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  4. Dynamic Routing for Flying Ad Hoc Networks - 2016
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Category: MTech NS2 Projects
By MTech Projects
MTech Projects
02.Jun
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Dynamic Routing for Flying Ad Hoc Networks - 2016

PROJECT TITLE :

Dynamic Routing for Flying Ad Hoc Networks - 2016

ABSTRACT:

This paper reports experimental results on self-organizing wireless networks carried by little flying robots. Flying accidental networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and quick to deploy. This makes them a terribly engaging technology for several civilian and military applications. Because of the high mobility of the nodes, maintaining a communication link between the UAVs could be a difficult task. The topology of those networks is more dynamic than that of typical mobile spontanepous networks (MANETs) and of typical vehicle unexpected networks. As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this paper, we compare 2 completely different routing algorithms for impromptu networks: optimized link-state routing (OLSR) and predictive OLSR (P-OLSR). The latter is an OLSR extension that we tend to designed for FANETs; it takes advantage of the Global Positioning System (GPS) info obtainable on board. To the best of our information, P-OLSR is currently the sole FANET-specific routing technique that has an accessible Linux implementation. We present results obtained by both media-access-control (MAC) layer emulations and real-world experiments. Within the experiments, we tend to used a testbed composed of 2 autonomous fastened-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication vary, moreover as the routing performance. Our emulation and experimental results show that P-OLSR significantly outperforms OLSR in routing in the presence of frequent network topology changes.

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Previous article: Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks - 2016 Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks - 2016 Next article: End-to-End Reliability-Aware Scheduling for Wireless Sensor Networks - 2016 End-to-End Reliability-Aware Scheduling for Wireless Sensor Networks - 2016
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