By Noah E. Friedkin and Eugene C. Johnsen
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Additional resources for ATTITUDE CHANGE, AFFECT CONTROL, AND EXPECTATION STATES IN THE FORMATION OF INFLUENCE NETWORKS
The forwarding functions are kept same as the original algorithm. Now the algorithm targets on two objectives load balancing as well as ﬁnding the stable nodes for the path to the destination. The route discovery and route maintenance is same as the Opt-Termite algorithm. 4 Results and Discussion The proposed methods are simulated in ns-2 . Diﬀerent simulation runs for diﬀerent scenarios with varying mobility of MN’s are taken and the average value is used to plot the graph. A comparative study is made among all the methods along with the traditional AODV protocol.
However, there is waiting delay due to channel contention. , the number of generated data during a frame time. The value N is integer, N ≥ [drate ∗ tf r + 1] (11) The following equation indicates the optimization problem: min such that E[EtoElatency ] tda ≥ E[t] ∗ (drate ∗ tf r ) N ≥ [drate ∗ tf r + 1] tda ≥ 0, and N ≥ 0, (12) The optimal direct access time and number of time slots in each sensor node is calculated according to the data rate from the source node. Hence, the following equation shows the ﬁnal optimization problem of the end-to-end latency.
And G. com Abstract. Providing Quality of Service (QoS) in Mobile Ad-Hoc Networks (MANET’s) is diﬃcult due to dynamic nature of its topology. Today’s research trends show that Swarm Intelligence (SI) can be used eﬀectively to provide QoS in MANET and also MANET is not much explored in the area of SI. Motivated by their self organizing behavior and robustness many routing algorithms have been proposed for both wired and wireless networks. SI routing algorithms are driven by mainly two functions, Pheromone update-decay functions and Forwarding functions.
ATTITUDE CHANGE, AFFECT CONTROL, AND EXPECTATION STATES IN THE FORMATION OF INFLUENCE NETWORKS by Noah E. Friedkin and Eugene C. Johnsen