Flying ad hoc network (FANET) is widely used in many military, commercial and civilian applications. Compared with mobile adhoc network (MANET) and vehicular ad hoc network (VANET), FANET holds unique characteristics such as high mobility, intermittent links and frequent topology changes, which cause a challenging task in the design of routing protocols. A novel adaptive software defined networking (SDN)-based routing framework for FANET called ASR-FANET is proposed in this article to solve the above challenges. The ASR-FANET framework is mainly composed of three important parts, which are the topology discovery mechanism, statistics gathering mechanism and route computation mechanism. In topology discovery mechanism, the periodic information about network topology is collected, including nodes and links. In statistics gathering mechanism, the status of the wireless network connection and flight statistics are collected. In route computation mechanism, the optimal path is calculated based on link costs. The performance of ASR-FANET framework is also has been evaluated by comprehensive simulations. The simulation results show that proposed framework is much better than other traditional protocols in packet delivery fraction, average end to end delay, normalized routing load, packet loss and throughput.
Unmanned aerial vehicles (UAVs) is widely used in many military, and civilian applications. UAVs communicate in a Flying Ad hoc Network (FANET) environment where UAVs communicate with each other through an ad hoc network without infrastructure. FANET provide a flexible platform for internet of things (IoT) applications by playing different roles in IoT such as mobile data collector. In fact, in deadline based IoT applications, the deadline is restricted to the critical application level. And as a result, this deadline for data acquisition is not adequate, and FANET cannot collect data from the sensed area with the predetermined deadline. In this paper, a novel efficient data gathering approach for IoT using FANET is proposed. The main objective of this approach is to solve the problem of insufficient deadlines given by FANET in IoT-based deadline applications. Authors will first provide a multi-objective optimization model as a MILP optimization model to solve this problem, and then normalize and add two weighing coefficients to solve the MILP model. The results obtained in the simulation show that the proposed approach can provide efficient data acquisition while guaranteeing the deadline time.
Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV.
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