This talk does not have an abstract.

This talk does not have an abstract.

5G mobile networks will have to deal simultaneously with three types of traffic: mobile broadband communications, critical communications and Internet of Things (IoT). In this paper, we propose a novel hybrid 5G Fiber-Wireless (FiWi) access architecture equipped with Mobile Edge Computing-enabled (MEC) macro base stations (BS). Each BS is itself interfaced to the end-users (UE) via Unmanned Aerial Vehicles (UAV) in order to provide a high level or flexibility and reliability of the infrastructure. Given the proposed architecture, our objective is to solve the user association problem under differentiated Quality of Service (QoS). As a benchmark, both IoT and haptic applications are considered. Our approach consists then in solving the user association problem by means of Genetic Algorithms (GA).

The Internet of Drones (IoD) is an environment, which facilitates the autonomous and hybrid (semi-autonomous) operations of drones. Due to this advantage, the drones have paved their way into every segment (almost) across the world. Whatever be the application (warfare, surveillance, photography, rescue, delivery, etc), the transmission of data (to and fro) occur between the drones and the other infrastructure over wireless channels. While on the move, this data (operational or service-related) is vulnerable to several security risks and attacks. Hence, the maintenance of the confidentiality, integrity, and authenticity of the data is the primary goal in the IoD environment. Recently, the blockchain, a distributed ledger-based technology is being popularly adapted to store the data immutably, making it a possible solution to handle the above-raised issues. But, there are several challenges with conventional blockchain architecture, which make it difficult to adapt it in its current form. Firstly, the blockchain works by building trust in the trust-less environment by using a consensus mechanism. Secondly, the blockchain inherently involves a large number of network communication operations to synchronize the peer-to-peer network. Even more, the bigger the blockchain grows, the more is the data flowing across the network and so does the challenges. Therefore, to overcome these problems, ODOB: One Drone One Block-based Lightweight Blockchain Architecture for IoD is proposed. ODOB decouples the data part (or block ledger) from the block header to form a distributed architecture. This architecture couple the drones with an individual updateable block (each drone can access only their own block), thereby making it simple, trustworthy, and lightweight. ODOB is evaluated for several parameters and the results obtained favor the proposed architecture.

An Unmanned aerial vehicle (UAV) can be used in many smart applications, such as defense, civilian, and healthcare services. As data in these applications flow through an open channel, i.e., the Internet, so security and privacy always remain a challenging issue. Though many solutions exist for this problem in literature, but these solutions are not adequate to handle security, privacy, latency, and efficient real-time delivery of healthcare services remotely over the wireless communication channel. Moreover, the existing UAV systems have security, reliability, latency, and storage cost issues, which restricts their applicability shortly. Motivated from these facts, this paper proposes an approach VAHAK, which is an Ethereum blockchain (BC) based secure outdoor healthcare medical supplies using UAVs. VAHAK provides reliable communication between the UAVs and the entities in a decentralized manner, which ensures the early delivery of required medical supplies to the critical patients. In VAHAK, security, privacy, and reliability issues have been resolved using Ethereum smart contract (ESC), while storage cost issues are handled with IPFS protocol. Then, we implemented a real-time ESC and deployed it in Remix IDE. The security vulnerabilities of the VAHAK are tested on MyThril open-source tool. VAHAK is efficient in terms of data storage cost as it uses the InterPlanetary File System (IPFS) for healthcare record storage and 5G-enabled Tactile Internet (TI) for communication, respectively. Finally, VAHAK performance evaluation demonstrates its effectiveness as compared to the traditional systems where it outperforms the existing schemes with respect to various performance evaluation metrics, such as scalability, latency, and network bandwidth.

5G, blockchain, and drones are potentially revolutionizing future technologies. 5G promises to provide a tactile internet environment to the users. Tactile internet is characterized by ultra-low latency, with high reliability, security, and availability. Few attempts have been made in academia and industry to use drones-mounted small cell base stations. Such flying base stations can be used in disaster areas, emergencies, or in rural areas. The major challenge in deploying such flying base stations is data security. Drones being resource-constrained devices cannot be overloaded with heavy security algorithms. Moreover, the decision of user association, drone movement, and bandwidth allocation are major bottlenecks in deploying such networks. In this paper, we propose a blockchain-based security framework for drone-mounted base stations in the tactile internet environment. Furthermore, a game-theoretic model is proposed as a smart contract to decide on the dynamic bandwidth allocation to different users based on bandwidth availability and cost. Numerical results show that the proposed model helps in better user experience in terms of bandwidth allocation in low network areas.

This talk does not have an abstract.

This talk does not have an abstract.

Heterogeneous networks (HetNets) comprising of various communication technologies with wireless backhaul capabilities are becoming essential ingredients to satisfy the high data rate requirements set for future wireless systems. The use of unmanned aerial vehicles (UAVs) and the addition of teraHertz (THz)-based communication is considered a key pillar in providing such capabilities. UAVs are used to provide infrastructure-less, rapid and on-demand deployment, while THz technology is a new wireless frontier and a potential enabler for providing immense data rates. Wireless backhaul capabilities are necessary to avoid cumbersome wired layouts. In this paper, we consider a multi-tier HetNet comprising of UAVs operating at sub-6GHz, small base stations (BSs) operating at THz frequencies and a macro-BS (MBS) operating at sub-6GHz. Wireless backhauling is provided by the MBS, some UAVs, and by a few THz cells. Network coverage and data rates are taken as quality of service (QoS) performance metrics. We analyze the effectiveness of the HetNet configuration on network coverage and data rates by varying different parameters. Extensive simulation has been performed, which validates the significance of deploying such a HetNet with wireless backhaul capabilities and to provide elevated data rates to the end users.

By leveraging the multi-hop relaying and self organization capabilities, mesh networks can provide enhanced and robust wireless coverage. Despite these capabilities, in deployment scenarios where some nodes are severely obstructed from others, overall network connectivity may be hampered. In this work, we investigate the use of an unmanned aerial vehicle (UAV) serving as a smart relay to improve the connectivity in a wireless mesh network. It is the first contribution of its kind in the context of mesh networks where an UAV autonomously navigates itself to maximize the mesh connectivity based on the positioning algorithm that exploits the radio measurements collected in the network. We also validate the performance of the developed algorithms in a real-life experimental setup.

This paper investigates a multiple unmanned aerial vehicles (UAVs) assisted network, where multiple UAVs send confidential information to multiple authorized access users (AUs), in the existence of an unauthorized users. In order to improve the fairness among AUs, an optimization problem is formulated to maximize the minimum secrecy rate of AUs by jointly optimizing user scheduling, UAVs' trajectories and transmit power allocation, subject to on board energy budget and curvature radius constraints. An effective algorithm is proposed by applying the techniques of block coordinate descent (BCD) and successive convex approximation (SCA) to tackle the mixed integer non-convex optimization problem. Simulation results show that the proposed algorithm converges within several iterations, and significant system performance gain is able to achieve over other benchmarks. Moreover, the larger the curvature radius is, the smoother the UAVs' trajectories are, and the lower the secrecy rate is. This is by no means that introducing curvature radius into the trajectory design will reduce system performance, because it is a approach to make the UAV trajectory closer to actual flight. Additionally, the higher the propulsion energy stored at UAVs is, the higher the achieved max-min secrecy rate is. Furthermore, the max-min secrecy rate is improved by allowing concurrent transmissions of multiple UAVs.

Radio frequency energy harvesting (RF-EH) offers unorthodox solution to the painstaking energy constraint drawback in wireless sensor networks (WSNs). In this paper, we consider a data-gathering from a WSN by the unmanned aerial vehicle (UAV), where sensor nodes (SNs) are remotely powered by the power beacons (PBs) using wireless power transfer (WPT). In the first phase, a cluster-head (CH) is selected from the powered-up SNs and each SN periodically sends its observations to the CH. In the second phase, UAV powers the CH and collects the aggregated sensor observations from the CH. Under the assumption of Rayleigh and Rician fading channels, we derive the outage probability at the UAV and identify the time ratio of the proposed time-block structure that minimizes the outage probability. Furthermore, we propose an effective algorithm to generate a solution for CH selection process. Finally, we demonstrate the achievable outage probability, throughput and the service range of the UAV for a given random setup of WSN through the theoretical and simulation results obtained.

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Device-to-Device (D2D) communications underlaying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve the data rate of the cell-edge user equipments (CEUEs). In this paper, we propose a mode selection scheme for wireless powered D2D communications underlaying UAV to improve the energy efficiency (EE) of a network. Here, the cell- center user equipments (CCUEs) first harvest energy from the radio frequency (RF) signals of the UAV, and then communicate with the CEUEs using UAV as a relay. Also, to support the massive connectivity, the CCUEs communicate directly with the CEUEs using non-orthogonal multiple access (NOMA). We formulated the problem as a mixed integer non-linear programming (MINLP) form, and then divide it into three sub-problems. In the first sub- problem, an optimal value of time allocation for energy harvesting (EH) and information transmission (IT) in relay or D2D mode is estimated using Hessian and Lagrangian method to enhance the gain of the CCUEs and CEUEs, respectively. Second, a Golden section search is used to obtain an optimal mode selection on the basis of their EH from the UAV. Finally, the UAV trajectory is optimized using the successive convex approximation (SCA) technique to minimize the energy consumption and maximize the enrgy harvesting. Numerical results demonstrated that the proposed scheme achieve better results as compared to the ex- isting conventional NOMA and orthogonal multiple access (OMA) schemes.

Drone-assisted backhaul networks are a new solution to for 5G data service. However, there are many challenges for these applications. In this paper, we propose a smart antenna-based multi-hop highly-energy-efficient Dynamic Spectrum Access (DSA) approach to drone-assisted backhaul networks for 5G. Firstly, we use the DSA technology to makes full use of the spectrum resources for these applications. Secondly, the smart antenna is introduced to reduce the interference and improves the stability of communications. The sleeping mechanism is exploited to reduce the power consumption and raise energy efficiency of networks. Finally, we propose an algorithm to effectively assign the channel in order to obtain optimal energy efficiency. Simulation results show that our approach is feasible and effective.

On the basis of the Internet of Things (IoT) applications, several variants such as Internet of Energy (IoE), Internet of Vehicles (IoV), Internet of Drones (IoD), Internet of Medical Things (Internet of Health, IoMT) and Internet of Intelligent Battlefield Things (IoIBT) are developed. The smart devices involved in IoT can also utilize artificial intelligence algorithm (i.e., machine learning) to make the communication environment more effective and efficient. Intelligent Battlefield Things (IBT) communication environment is an example of that kind. It consists of smart and intelligent drones, which monitor the activities of the enemies and retaliate accordingly. Hence, we need very less or negligible human involvement. IBT communication environment is very suitable for the purpose of battlefield communication (i.e., war zone, target tracking and hitting, border area surveillance and retaliation). However, IBT has certain drawbacks, because it may be vulnerable to different types of attacks, such as replay, man-in-the-middle, impersonation, data modification, data leakage attacks, etc. Therefore, it becomes essential to provide a security mechanism to secure the communication that happens in IBT environment. To achieve this purpose, we propose a secure communication framework for the blockchain of IBT environment. In addition, the benefits of applications of blockchain of IBT are discussed. The issues and challenges of blockchain of IBT environment are also highlighted. Finally, a security analysis of the proposed framework is conducted to depict its resilience against possible attacks.

Recently, unmanned aerial vehicles (UAVs) become a promising enabler for building on-demand deployable and cost-effective wireless communication systems in various real-world applications. However, the openness of the aerial communication environment may render UAV communications more vulnerable to eavesdropping as compared to its terrestrial counterparts. Existing literature on UAV secure communications mainly focused on the single- or dual-UAV scenarios, and the secure communication problem for multiple networked UAVs still remains largely unexplored, partly due to the challenges caused by the complicated network topology and the mutual dependence among UAVs. With this consideration, a joint friendly jamming and bandwidth allocation algorithm is proposed in this work, and to the best of our knowledge, this work is among the first to study the secure communication problem for UAV networks. In addition to the analysis, extensive simulations are conducted to corroborate the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm can automatically direct the friendly jammer UAV moving to a favorable location in adaptive to the network topology and guide the other relay UAVs cooperatively adjusting their bandwidth accordingly, leading to a substantial improvement of the secure communication capability of the UAV network.

This talk does not have an abstract.

In recent years, routing algorithms have attracted tremendous research attentions in Flying Ad hoc Networks (FANETs) in order to provide efficient networking to fulfill the demand of Intelligent Transport System (ITS). Due to the lack of a global network information in FANETs, existing routing algorithms still have many issues in finding the optimal next-hop node, suffering low packet delivery rate and high delay. With the inclusion of Flying-Software Defined Network (F-SDN), providing global network information has eased the routing and forwarding of data for Unmanned Aerial Vehicles (UAVs). In this paper, we introduce a new routing algorithm by our Modified Simulated Annealing (MSA) algorithm to increase performance of networking in dense environments. The experimental results show the proposed algorithm outperforms the standard algorithm in the finding of routing paths in F-SDNs.

Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) is considered to offer computational capabilities to the resource-constraints end-users. In this paper, we study the task offloading strategy in UAV-enabled MEC systems, where end-users can offload the computation-intensive tasks to the UAV to minimize the overall costs of the delay and energy consumption. The end-users either process the task by itself or offload the tasks to the UAV that acts as a computing access point. However, due to the computation bottleneck and limited channel capacity between UAV and the end-users, it becomes a challenging issue to offload the tasks to the UAV. Thus, to find the optimal offloading decision for the tasks generated by the end-users, we build a distributed deep neural network framework. For faster convergence of the training process, we use the optimal generated offloading decision using a Quadratically Constrained Linear Program (QCLP) with semidefinite relaxation (SDR). The extensive simulation results show that the offloading decision generated by the trained DNN can achieve the near-optimal performance with numerous system parameter settings.

Unmanned aerial vehicles (UAVs) or drones are an important component of 5G and beyond 5G communications network. In this paper, we have proposed the deployment of UAV network for video surveillance over an inaccessible region that lacks cellular infrastructure. The remote surveillance of such a region is facilitated by streaming the surveillance video via the ground control station (GCS) connected using a 5G backhaul. We have developed the UAV wireless network and obtained the optimal topology of UAV grid (with the the least number of drones) for such a surveillance requirement. We achieve zero blind spots during surveillance by obtaining a suitable topology of UAV grid with minimum number of UAVs using the concept of Field of View (FOV) of the on-board camera. A wireless network for the UAV grid is set up using Raspberry Pi mounted on each UAV. The interUAV distance in the network has been fixed based on acceptable packet loss and Quality-of-Experience (QoE) for video streaming. The results achieved from the experimental setup showed that the QoE for the video streaming falls below acceptable limits at 4% packet loss. Hence, the inter-UAV distance should be such that the packet-loss does not exceed the threshold limit of 4%. The paper also brings out the necessary conditions to avoid overlap in coverage area between neighbouring UAVs, thereby, optimising the number of drones to cover an area of interest.

The use of unmanned aerial vehicle (UAV) as a dynamic node in point to point communication of 5G and beyond 5G cellular networks is an emerging technology. It provides a reliable and efficient mode of wireless communication network in the sky. Existing architectures include single tier and multi-tier drone architecture. Major challenges in multi-tier architecture is in establishing an air traffic control system and in efficient resource sharing between the drones. To overcome the challenges associated with the multitier drone and cellular drone networks, a bidirectional multitier cognitive swarm drone network (BMCSDN) is proposed and investigated in terms of spectrum efficiency and allocation of optimized altitude and power for down link transmission. The proposed method uses the cognitive swarm drone (CSD) concept and provides enhancement of capacity and coverage with optimized transmission efficiency. The work also analyses the effect of interference when all CSDs transmit and receive with static and dynamic frequency allocations.

A well-structured smart parking system is an important component of smart cities that enables effective traffic management. Integrating several enabling technologies like Internet of Things (IoT), Unmanned Aerial Vehicle (UAV) and 5G communications can facilitate monitoring and management of parking spaces more accurately. In this paper, we propose a smart parking system that uses multiple sensors to monitor the occupancy of parking spaces, assisted by observations from UAVs to increase accuracy. The system includes interconnected wireless sensor nodes placed across each parking slot and an UAV connected to a central node. Multiple sensors assisted by UAV increase the accuracy of the system and lowers false negatives and false positives.

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