Internet of Things in Business Transformation - Группа авторов

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and associating the distance values with corresponding nodes 7) For (iteration = 1) 1 to 10 8) Available nodes for cluster formation = All Node a) WHILE (Nodes Available for clustering! = empty) b) End while 9) FOR Drag-fly(i) = form 1 to Total population a) Source (Food/enemy) = empty, Food Source Cost = infinity, and Enemy Source cost = -infinity b) Objective Values calculation, of Dragonflies c) Update the radius d) Update sources (food and enemy) e) Update weights 10) END FOR 11) FOR Drag-fly(i) = From 1 to Total population a) FOR Drag-fly(i) = 1 to Total population i) Update neighboring radius b) ENDFOR c) Calculate Separation, Alignment, Cohesion, Enemy and Food weights 12) IF neighbor! =0 a) Velocity Update b) Position Update 13) Else a) Levy flight 14) END IF 15) END FOR 16) Best cost == Food fitness 17) END FOR 18) IF not clustered nodes>20% a) Goto line #1 19) Else a) Output 20) End IF

      [3.2]image

      Attrition for food calculated as [18]:

      [3.3]image

      Here X+ is position of food source. And X is current position of an individual.

      Distraction away from an enemy [18]:

      [3.4]image

      X—is position of an enemy.

Graph depicts the second part of the transmission range versus Cluster Heads for Nodes 50–200.

      As transmission range increases to 8 and 10 MPSO is also trying to meet DA. But if we analyze overall results DA stands alone as the best participant. That formed a smaller number of clusters in almost all number of nodes and transmission ranges.

      Wireless Body Area Network (WBAN) protects the patient’s life by its continuous monitoring and data transmission mechanism. For load balancing the most important method in WBAN is clustering which provides practical approach for energy optimization of senor nodes. We designed a cluster formation technique using Evolutionary algorithms. Optimized clustering is grouping the nodes of the network in the most efficient way. We also need a minimum number of possible clusters, long-lasting in the network. We analyzed the performance difference in Comprehensive Learning Particle Swarm Optimization (CLPSO), Dragonfly Algorithm (DA), and Multi-objective particle swarm optimization (MOPSO). Our experimentation has shown that the overall performance of DA is the most efficient among all three algorithms, as it forms fewest optimized long lasting clusters.

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      8. Mirjalili, S., Dragonfly algorithm:

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