By Zayneb Trabelsi Ayoub, Sofiane Ouni (auth.), Mostafa Hashem Sherif, Abdelhamid Mellouk, Jun Li, Paolo Bellavista (eds.)
This publication constitutes the completely refereed court cases of the fifth overseas convention on advert Hoc Networks, ADHOCNETS 2013, held in Barcelona, Spain, in October 2013. The 14 revised complete papers provided have been conscientiously chosen and reviewed from quite a few submissions and canopy a variety of purposes, advertisement and army similar to cellular advert hoc networks, sensor networks, vehicular networks, underwater networks, underground networks, own quarter networks, domestic networks and large-scale metropolitan networks for shrewdpermanent towns. they're prepared in topical sections on instant sensor networks, routing, functions and security.
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Extra info for Ad Hoc Networks: 5th International ICST Conference, ADHOCNETS 2013, Barcelona, Spain, October 2013, Revised Selected Papers
The ants in the algorithms estimated the most desirable nodes to be data receivers that minimize the influence to the lifetime of the sensor nodes. Ali and Shahzad  surveyed various swarm intelligence algorithms. It was also observed that these approaches can be effectively applied to NP-hard problems for optimization. Yan et al.  introduced wireless sensor networks routing algorithms implemented using two kinds of ACO and an improved AS algorithm. It was observed that the routing algorithm implemented by ACO can reduce consumption of energy effectively.
C) Data delivery ratio with varying network area. (d) Energy consumed with varying network area. (e) Overhead (bytes) with varying network area. (f) Overhead (packets) with varying network area It can be seen in Fig. 2(d) energy consumed by VAS is least as compare to that of AODV and OSPFv2. In the case of AODV more energy is consumed with large networks because of probability of link failure and congestion in the network due to sparse population of sensor nodes. OSPFv2 consumes more energy out of all three algorithms.
Comput. 8, 358–375 (2012) 5. : Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997) 6. : Global path planning method of mobile robot in uncertain environment. In: IEEE, pp. 4320–4324 (2010) 7. : Route planning for unmanned aircraft based on ant colony optimization and voronoi diagram. In: IEEE, International conference on Intelligent Systems Design and Engineering Application, pp. 732–735 (2012) 8. : Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi Diagrams and Delaunay Triangulations.