Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Softwarization of UAV Networks: A Survey of Applications and Future Trends

The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and different quality of service (QoS) requirements of different services, such as machine type communication (MTC), enhanced mobile broad band (eMBB), and ultra-reliable low latency communications (URLLC) over 5G networks. Unlike the previous ”one size fits all” system, the softwarization, slicing and network capability exposure of 5G provide dynamic programming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it is non-trivial for a programmer to develop traditional software codes to schedule the network resources based on expert knowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies. Machine learning is a computer technology that gives computer systems the ability to learn with data and improve performance and accuracy of decision making on a specific task, without being explicitly programmed. The areas of machine learning and communication technology are converging. Supervised learning based QoS assurance architecture for 5G networks was proposed in this paper. The supervised machine learning mechanisms can intelligently learn the network environment and react to dynamic situations. They can learn from the fore passed QoS related information and anomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomalies automatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machine learning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study for QoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.

/pdf/a-supervised-learning-based-qos-assurance-architecture-for-582pz08uz9.pdf

A Supervised Learning Based QoS Assurance Architecture for 5G Networks

In large scale networks like Vehicular Ad-hoc Networks (VANETs), the full coverage of fixed infrastructure is hard to ensure, making network management difficult. Whether in infrastructure-less environments where the network connectivity is poor or where the infrastructure deployment is difficult, costly or not profitable. Recently, in the one side, Unmanned Aerial Vehicles (UAVs) have been used as a new flexible solution to assist infrastructure-less vehicular networks for the investigation of inaccessible areas. In the other side, several works have shown interest in the use of the emerging network paradigm of Software-Defined Networking (SDN) to facilitate the management and improve the performances of vehicular networks. In this paper, we propose a novel distributed SDN-based architecture for UAV-assisted infrastructure-less vehicular networks. The main goal is to fill the gap that no SDN-based architecture has been proposed for these networks. We focus particularly on a road safety use-case that incorporates UAVs to assist emergency vehicles in the exploration of affected zones in critical emergency situations. Moreover, we investigate how to achieve efficient data processing policy through a computation offloading/sharing decision-making problem. The main challenge is to reach the best tradeoff between computation delay and energy consumption for computation-intensive tasks in a delay-sensitive context. We formulate this decision problem as a two-player sequential game approach and design distributed computation algorithms to solve the problem. Numerical results show that data processing policy of distributed offloading/sharing algorithms achieves efficient computation performances in terms of delay and energy whilst ensuring until 28% gain of system cost and 95% better response time, compared to native computation scenarios and related data delivery UAV-assisted VANET works, respectively.

Efficient Data Processing in Software-Defined UAV-Assisted Vehicular Networks: A Sequential Game Approach

Energy consumption of data mining algorithms on mobile phones: Evaluation and prediction

Providing integrity, non-repudiation and validity for transmitted data is always very attractive in unmanned aerial vehicle cluster networks (UAVCN). Currently, “one by one” data authentication schemes with expensive computations can hardly be applied to a fast data transmission. In order to simultaneously resolve the security and efficiency issues, a novel data aggregate authentication scheme based on the ID-Based encryption, named IBE-AggAuth, is proposed for UAVCN. In the proposed scheme, all kinds of data from different UAVs can be checked by batch verification instead of “one-by-one” verification. In particular, it proves that IBE-AggAuth scheme is the existentially unforgeable security under adaptive chosen message and ID attacks (EUF-CMA), rigorously based on the elliptic curve computational Diffie-Hellman assumption in the random oracle model. The detailed performance evaluations demonstrate that IBE-AggAuth can not only guarantee data security, but also speed up the verification process with reducing computation and communication costs for UAVCN. Moreover, UAVs are allowed to join the data transmission dynamically at any moment, and IBE-AggAuth can be applicable to the de-centralized and self-organized environment of UAVCN.

A provably secure aggregate authentication scheme for unmanned aerial vehicle cluster networks

In this paper, we propose a novel UAV to UAV communication approach that is based on the concept of Software Defined Networking (SDN). The proposed approach uses a controller as a central source of information to assign routes that maximize throughput, distribute traffic evenly, reduce network delay and utilize all network elements. Simulation results of the proposed methodology were compared to the performance of two common ad hoc routing protocols, namely AODV and OLSR. Performance analysis shows that the proposed methodology improves throughput by over 300%. Simulation results also show a reduction in network delay for delay sensitive packets by nearly 25% and a 26 times increase in packet delivery ratio for packets with higher priority.

Intelligent Wireless Ad Hoc Routing Protocol and Controller for UAV Networks.

Smart phones are ubiquitous nowadays and have taken over the bulk data transfers in mobile networks. The next generation phones are even more powerful to handle voice, video and data catering real time multimedia experience. Unfortunately, the increase in service provider capacity has not kept up with the user demand for more bandwidth. It is becoming very expensive for service providers to cater higher bandwidth without investing on new technology or expansion. In this paper we propose a bandwidth optimization algorithm based on cache coherency where the user data transfer is optimized without compromising the user expectation or the need for service providers to expand their capacity. The proposed algorithm is compared with existing data transfer techniques (such as GIT [9]) and we show the efficiency of the proposed method through simulation. We validate the simulation results through two year measured result.

Performance optimization of big data in mobile networks

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Intelligent Wireless Ad Hoc Routing Protocol and Controller for UAV Networks

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Intelligent Wireless Ad Hoc Routing Protocol and Controller for UAV Networks.

Performance optimization of big data in mobile networks

Intelligent Wireless Ad Hoc Routing Protocol and Controller for UAV Networks