A Dual-Antenna Collaborative Communication Strategy for Flying Ad Hoc Networks

A Dual-Antenna Collaborative Communication Strategy for Flying Ad Hoc Networks

Abstract: Existing communication protocols for mobile ad hoc networks (MANETs) misfit flying ad hoc networks (FANETs) due to their exclusive design challenges on the typical long communication distance and high mobility degree of nodes. To tackle them, we introduce a hybrid Omni directional and multi beam directional antenna structure for gradient-elastic control data separation. Based on dual antennas, we further propose a novel communication strategy, which combines location prediction with target tracing, to catch and trace communication targets by adaptively exchanging heartbeat locations for stable data transmission.

Performance evaluation shows that our proposal achieves 35.8% increase in data link robustness.

Existing system: As a sub-branch of mobile ad-hoc networks (MANETs), flying ad-hoc network (FANET) consists of multiple UAVs with six degrees of freedom on mobility (i.e. latitude, longitude and altitude; roll, pitch and yaw angle) and peer-topeer communication support. Despite sharing some common characteristics with

MANETs, FANETs have two exclusive design challenges that existing communication protocols for MANETs are not capable of tackling, as follows.

First, communication links in FANETs are normally across longer distances compared with the typical one among territorial vehicles (around 250 m). For instance, a lightweight UAV (e.g. 10 kg) normally covers a communication radius of at least 5 km, let alone the large-sized ones which reach tens of kilometers. With energy constrained, it is costly in power dissipation for a UAV to realize efficient data transmission over such a long communication distance by conventional omni directional antenna.

Proposed system: Accordingly, we further propose a communication strategy to collaborate dual antennas against high mobility degree of FANET nodes. By activating fast-refresh location information (i.e. high-frequency heartbeat locations) on data channels, the strategy is operated to trace communication targets for both stable and high-rate data transmission, combined with location prediction, which is responsible to catch them before tracing. More details are illustrated in Section IV. The contribution of this letter is twofold: i) A novel hybrid omni directional and directional antenna structure is introduced for gradient-elastic control-data separation to overcome directional deafness problem and break through limitations of dual directional antennas(shown in Section II). ii) Based on dual-antenna structure, a collaborative communication strategy is proposed to support the high mobility degree of FANET nodes by catching and tracing communication targets.

Advantages: Second, peer-to-peer communication in FANETs has to tackle the challenge of high mobility of UAVs. Compared with vehicles on the ground, UAVs own more degrees of freedom on mobility in the air, which lead to changeable relative speeds up to 920 km/h with variable directions in a three-dimension vector, and further, result in fluctuating communication links.

Hence, FANET nodes are difficult to establish stable links if their moving locations cannot be accurately managed. As a solution, location prediction is introduced in for the mobility management, i.e., FANET nodes exchange location information messages periodically or so-called heartbeat locations on the control

channel to calculate neighbor nodes’ future location coordinates or moving trajectories.

Disadvantages: To tackle these two design challenges of FANETs and corresponding problems, we propose a dual-antenna collaborative communication strategy. In physical layer, dual-antenna structure is adopted to address directional deafness problem by separating control and data plane, so that one control channel is specialized for exchanging control massages even if other channels are busy.

Note that the separation is gradient elastic, where 50-50% control-data gradient slice will be pulled towards data plane on demand, i.e., controlling on mobility management is partly transferred to data plane, for releasing overloaded location information upon the low-capacity control channel.

Modules: Unmanned aerial vehicle: UNMANNED aerial vehicles (UAVs) are remotely autonomous piloted drones that play important roles in both defence and commerce with rapid-growing global markets, like real-time video reconnaissance and surveillance, search and rescue (SAR) operations in disasters, and so forth. As a sub-branch of mobile ad-hoc networks (MANETs), flying ad-hoc network (FANET) consists of multiple UAVs with six degrees of freedom on mobility (i.e. latitude, longitude and altitude; roll, pitch and yaw angle) and peer-topeer communication support. Despite sharing some common characteristics with MANETs, FANETs have two exclusive design challenges that existing communication protocols for MANETs are not capable of tackling , as follows. First, communication links in FANETs are normally across longer distances compared with the typical one among territorial vehicles (around 250 m). For instance, a lightweight UAV (e.g. 10 kg) normally covers a communication radius of at least 5 km, let alone the large-sized ones which reach tens of kilometers. With energy constrained, it is costly in power dissipation for a UAV to realize efficient data transmission over such a long communication distance by conventional omni directional antenna.

Peer- to- peer:

Second, peer-to-peer communication in FANETs has to tackle the challenge of high mobility of UAVs. Compared with vehicles on the ground, UAVs own more degrees of freedom on mobility in the air, which lead to changeable relative speeds up to 920 km/h with variable directions in a three-dimension vector, and further, result in fluctuating communication links. Hence, FANET nodes are difficult to establish stable links if their moving locations cannot be accurately managed. As a solution, location prediction is introduced in for the mobility management, i.e., FANET nodes exchange location information messages periodically or so-called heartbeat locations on the control channel to calculate neighbor nodes’ future location coordinates or moving trajectories. However, to guarantee enough accuracy of location prediction under such a high degree of mobility, a millisecond updating period of location information is required (e.g., no more than 3 ms when the communication distance is 0.5 km), which produces large amounts of control messages.

Location prediction vs. Collaborative communication strategy: We adopt DLSR among FANET nodes (equipped with the dual-antenna structure) for the comparison in link robustness, where survival means their data links are not disrupted before finishing data transmission. Additionally, we add a conventional method (i.e., no strategies for mobility with a single omni directional antenna) into the comparison as a lower bound of DLSR in FANETs. Results in Fig. 5 show that DLSR of collaborative communication strategy, combined location prediction with target tracing, is more than the lone location prediction, proposed in. Above the average speed of 90 m/s, our proposal achieves average 35.8% increase in DLSR compared with the lone location prediction, and thus caters for the high mobility of UAVs better.