EdgeIoT represents an approach that brings together mobile edge computing with Internet of Things (IoT) devices, allowing for data processing close to the data source. Sending source data to a server is bandwidth-intensive and may compromise privacy. Instead, federated learning allows each device to upload a shared machine-learning model update with locally processed data. However, this technique, which depends on aggregating model updates from various IoT devices, is vulnerable to attacks from malicious entities that may inject harmful data into the learning process. This paper introduces a new attack method targeting federated learning in EdgeIoT, known as data-independent model manipulation attack. This attack does not rely on training data from the IoT devices but instead uses an adversarial variational graph auto-encoder (AV-GAE) to create malicious model updates by analyzing benign model updates intercepted during communication. AV-GAE identifies and exploits structural relationships between benign models and their training data features. By manipulating these structural correlations, the attack maximizes the training loss of the federated learning system, compromising its overall effectiveness.

Cloud-of-clouds storage systems are widely used in online applications, where user data are encrypted, encoded, and stored in multiple clouds. When some cloud nodes fail, the storage systems can reconstruct the lost data and store it in the substitute nodes. It is a challenge to reduce the latency of data recovery to ensure data reliability. In this paper, we adopt a Reinforcement Learning-based Data Recovery (RLDR) approach to reduce the regeneration time. By employing the Monte-Carlo method, our approach can construct the tree-topology-based regeneration process, a.k.a. regeneration tree, to effectively reduce the regeneration time. Through rigorous analysis, we apply the information flow graph to optimize the inter-cloud traffic for a given regeneration tree. To verify the merit of RLDR, We conduct extensive experiments on real-world traces. Experiments demonstrate that RLDR can significantly accelerate the regeneration process. Specifically, RLDR can reduce the regeneration time by up to 92% and increase the throughput by up to twelve-fold, compared to the prior art.

Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and lowlatency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequencydomain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-EFT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE'05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments.

Cloud-of-clouds storage can enhance the data security and reliability of online applications by encrypting, encoding, and distributing user data across multiple clouds. Fast transferring large volumes of data through networks with limited bandwidths remains a practical challenge, especially in the event of disaster backup. To address this, we model a data storage process using an information flow graph and estimate inter-cloud traffic. We propose a new Network Coding-based Cloud-of-Clouds Backup (NC3B) framework, which enables collaborative encoding and data exchange among backup clouds to utilize inter-cloud bandwidth efficiently. We analytically corroborate that NC3B effectively reduces write operation latency. We also demonstrate the NC3B framework by incorporating two cutting-edge Reed-Solomon (RS) based data storage techniques, namely All-Or-Nothing Transform-RS (AONT-RS) and Converge AONT-RS (CAONT-RS), referred to as Network coding-based Backup AONT-RS (NBAONT-RS) and Network coding-based Backup CAONT-RS (NBCAONT-RS), respectively. To validate our approach, we deploy a real-world prototype storage system on Amazon EC2 using a cluster trace set, and underscore the effectiveness of NC3B, showcasing reductions in latency of up to 50% compared to state-of-the-art approaches, alongside throughput improvements of up to 98%. These findings underscore the benefits of NC3B in real-world storage scenarios.

Allowing for energy sharing among base stations (BSs), we investigate a distributed BS system equipped with renewable power units and energy storage batteries. While offering significant benefits, this raises concerns about privacy protection. By leveraging the Laplace mechanism, our differential privacy (DP)-based method safeguards energy consumption data related to processing data tasks from different BSs. To address a long-term average cost minimization problem, we propose a distributed online algorithm for efficient energy sharing. We demonstrate that the boundary constraints of energy storage batteries can still be satisfied by choosing parameters appropriately. We provide a theoretical bound for the optimality gap and validate the effectiveness of our theoretical results. Numerical results indicate our algorithm can potentially reduce the average costs of BSs by up to 34%.

This paper studies the potential of tightly coupling the Internet-of-Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on non-renewable energy by diversifying power supply. Rechargeable batteries at end devices are considered for holistic energy management of the system. We jointly optimize the transmit powers and battery (dis)charging decisions of the devices, the receive beamformer of the edge servers, and the dynamic generation of different energy types. The alternating direction method of multipliers (ADMM) is applied to support distributed optimization of (dis)charging decisions at individual devices. The Karush–Kuhn–Tucker (KKT) conditions are applied to deliver semi-closed-form power control of the devices. Simulations demonstrate significant improvement of the algorithm in renewable energy utilization and cost saving, compared to the existing techniques.

Multi-cloud storage is recently a viable approach to solve the vendor lock-in, reliability, and security issues in cloud storage systems. As a key concern, data placement influences the cost and performance of storage services. Yet, in practice it remains challenging to address the huge solution space. Previous studies typically focus on constructing efficient data placement schemes based on the predicted pattern of workloads or assuming fully a-priori known network conditions. They cannot be easily applied in multi-cloud storage scenarios, which typically involve dynamic network conditions and time-varying workloads. To this end, we formulate the data placement optimization in a combinatorial multi-arm bandit (CMAB) perspective and solve it by learning placement strategy online. In contrast to a stationary setting where reward distributions are unknown but identical over time, we consider a realistic multi-cloud environment with non-stationary conditions, i.e., reward distributions change over time. To swiftly accommodate this, we propose an adaptive window combinatorial upper confidence bound based data placement (AW-CUCB-DP) scheme to reduce latency and cost. In AW-CUCB-DP, a simple and efficient change detector, i.e.,Page-Hinkley testwith forgetting mechanism (FM-PHT), is employed to enable variable-size sliding windows to handle both gradual and abrupt variations in network conditions or workloads. We establish that AW-CUCB-DP is asymptotically optimal in the non-stationary multi-cloud environment. Trace-driven experiments further verify that our scheme outperforms alternatives, especially in highly dynamic environments.

By orchestrating resources of edge and core network, the delays of edge-assisted computing can decrease. Offloading scheduling is challenging though, especially in the presence of many edge devices with randomly varying link and computing conditions. This paper presents a new online learning-based approach to the offloading scheduling, where multi-agent multi-armed bandit (MA-MAB) learning is designed to exploit the randomly varying conditions and asymptotically minimize the computing delay. We first propose a combinatorial bandit upper confidence bound (CB-UCB) algorithm, where users collectively feed back the observed delays of all edge devices and links. The optimistic bound of the delay is derived to facilitate centralized offloading scheduling for all users. In addition, we put forth a distributed bandit upper confidence bound (DB-UCB) algorithm, where users take random turns to make conflict-free, distributed selections of edge devices. The optimistic confidence bound of each user is developed to allow the user’s selection only based on its own observations and decisions. Furthermore, we establish the asymptotic optimality of the proposed algorithms by proving the sublinearity of their regrets, and that the random turns the users take to make decisions do not compromise the asymptotic optimality of the DB-UCB algorithm, as corroborated by numerical simulations.

The exponential growth of data traffic over mobile internet leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we leverage an online learning approach to develop a fronthaul aware upper confidence bound (FA-UCB) algorithm that is capable of dealing with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal scheme with full a-priori knowledge. In addition, numerical results demonstrate that the proposed FA-UCB scheme can significantly outperform the existing alternatives in terms of learning regret.