Sem6

IOT Professional elective IOT: A dynamic globan network infrastructure with a self-configuring capability based on standard protocols and inter operatble communication protocols where physical and virtual things have identities, physical attributes, and virtual personalities use intelligent interfaces and are seamlessly integrated into the information network often communicate data associated with users and external environment. Charecteristics Dynamic and self-configuring Self configuring Interopeable communications protocols Unique identity Integrated info network Block diagram of an iot device ...

Lab expts sem 6 AIML TW1 - DFID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 from collections import defaultdict class Graph: def __init__(self, vertices): self.V = vertices self.graph = defaultdict(list) def addEdge(self, u, v): self.graph[u].append(v) def DLS(self, src, target, maxDepth, visited): visited.append(src) # Add the current node to visited if src == target: return True, visited if maxDepth <= 0: return False, visited for i in self.graph[src]: found, visited = self.DLS(i, target, maxDepth - 1, visited) if found: return True, visited return False, visited def IDDFS(self, src, target, maxDepth): visited = [] # Initialize the visited list for i in range(maxDepth + 1): # Include maxDepth level found, visited = self.DLS(src, target, i, visited) if found: return True, visited return False, visited # Create a graph g = Graph(7) g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 3) g.addEdge(1, 4) g.addEdge(2, 5) g.addEdge(2, 6) target = 6 maxDepth = 3 src = 0 found, visited = g.IDDFS(src, target, maxDepth) if found: print("Target is reachable from source within max depth") print("Visited nodes:", visited) else: print("Target is NOT reachable from source within max depth") print("Visited nodes:", visited) TW2 - BFS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 # Python3 Program to print BFS traversal # from a given source vertex. BFS(int s) # traverses vertices reachable from s. from collections import defaultdict # This class represents a directed graph # using adjacency list representation class Graph: # Constructor def __init__(self): # Default dictionary to store graph self.graph = defaultdict(list) # Function to add an edge to graph def addEdge(self, u, v): self.graph[u].append(v) # Function to print a BFS of graph def BFS(self, s): # Mark all the vertices as not visited visited = [False] * (max(self.graph) + 1) # Create a queue for BFS queue = [] # Mark the source node as # visited and enqueue it queue.append(s) visited[s] = True while queue: # Dequeue a vertex from # queue and print it s = queue.pop(0) print(s, end=" ") # Get all adjacent vertices of the # dequeued vertex s. # If an adjacent has not been visited, # then mark it visited and enqueue it for i in self.graph[s]: if not visited[i]: queue.append(i) visited[i] = True # Driver code if __name__ == '__main__': # Create a graph given in # the above diagram g = Graph() g.addEdge(0, 1) g.addEdge(0, 2) g.addEdge(1, 2) g.addEdge(2, 0) g.addEdge(2, 3) g.addEdge(3, 3) print("Following is Breadth First Traversal" " (starting from vertex 2)") g.BFS(2) # This code is contributed by Neelam Yadav # This code is modified by Susobhan Akhuli TW3 - A* algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 import heapq # tw3 def astar(graph, start, goal): # priority queue to store nodes to be explored open_list = [(0, start)] # dictionary to store parent nodes parents = {} # dictionary to store g values (cost from start node to current node) g_values = {node: float('inf') for node in graph} g_values[start] = 0 # dictionary to store f values (estimated total cost from start to goal) f_values = {node: float('inf') for node in graph} f_values[start] = graph[start][1] iteration = 0 while open_list: # get node with minimum f value current_f, current_node = heapq.heappop(open_list) # check if current node is the goal if current_node == goal: path = [] while current_node in parents: path.append(current_node) current_node = parents[current_node] path.append(start) final_cost = g_values[goal] print(f"\nFinal Cost: {final_cost}") return path[::-1] # explore neighbors for child, cost in graph[current_node][0].items(): # calculate tentative g value tentative_g = g_values[current_node] + cost if tentative_g < g_values[child]: # update parent and g values parents[child] = current_node g_values[child] = tentative_g f_values[child] = tentative_g + graph[child][1] # add child to open list heapq.heappush(open_list, (f_values[child], child)) iteration += 1 print(f"\nIteration {iteration}:") print("Current Path:", reconstruct_path(parents, start, current_node)) print(f"Evaluation Function Value for {current_node}: {f_values[current_node]}") # Function to reconstruct the path from start to goal using parent nodes def reconstruct_path(parents, start, goal): path = [goal] while goal != start: goal = parents[goal] path.append(goal) return path[::-1] # Example usage: start_node = 'A' goal_node = 'G' graph = { 'A': [{'B': 5, 'C': 10}, 10], 'B': [{'D': 5, 'E': 5}, 7], 'C': [{'F': 5}, 7], 'D': [{'G': 10}, 3], 'E': [{'G': 7}, 2], 'F': [{'G': 8}, 1], 'G': [{}, 0] } print("\nA* Search Path:") path = astar(graph, start_node, goal_node) print("Final Path:", path) USP Running a code We shall use fedora to run most of the code. The code will be written in ANSI C and. To run the code, we shall use the following commands: ...