A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities

A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles

While the convergence of Artificial Intelligence (AI) techniques with improved information technology systems ensured enormous benefits to the Internet of Vehicles (IoVs) systems, it also introduced an increased amount of security and privacy threats. To ensure the security of IoVs data, privacy preservation methodologies have gained significant attention in the literature. However, these strategies also need specific adjustments and modifications to cope with the advances in IoVs design. In the interim, Federated Learning (FL) has been proven as an emerging idea to protect IoVs data privacy and security. On the other hand, Blockchain technology is showing prominent possibilities with secured, dispersed, and auditable data recording and sharing schemes. In this paper, we present a comprehensive survey on the application and implementation of Blockchain-Enabled Federated Learning frameworks for IoVs. Besides, probable issues, challenges, solutions, and future research directions for BC-Enabled FL frameworks for IoVs are also presented. This survey can further be used as the basis for developing modern BC-Enabled FL solutions to resolve different data privacy issues and scenarios of IoVs

A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Resource and Interference Management in UAV-Cellular Network

The future Sixth-Generation (6G) network is anticipated to extend connectivity for millions of Unmanned Aerial Vehicles (UAVs) worldwide and support various innovative use cases, such as cargo transport, inspection, and intelligent agriculture. The terrestrial cellular networks provide real-time information exchange between UAVs and Ground Control Stations (GCS), which facilitates the evolution of UAV communication systems while bringing promising economic benefits to cellular network operators. However, the tremendous growth in the UAV data traffic, with diverse and stringent service requirements, would add another pressure on the already congested terrestrial cellular network that is facing a rigorous challenge to increase network capacity with the limited spectrum resources. Moreover, since Macro Base Station (MBS) antennas are typically downtilt, UAVs, which are served by the MBS antenna’s side lobes, suffer from sharp signal fluctuations causing throughput reduction and coverage drop. Besides, due to the Line-of-Sight (LoS) between UAVs and MBSs, UAVs experience higher uplink/downlink interference compared to ground Cellular Users (CUs). In this thesis, we propose two novel aerial network architectures in which we design efficient interference and resource management strategies to support the UAV Quality-of-Service (QoS) guarantee while considering different types of interference. Firstly, we propose a novel standalone aerial multi-cell network where multiple UAV Base Stations (UAV-BSs) provide cellular services to UAV Users by reusing the licensed and unlicensed spectrum. Our objective is to jointly optimize the subchannels and power allocations of UAV-Users in the licensed and unlicensed spectrum to maximize the network uplink sum rate, considering inter-cell interference, co-existence with terrestrial cellular and WiFi systems, and the QoS of UAV-Users. We prove mathematically that the formulated optimization problem is an NP-hard problem. Therefore, the original problem is decomposed into three subproblems to solve it efficiently. We first use convex optimization and the Hungarian algorithm to obtain the global optimal of power and subchannel allocations in the licensed spectrum, respectively. Then, we design a matching game with externalities and coalition game algorithms to obtain the Nash stable of the subchannel allocation in the unlicensed band. Local optimal power assignment in the unlicensed spectrum is obtained using the successive convex approximation method. Lastly, we develop an iterative algorithm to solve the three subproblems sequentially until convergence is reached. Simulation results demonstrate that the proposed algorithm achieves a significantly higher uplink sum rate compared with other resource allocation schemes. Moreover, the proposed algorithm improves the network throughput and capacity by nearly two times comparing to the Long Term Evolution-Advanced (LTE-A). Secondly, we propose a novel integrated aerial-terrestrial multi-operator network. In the network, each operator deploys a number of UAV-BSs besides the terrestrial MBS, where each BS reuses the operator’s licensed spectrum to provide downlink connectivity for UAV-Users. Moreover, the operators allow the UAV-Users, whose demand cannot be satisfied by the licensed band, to compete with others to obtain bandwidth from the unlicensed spectrum. Given the QoS requirements of UAV-Users, we aim to maximize the total sum rate by jointly optimizing user association, BSs transmit power, and dynamic spectrum allocation considering inter-cell interference in the licensed band and inter-operator interference in the unlicensed spectrum. In particular, we divide the resulting non-convex Mixed-Integer Non-Linear Programming (MINLP) optimization problem into two sequential subproblems: user association and power control in the licensed spectrum; and dynamic spectrum allocation and user association in the unlicensed spectrum. Furthermore, the former subproblem is decomposed into multiple subproblems for distributed and parallel problem-solving. Since the resulting former subproblem is still a non-convex MINLP problem, we propose a distributed iterative algorithm consisting of a matching game, coalition game, and successive convex approximation technique to solve it. Afterwards, in the latter subproblem, we first use a matching game to associate UAV-Users with the UAV-BSs for each operator in the unlicensed spectrum. Then, we propose a three-layers auction algorithm to allocate the unlicensed spectrum among operators dynamically. Extensive simulation results demonstrate that the proposed algorithm in the licensed spectrum significantly improves network throughput per operator than the conventional terrestrial network alone. Moreover, the achieved system throughput of the proposed algorithms in both licensed and unlicensed spectrum is 86.8% higher compared with that of using the licensed spectrum only. In summary, we have proposed integrated aerial-terrestrial network architectures that leverage the aerial network to complete the terrestrial network to serve cellular-connected UAVs by reusing licensed and unlicensed spectrum considering multi-cell and multi-operator scenarios. Under the proposed network architectures, we have investigated the subchannel allocation, UAV-Users’ transmit power, user association, BSs’ transmit power, and dynamic spectrum management to maximize the network throughput considering the QoS of UAV-User. The proposed architectures and algorithms should provide valuable guidelines for future research in designing resource and interference management schemes, improving network capacity, and enhancing spectrum utilization for complex interference environments in integrated UAV-cellular networks

Task Allocation among Connected Devices: Requirements, Approaches and Challenges

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing-intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for the vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game-based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the best-effort worker selection scheme, the proposed scheme can improve the utility of metaverse service provider and the average profit of CDC workers