By Gianni A. Di Caro, Guy Theraulaz (eds.)
This ebook constitutes the completely refereed post-conference complaints of the seventh overseas convention on Bio-Inspired types of community, details and Computing platforms (Bionetics 2012), held in Lugano, Switzerland, in December 2012. The 23 revised complete papers provided have been conscientiously reviewed and chosen from forty submissions. They conceal issues comparable to networking, robotics and neural networks, molecular scale and bioinformatics, optimization and bio-inspired modeling in numerous fields.
Read Online or Download Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers PDF
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Additional info for Bio-Inspired Models of Network, Information, and Computing Systems: 7th International ICST Conference, BIONETICS 2012, Lugano, Switzerland, December 10--11, 2012, Revised Selected Papers
The load ρ at the variance σa2 is calculated by λ/μ, where μ is the service rate of the bottleneck link and λ is the arrival rate when the variance is σa2 . Therefore, ρ depends on σa2 and the x-axis can be mapped to ρ. In Fig. 9 is depicted. To compare the analytical result of an M/D/1 system discussed in the previous 6 750 by Δλ = Δa×10 in Eq. (10). 9. As shown in Fig. 3, there is a diﬀerence in slope between the analytical result and the simulation result. 1 −∈w∀a is larger in the simulation than in the analysis.
Regarding our proposal, independently of the setting of coeﬃcient b, the average end-to-end delay stays close to the target delay except for the period right after the sudden load increase. In the case of b = 300 for example, a sender node tries to decrease the sending rate on reception of a RR packet from a receiver node at 201 s. 33 Mbps at that time as shown in Fig. 6. It is because the delay and variance informed by the RR packet are derived from RTP packets sent before the load increase. 8 Mbps target 7 6 0 50 100 150 200 time [s] 250 300 Fig.
We use statistical models of data packet delay to describe the current traﬃc conditions on diﬀerent paths in the network. We believe that thank to these models it will be possible to detect and quickly react on various load level changes. The paper is organized as follows. In Sect. 2 we shortly introduce the swarm intelligence approach to adaptive routing and describe the ant-routing algorithms. Section 3 describes the modeling scheme of the packet delay distribution for ﬁxed networks. In Sect.