Speed limits in autonomous vehicular networks due to communication constraints

Autonomous vehicles need to be aware of other vehicles in their vicinity in order to avoid collisions and successfully perform their tasks. Such network awareness is ensured by exchanging location and control information over wireless radio channels. However, wireless interference constraints limit the number of messages that can be exchanged between the vehicles. In this paper, we study the impact of such communication constraints on maximum vehicle speed in dense autonomous vehicular networks. We define hazard rate to be the fraction of time a vehicle enters an `uncertainty region', i.e., a region where there is a positive probability of other vehicles being present due to lack of situational awareness. We show that the hazard rate follows a threshold behavior with respect to maximum speed v as the network density n increases to infinity. We show that for a 2D network the hazard rate tends to 1, if the maximum speed v decreases slower than n[superscript -3/2]; and tends to 0, if v decreases faster than n[superscript -3/2]. For the network hazard rate, which is the fraction of time any vehicle enters its uncertainty region, the threshold is n[superscript -2]. Finally, we extend these results to a 3D network and show that the thresholds for the 3D network are larger than in the 2D network

Analysis of energy transfer efficiency in UAV-enabled wireless networks

Wireless power transfer (WPT) is a promising charging technology for battery-limited sensors. In this paper, we study the energy transfer in a wireless network using an unmanned aerial vehicle (UAV). Instead of charging the remote wireless sensors directly from the access point (AP), we study the schemes of using a UAV to charge the remote wireless sensors after it is charged by the AP. To this end, two schemes are proposed. The performances of these two schemes are examined and compared with the conventional scheme without using a UAV. A distance threshold beyond which the new schemes have superiority over the conventional scheme is derived by solving energy equations. Numerical results show that the proposed schemes can achieve significantly higher energy efficiency than the conventional scheme when the transmission distance is within the derived critical range

A service-constrained positioning strategy for an autonomous fleet of airborne base stations

This paper proposes a positioning strategy for a fleet of unmanned aerial vehicles (UAVs) airlifting wireless base stations driven by communication constraints. First, two schedulers that model the distribution of resources among users within a single cell are analyzed. Then, an UAV autonomous positioning strategy is developed, based on a fair distribution of the radio resources among all the users of all the cells in a given scenario, in such a way that the user bitrate is the same regardless the users’ distribution and spatial density. Moreover, two realistic constraints are added related to capacity of the backhaul link among the UAVs and the ground station: the bitrate delivered per UAV and the total backhaul bandwidth shared among all the UAVs. Additionally, an energy consumption model is considered to evaluate the efficiency and viability of the proposed strategy. Finally, numerical results in different scenarios are provided to assess both the schedulers performance and the proposed coordinated positioning strategy for the UAVs.Peer ReviewedPostprint (published version

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

Design, Analysis and Evaluation of Unmanned Aerial Vehicle Ad hoc Network for Emergency Response Communications

In any emergency situation, it is paramount that communication be established between those affected by an emergency and the emergency responders. This communication is typically initiated by contacting an emergency service number such as 9-1-1 which will then notify the appropriate responders. The communication link relies heavily on the use of the public telephone network. If an emergency situation causes damage to, or otherwise interrupts, the public telephone network then those affected by the emergency are unable to call for help or warn others. A backup emergency response communication system is required to restore communication in areas where the public telephone network is inoperable. The use of unmanned aerial vehicles is proposed to act as mobile base stations and route wireless communication to the nearest working public telephone network access point. This thesis performs an analysis based on wireless attributes associated with communication in this type of network such as channel capacity, network density and propagation delay

Airborne Wireless Communication Modeling and Analysis with MATLAB

Over the past decade, there has been a dramatic increase in the use of unmanned aerial vehicles (UAV) for military, commercial, and private applications. Critical to maintaining control and a use for these systems is the development of wireless networking systems [1]. Computer simulation has increasingly become a key player in airborne networking developments though the accuracy and credibility of network simulations has become a topic of increasing scrutiny [2-5]. Much of the inaccuracies seen in simulation are due to inaccurate modeling of the physical layer of the communication system. This research develops a physical layer model that combines antenna modeling using computational electromagnetics and the two-ray propagation model to predict the received signal strength. The antenna is modeled with triangular patches and analyzed by extending the antenna modeling algorithm by Sergey Makarov, which employs Rao-Wilton-Glisson basis functions. The two-ray model consists of a line-of-sight ray and a reflected ray that is modeled as a lossless ground reflection. Comparison with a UAV data collection shows that the developed physical layer model improves over a simpler model that was only dependent on distance. The resulting two-ray model provides a more accurate networking model framework for future wireless network simulations

Maximizing Throughput of UAV-Relaying Networks with the Load-Carry-and-Deliver Paradigm

We consider the task of using one or more Unmanned Aerial Vehicles (UAVs) to relay messages between two distant ground nodes. For delay-tolerant applications like latency-insensitive bulk data transfer, we seek to maximize throughput by having a UAV load from a source ground node, carry the data while flying to the destination, and finally deliver the data to a destination ground node. We term this the ”load-carry-anddeliver” (LCAD) paradigm and compare it against the conventional multi-hop, store-and-forward paradigm. We identify and analyze several of the most important factors in constructing a throughput-maximizing framework subject to constraints on both application allowable delay and UAV maneuverability. We report performance measurement results for IEEE 802.11g devices in three flight tests, based on which we derive a statistical model for predicting throughput performance for LCAD. Due to the nature of commercial off-the-shelf systems, this methodology is of essential importance for allowing better flight-path design to achieve high throughput. I