Cooperative communication based connectivity recovery for UAV networks

Abstract

 UAV networks often partition into separated clusters due to the high node and link dynamic. As a result, network connectivity recovery is an important issue in this area. Existing solutions always need excessive movement of nodes and thus lead to low recovery efficiency in terms of the time and energy consumption. In this paper, we for the first time study the issue of how to utilize cooperative communication technology to improve the connectivity recovery efficiency in UAV networks. We propose a Cooperative Communication based Connectivity Recovery algorithm for UAV Networks, named C 3 RUN. The key novelty is C 3 RUN not only uses cooperative communication to enlarge node's communication range and thus achieve quick repair of network connectivity, but also enables nodes to proactively move to better places for ensuring the establishment of cooperative communication links. We conduct extensive simulations to evaluate the performance of C 3 RUN. The simulation results reveal that C 3 RUN can not only achieve connectivity recovery with less nodes and shorter distance to move, but also always finish recovery with less time, when comparing with existing work. Furthermore, C 3 RUN can achieve 100% success ratio for connectivity recovery.

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

“…Theorem 1 shows that the minimum flight speed of the node in the direction of aggregating subnets is the maximum flight speed V max minus the speed of tracking the flight path v f , thus the maximum time required to move the subnet to the circular area with the waypoint f p (t) as its center and R 2 as its radius is d max move V max − v f , which is consistent with (25). Besides, the distance between the subnet and the waypoint is not greater than R 2 after the movement is completed.…”

“…In [24], uni-directional antennas are used to expand the communication range of the nodes, thereby improving the connectivity of WSNs. Tian et al [25] proposed a connectivity recovery algorithm for UAV networks, which uses cooperative communication technology to establish long-distance communication link between partitioned network parts to reduce movement of nodes. Once the cooperative communication links cannot be established, nodes can proactively move to better places for establishing the links.…”

“…where T max move is defined in (25), and it represents the maximum time to move a subnet to the circular area around the waypoint, and τ > 0 represents the maximum time required for merging subnets, τ is an empirical value associated with the current routing protocol and network size.…”

An Optimal Relay Number Selection Algorithm for Balancing Multiple Performance in Flying Ad Hoc Networks

“…Comprehensively, some works [23]- [25] have been proposed from the perspective of cross-layer analysis. Considering the problem of spectrum scarcity, [23] proposed an integration of UAVs with cognitive radio (CR) technology, discussed the CR-UAV integration issues, and highlighted future research challenges in layer-wise manner.…”

“…Simulation results showed that the proposed PPMAC overcome the directional deafness problem with directional antennas, and RLSRP provided an automatically evolving and more effective routing scheme. Tian W et al [25] firstly studied the issue of how to utilize CC to improve the connectivity recovery efficiency in FANETs, and designed a CC-based connectivity recovery algorithm named C3RUN, which not only uses CC to enlarge UAV's communication range and thus achieve quick repair of network connectivity, but also enables UAVs to proactively move to better places for ensuring the establishment of relay links. Simulation results revealed that C3RUN not only can achieve connectivity recovery with less nodes and shorter distance to move, but also can achieve 100% success ratio for connectivity recovery compared with existing work.…”