trajectory[trajectory_id][current_time][0] - beacon_list[beacon_id][0])**2 + (ground_truth_trajectory[trajectory_id][current_time][1] - beacon_list[beacon_id][1])**2)
beacon_reception_probability.get(trajectory_id).get(beacon_id).put(current_time, self.beaconReceptionProbability(distance))
if random.random() < beacon_reception_probability.get(trajectory_id).get(beacon_id).get(current_time):
signature = Vector()
signature.add(beacon_id)
signature.add(current_time)
received_beacons[trajectory_id].add(signature)
else:
beacon_reception_probability.get(trajectory_id).get(beacon_id).put(current_time, 0.0)
if current_time - TIME_STEP * len(self.settings.PARAM_K) * WINDOW_SIZE >= 0:
beacon_reception_probability.get(trajectory_id).get(beacon_id).remove(current_time - TIME_STEP * len(self.settings.PARAM_K) * WINDOW_SIZE)
# trajectory identification phase
if current_time < WINDOW_SIZE * TIME_STEP * len(self.settings.PARAM_K):
continue
if current_time % (TIME_STEP * len(self.settings.PARAM_K)) == 0:
decryptability_table[current_time] = {}
for target_trajectory_id in ground_truth_trajectory.keys():
# decryptability_table[t][i][j] is True if the client i can decrypt trajectory j at time t
if current_time % (TIME_STEP * len(self.settings.PARAM_K)) == 0:
decryptability_table[current_time][target_trajectory_id] = {}
for trajectory_id in ground_truth_trajectory.keys():
decryptability_table[current_time][target_trajectory_id][trajectory_id] = False
# set of all the location signatures that the client has received during the recent ${WINDOW_SIZE} time steps
all_location_signatures = []
for beacon_id in beacon_list.keys():
for time in range(current_time - (WINDOW_SIZE - 1) * (TIME_STEP * len(self.settings.PARAM_K)), current_time + 1, TIME_STEP * len(self.settings.PARAM_K)):
if beacon_reception_probability.get(target_trajectory_id).get(beacon_id).get(time) > self.settings.MIN_RECEPTION_PROBABILITY:
aposteriori_prob = beacon_reception_probability.get(target_trajectory_id).get(beacon_id).get(time) / sum([beacon_reception_probability.get(trajectory_id).get(beacon_id).get(time) for trajectory_id in ground_truth_trajectory.keys()])
signature = Vector()
signature.add(beacon_id)
signature.add(time)
all_location_signatures.append((signature, aposteriori_prob))
#
# selecting the set W (this process is off-loaded to Java code)
#
all_location_signatures.sort(key=lambda a: a[1], reverse=1)
all_location_signatures = [elem[0] for elem in all_location_signatures]
if len(all_location_signatures) < k:
print "trajectory_id=", target_trajectory_id, ", time=", current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), ", k=", k, ", W= N/A"
output_file.write("0,%d,%d,%d,N/A,N/A\n" % (target_trajectory_id, current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), k))
else:
size_of_W = self.policy_calculator.calcPolicy(current_time, all_location_signatures, beacon_reception_probability, target_trajectory_id, self.settings.LIKELIHOOD_THRESHOLD, k, min(len(all_location_signatures), max_size_w))
#
# Selection of the set W (Python version) (slow; it seems that overhead for reference of dictionary values significantly affects the total execution time)
#
# size_of_W = max_size_w
# size_of_W = k
# for set_size in range(k, len(all_location_signatures)+1):
# sum_likelihood = 0.0
# target_likelihood = 0.0
# for trajectory_id in ground_truth_trajectory.keys():
# probability = 1.0
# for signature_idx in range(set_size-k, set_size):
# probability *= beacon_reception_probability.get(trajectory_id).get(all_location_signatures[signature_idx][0]).get(all_location_signatures[signature_idx][1])
# if trajectory_id == target_trajectory_id:
# target_likelihood = probability
# sum_likelihood += probability
# aposteriori_prob = target_likelihood / sum_likelihood
#
# if aposteriori_prob > self.settings.LIKELIHOOD_THRESHOLD:
# size_of_W = set_size
# else:
# break
# for set_size in reversed(range(k, )):
# for k_set in itertools.combinations(range(set_size), k):
# if not aposteriori_prob.has_key(k_set):
# likelihood = {}
# for trajectory_id in ground_truth_trajectory.keys():
# probability = 1.0
# for signature_idx in k_set:
# probability *= beacon_reception_probability.get(trajectory_id).get(all_location_signatures[signature_idx][0]).get(all_location_signatures[signature_idx][1])
# likelihood[trajectory_id] = probability
# aposteriori_prob[k_set] = likelihood[target_trajectory_id] / sum(likelihood.values())
# # aposteriori_prob[k_set] = 0.9
#
# if aposteriori_prob[k_set] < self.settings.LIKELIHOOD_THRESHOLD:
# is_valid = False
# break
if size_of_W > 0:
print "trajectory_id=", target_trajectory_id, ", time=", current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), ", k=", k, ", |W|=", size_of_W
output_file.write("0,%d,%d,%d,%d," % (target_trajectory_id, current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), k, size_of_W))
output_file.write("\n")
for trajectory_id in ground_truth_trajectory.keys():
if len(set(all_location_signatures[0:size_of_W]).intersection(received_beacons[trajectory_id])) >= k:
decryptability_table[current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K))][target_trajectory_id][trajectory_id] = True
else:
print "trajectory_id=", target_trajectory_id, ", time=", current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), ", k=", k, ", W= N/A"
output_file.write("0,%d,%d,%d,N/A,N/A\n" % (target_trajectory_id, current_time / (TIME_STEP * len(self.settings.PARAM_K)) * (TIME_STEP * len(self.settings.PARAM_K)), k))
for time in decryptability_table.keys():
for target_trajectory_id in ground_truth_trajectory.keys():
for trajectory_id in ground_truth_trajectory.keys():
output_file.write("1,%d,%d,%d,%s\n" % (time, target_trajectory_id, trajectory_id, decryptability_table[time][target_trajectory_id][trajectory_id]))
# calculating performance statistics
n_true_positives = 0
n_false_positives = 0
n_true_negatives = 0
n_false_negatives = 0
for time in decryptability_table.keys():
for target_trajectory_id in ground_truth_trajectory.keys():
for trajectory_id in ground_truth_trajectory.keys():
if target_trajectory_id == trajectory_id:
if decryptability_table[time][target_trajectory_id][trajectory_id]:
n_true_positives += 1
else:
n_false_negatives += 1
else:
if decryptability_table[time][target_trajectory_id][trajectory_id]:
n_false_positives += 1
else:
n_true_negatives += 1
print "accuracy=", float(n_true_positives + n_true_negatives) / (n_true_positives + n_false_positives + n_true_negatives + n_false_negatives)
precision = float(n_true_positives) / (n_true_positives + n_false_positives)
recall = float(n_true_positives) / (n_true_positives + n_false_negatives)
f_measure = 2*precision*recall / (precision+recall)
print n_true_positives, n_true_negatives, n_false_positives, n_false_negatives
output_file.write("2,%f,%f,%f,%d,%d,%d,%d\n" % (precision, recall, f_measure, n_true_positives, n_true_negatives, n_false_positives, n_false_negatives))
output_file.close()
# selection of W by depth-first search; super slow :(
# if len(all_location_signatures) < self.settings.PARAM_K:
# print "trajectory_id=", target_trajectory_id, ", time=", current_time, ", W= NaN"
# else:
# valid_combinations = []
# for k_set in itertools.combinations(range(len(all_location_signatures)), self.settings.PARAM_K):
# likelihood = {}
# for trajectory_id in ground_truth_trajectory.keys():
# probability = 1.0
# for signature_idx in k_set:
# probability *= beacon_reception_probability[trajectory_id][all_location_signatures[signature_idx][0]][all_location_signatures[signature_idx][1]]
# likelihood[trajectory_id] = probability
# aposteriori_prob = likelihood[target_trajectory_id] / sum(likelihood.values())
# if aposteriori_prob > 0.8:
# label = 0
# for i in range(len(all_location_signatures)):
# if i in k_set:
# label += pow(2, i)
# valid_combinations.append(label)
#
# stack = ["1", "0"]
# visited = []
# best_label = ""
# while len(stack) > 0:
# label = stack.pop(0)
# if label not in visited:
# visited.append(label)
# if label.count("1") < self.settings.PARAM_K:
# if len(label) < len(all_location_signatures):
# stack = [label + "1", label + "0"] + stack
# else:
# num_valid_combinations = 0
# target_label = sum([pow(2, i) for i in range(len(label)) if label[i] == "1"])
# for valid_combination in valid_combinations:
# if bin(valid_combination & target_label).count("1") == self.settings.PARAM_K:
# num_valid_combinations += 1
# if num_valid_combinations == self.ncr(label.count("1"), self.settings.PARAM_K):
# if best_label.count("1") < label.count("1"):
# best_label = label
# if best_label.count("1") >= self.settings.MAX_SET_SIZE:
# break
# print best_label, best_label.count("1"), len(all_location_signatures), len(label)
# if len(label) < len(all_location_signatures):
# stack = [label + "1", label + "0"] + stack
# if best_label != "":
# print "trajectory_id=", target_trajectory_id, ", time=", current_time, ", W=", [all_location_signatures[i] for i in range(len(best_label)) if best_label[i] == "1"]
# else:
# print "trajectory_id=", target_trajectory_id, ", time=", current_time, ", W= NaN"
print 'Done.'
