Homework 4

BL40A2010 Introduction to IoT-Based Systems

Assignment 4, 28.09.2023

Author: Tiia Syrjänen

(1) Draw a graph with star topology with five nodes (one in the center A, four in the edges B-E). If the topology is directed as a many-to-one topology, write the structure of awareness of the system, considering that nodes B-E acquire and process data about a supporting reality T, while A can only receive data from it. Remember the notation X_y means the image of X by Y.

#Import libraries !pip install networkx import networkx as nx import matplotlib.pyplot as plt import scipy as sp import numpy as np from IPython.lib.display import YouTubeVideo

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G = nx.Graph() G.add_nodes_from(['A', 'B', 'C', 'D', 'E']) G.add_edges_from([('A', 'B'), ('A', 'C'), ('A', 'D'), ('A', 'E')]) nx.draw_networkx(G, node_color='black', font_color='white', edge_color='black', font_weight='bold', node_size=700) plt.axis('off') plt.show()

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In this star topology there is node A and four nodes B-E connected to node A. Because of this many to one topology,

information only goes between nodes from B-E to node A. Structure of awareness T=A=(B+C+D+E)

(2) Assume that graph illustrate a scenario of four sensor nodes that also (pre-)process data and one aggregator node as follows. Nodes B and C monitor temperature and wind speed at Airport station in Lappeenranta, respectively. Nodes D and E monitor temperature and wind speed at Lepola station in Lappeenranta, respectively. Nodes B and D send a binary message to A indicating if during that period the temperature was more than 25 degrees ("1" means above, "0" means below or equal to). Likewise nodes C and E send a binary message to indicating if during that period the wind speed was more than 5 m/s ("1" means above, "0" means below or equal to). Download and plot the data from FMI with 1 hour resolution during July, 2018. Using this data, plot the binary signals sent from the nodes B-E to the aggregator A.

#Import libraries import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt ## # Nodes B and C ## Temperature = pd.read_csv("temperature.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5]) Temperature.info() #plotting plt.figure(figsize=(16,6)) # Node B = 0 #plt.plot(Temperature,color='blue', marker='x',linestyle=':') # Node C = 1 plt.plot(Temperature.values > 25 ,color='black', marker='o',linestyle=' ') #for row in Temperature: # if Temperature > 25 # Temperature = 1 #else: # Temperature = 0 plt.title("Air temperature in July 2018, at Lappeenranta Lepola station") plt.ylabel("Temperature in [°C]") plt.grid(True) plt.tight_layout() plt.show()

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#Import libraries import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt ## # Nodes D and E ## Windspeed = pd.read_csv("windspeed.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0, 1, 2, 3]],usecols=[0,1,2,3,5]) Windspeed.info() #plotting plt.figure(figsize=(16,6)) # Node D = 0 plt.plot(Windspeed,color='blue', marker='x',linestyle=':') # ! # My data with windspeed doesn't work, it says: TypeError: unhashable type: 'numpy.ndarray', even though data is # exactly same with temperature data !!! # # Node E = 1 plt.plot(Windspeed.values > 5,color='red', marker='.',linestyle='--') #for column in Windspeed[5]: # if Windspeed > 5 # Windspeed = 1 #else: # Windspeed = 0 plt.title("Wind speed in July 2018, at Lappeenranta Lepola station") plt.ylabel("Speed in [m/s]") plt.grid(True) plt.tight_layout() xfmt = mdates.DateFormatter('%H:%M') plt.show()

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(3) Node A needs to identify risky situations in Lappeenranta when the temperature is above 25 degrees and the wind is above 5 m/s based on the sensors' signals. If a risky situation is identified at node A as binary number "1" while normal situations as "0". How would you solve this problem? Plot the output signal from node A and discuss its relation to the input signals from B-E.

#subplot function #https://matplotlib.org/2.0.1/examples/api/two_scales.html #### # A=(B+C+D+E) fig, ax1 = plt.subplots(figsize=(16,6)) #left axis ax1.set_ylabel("Windspeed in [m/s]", color='black') # !!!!!! # Wind speed data gets error # B + C #ax1.plot(Windspeed,color='black', marker='o',linestyle='--') #ax1.plot(Windspeed > 5,color='red', marker='o',linestyle='') #right axis # D + E ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis ax2.set_ylabel("Temperature in [°C]", color='black',rotation=270,va="bottom") #ax2.plot(Temperature,color='blue', marker='x',linestyle=':') ax2.plot(Temperature>25,color='blue', marker='x',linestyle='') # ax1.grid(True) plt.title("Wind speed and air temperature in July, 2018") #title #change x axis xfmt = mdates.DateFormatter('%H:%M') ax1.xaxis.set_major_formatter(xfmt) # plt.show()

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Node A identifies, if nodes B-E print 1 or 0.

A is dependent on every node from B-E. If there is one node missing, there could be lack of information and therefore

risky situations.

Output, node A, models the risky times in July 2018 when temperature is 25 degrees and wind speed over 5 m/s. Results are not

qualified for this specific measurement, if nodes B-E do not set requirements which is sending data with terms 1 or 0, condition is

true of false.