Intelligent Offloading in Blockchain-Based Mobile Crowdsensing Using Deep Reinforcement Learning

Mobile Crowdsensing (MCS) utilizes sensing data collected from users' mobile devices (MDs) to provide high-quality and personalized services, such as traffic monitoring, weather prediction, and service recommendation. In return, users who participate in crowdsensing (i.e., MCS participants) get payment from cloud service providers (CSPs) according to the quality of their shared data. Therefore, it is vital to guarantee the security of payment transactions between MCS participants and CSPs. As a distributed ledger, the blockchain technology is effective in providing secure transactions among users without a trusted third party, which has found many promising applications such as virtual currency and smart contract. In a blockchain, the proof-of-work (PoW) executed by users plays an essential role in solving consensus issues. However, the complexity of PoW severely obstructs the application of blockchain in MCS due to the limited computational capacity of MDs. To solve this issue, we propose a new framework based on Deep Reinforcement Learning (DRL) for offloading computation-intensive tasks of PoW to edge servers in a blockchain-based MCS system. The proposed framework can be used to obtain the optimal offloading policy for PoW tasks under the complex and dynamic MCS environment. Simulation results demonstrate that our method can achieve a lower weighted cost of latency and power consumption compared to benchmark methods

RAFL: A Robust and Adaptive Federated Meta-Learning Framework Against Adversaries

With the emergence of data silos and increasing privacy awareness, traditional centralized machine learning provides limited support. Federated learning (FL), as a promising alternative machine learning approach, is capable of leveraging distributed personalized datasets from multiple clients to train a shared global model in a privacy-preserving manner. However, FL systems are vulnerable to attacker-controlled adversarial clients that potentially conduct adversarial attacks by uploading unreliable model updates or clients unintentionally uploading low-quality models leading to degraded FL performance and reduced resilience to attacks. In this paper, we propose RAFL: a new robust-by-design federated meta learning framework capable of mitigating adversarial model updates on non-IID data. RAFL leverages 1) a residual rule-based detection method and a Variational AutoEncoder (VAE) learning based detection method combined to distinguish adversarial clients from benign clients. 2) a similarity-based model aggregation method to reduce the likelihood of uploading adversarial models from adversarial clients. 3) multiple learning loops to collaboratively train multiple personalized detection models against adversaries effectively. Experimental results demonstrate that our proposed FL framework is robust by design and outperforms other defensive methods against adversaries in terms of model accuracy and efficiency

Federated Meta Learning for Visual Navigation in GPS-denied Urban Airspace

Urban air mobility (UAM) is one of the most critical research areas which combines vehicle technology, infrastructure, communication, and air traffic management topics within its identical and novel requirement set. Navigation system requirements have become much more important to perform safe operations in urban environments in which these systems are vulnerable to cyber-attacks. Although the global navigation satellite system (GNSS) is a state-of-the-art solution to obtain position, navigation, and timing (PNT) information, it is necessary to design a redundant and GNSS-independent navigation system to support the localization process in GNSS-denied conditions. Recently, Artificial intelligence (AI)-based visual navigation solutions are widely used because of their robustness against challenging conditions such as low-texture and low-illumination situations. However, they have weak adaptability to new environments if the size of the dataset is not sufficient to train and validate the system. To address these problems, federated meta learning can help fast adaptation to new operation conditions with small dataset, but different visual sensor characteristics and adversarial attacks add considerable complexity in utilizing federated meta learning for navigation. Therefore, we proposed a robust-by-design Federated Meta Learning based visual odometry algorithm to improve pose estimation accuracy, dynamically adapt to various environments by using differentiable meta models and tunning its architecture to defense against cyber-attacks on the image data. In this proposed method, multiple learning loops (inner-loop and outer-loop) are dynamically generated. Each vehicle utilizes its collected visual data in different flight conditions to train its own neural network locally for a particular condition in the inner loops. Then, vehicles collaboratively train a global model in the outer loop which has generalizability across heterogeneous vehicles to enable lifelong learning. The inner loop is used to train a task-specific model based on local data, and the outer loop is to extract common features from similar tasks and optimize meta-model adaptability of similar tasks in navigation. Moreover, a detection model is designed by utilizing key characteristics in trained neural network model parameters to identify attacks

Federated meta learning for visual navigation in GPS-denied urban airspace

In this paper, we have proposed a novel FLVO framework which can improve pose estimation accuracy in terms of translational and rotational RMSE drift while reducing security and privacy risks. It also enables fast adaptation to new conditions thanks to the aggregation process of the local agents which operate in different environments. In addition, we have shown that it is possible to transfer an end-to-end visual odometry agent that is trained by using ground vehicle dataset (i.e. KITTI dataset) to an aerial vehicle pose estimation problem for low-altitude and low-speed operating conditions. Dataset size is an important topic that should be considered in both AI-based end-to-end visual odometry applications and federated learning approaches. Although it is demonstrated that federated learning could be applied for visual odometry applications to aggregate the agents that are trained in different environments, more data should be collected to improve the translational and rotational pose estimation performance of the aggregated agents. In our future work, we will evaluate cyber-attack detection performance of the proposed FLVO framework by utilizing multiple learning loops. In addition, dataset size will be expanded by utilizing real flight tests to increase the realm of the training data and to improve the robustness of the proposed federated learning based end-to-end visual odometry algorithm

Location-based Robust Beamforming Design for Cellular-enabled UAV Communications

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for Unmanned Aerial Vehicles (UAVs), which have been widely deployed to conduct various missions, e.g. precision agriculture, forest monitoring and border patrol. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe inter-cell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of Direction-of-arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14dB SINR gain compared with five benchmark beamforming algorithms, including Linearly Constrained Minimum Variance (LCMV), Position-based beamforming, Diagonal Loading (DL), Robust Capon Beamforming (RCB) and Robust LCMV algorithm