No Arabic abstract
This paper reports experimental results on self-organizing wireless networks carried by small flying robots. Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a communication link between the UAVs is a challenging task. The topology of these networks is more dynamic than that of typical mobile ad hoc networks (MANETs) and of typical vehicle ad hoc networks (VANETs). As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this work, we compare two different routing algorithms for ad hoc networks: optimized link-state routing (OLSR), and predictive-OLSR (P-OLSR). The latter is an OLSR extension that we designed for FANETs; it takes advantage of the GPS information available on board. To the best of our knowledge, P-OLSR is currently the only FANET-specific routing technique that has an available Linux implementation. We present results obtained by both Media Access Control (MAC) layer emulations and real-world experiments. In the experiments, we used a testbed composed of two autonomous fixed-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication range, as well 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.
Multicasting is effective when its group members are sparse and the speed is low. On the other hand, broadcasting is effective when the group members dense and the speed are high. Since mobile ad hoc networks are highly dynamic in nature, either of the above two strategies can be adopted at different scenarios. In this paper, we propose an ant agent based adaptive, multicast protocol that exploits group members desire to simplify multicast routing and invoke broadcast operations in appropriate localized regimes. By reducing the number of group members that participate in the construction of the multicast structure and by providing robustness to mobility by performing broadcasts in densely clustered local regions, the proposed protocol achieves packet delivery statistics that are comparable to that with a pure multicast protocol but with significantly lower overheads. By our simulation results, we show that our proposed protocol achieves increased Packet Delivery Fraction (PDF) with reduced overhead and routing load.
In dynamic wireless ad-hoc networks (DynWANs), autonomous computing devices set up a network for the communication needs of the moment. These networks require the implementation of a medium access control (MAC) layer. We consider MAC protocols for DynWANs that need to be autonomous and robust as well as have high bandwidth utilization, high predictability degree of bandwidth allocation, and low communication delay in the presence of frequent topological changes to the communication network. Recent studies have shown that existing implementations cannot guarantee the necessary satisfaction of these timing requirements. We propose a self-stabilizing MAC algorithm for DynWANs that guarantees a short convergence period, and by that, it can facilitate the satisfaction of severe timing requirements, such as the above. Besides the contribution in the algorithmic front of research, we expect that our proposal can enable quicker adoption by practitioners and faster deployment of DynWANs that are subject changes in the network topology.
Routing protocols for Mobile Ad Hoc Networks (MANETs) have been extensively studied for more than fifteen years. Position-based routing protocols route packets towards the destination using greedy forwarding (i.e., an intermediate node forwards packets to a neighbor that is closer to the destination than itself). Different position-based protocols use different strategies to pick the neighbor to forward the packet. If a node has no neighbor that is closer to the destination than itself, greedy forwarding fails. In this case, we say there is void (no neighboring nodes) in the direction of the destination. Different position-based routing protocols use different methods for dealing with voids. In this paper, we use a simple backtracking technique to deal with voids and design a position-based routing protocol called Greedy Routing Protocol with Backtracking (GRB). We compare the performance of our protocol with the well known Greedy Perimeter Stateless Routing (GPSR) routing and the Ad-Hoc On-demand Distance Vector (AODV) routing protocol as well as the Dynamic Source Routing (DSR) protocol. Our protocol needs much less routing-control packets than those needed by DSR, AODV, and GPSR. Simulation results also show that our protocol has a higher packet-delivery ratio, lower end-to-end delay, and less hop count on average than AODV.
In this paper, we propose and evaluate a distributed protocol to manage trust diffusion in ad hoc networks. In this protocol, each node i maintains a trust value about an other node j which is computed both as a result of the exchanges with node j itself and as a function of the opinion that other nodes have about j. These two aspects are respectively weighted by a trust index that measures the trust quality the node has in its own experiences and by a trust index representing the trust the node has in the opinions of the other nodes. Simulations have been realized to validate the robustness of this protocol against three kinds of attacks: simple coalitions, Trojan attacks and detonator attacks.
Cognitive ad-hoc networks allow users to access an unlicensed/shared spectrum without the need for any coordination via a central controller and are being envisioned for futuristic ultra-dense wireless networks. The ad-hoc nature of networks require each user to learn and regularly update various network parameters such as channel quality and the number of users, and use learned information to improve the spectrum utilization and minimize collisions. For such a learning and coordination task, we propose a distributed algorithm based on a multi-player multi-armed bandit approach and novel signaling scheme. The proposed algorithm does not need prior knowledge of network parameters (users, channels) and its ability to detect as well as adapt to the changes in the network parameters thereby making it suitable for static as well as dynamic networks. The theoretical analysis and extensive simulation results validate the superiority of the proposed algorithm over existing state-of-the-art algorithms.