To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

As a core technique of machine learning, deep neural networks (DNNs) have been extensively used in today's mobile applications. However, users' mobile devices (MDs) have limited capabilities to execute computation-intensive DNN inference operations and meet the latency constraints. Offloading part of DNN computations to edge servers (ESs) in a mobile edge computing (MEC) network can mitigate these challenges. However, prior studies on offloading either overlook the varying computation demands and output data sizes for different layers of neural networks or only consider split DNN offloading based on a single user or a single ES. Driven by the question of how to partition multiple parallel DNN inferences and offload multiple partitioned DNN processes in a multi-user multi-ES network, in this paper, we design a distributed scheme that jointly optimizes user-ES association, DNN layer-level partitioning, computing and wireless communication resource allocation, and offloading. Specifically, MDs and ESs make decisions to maximize their own utilities based on a multi-leader multi-follower Stackelberg game by leveraging DNN layer characteristics and taking account of multi- server heterogeneous network environments, computation load, and available resources. The evaluation results show that the proposed joint optimization scheme can significantly improve the performance of DNN inferences, compared to the commonly used benchmarks.

Mobile edge computing (MEC) integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. This paper investigates the task assignment problem for cooperative MEC networks in which a set of geographically distributed heterogeneous edge servers not only cooperate with remote cloud data centers but also help each other to jointly process user tasks. We introduce a novel stochastic MEC cooperation framework to model the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment optimization problem is formulated by taking into consideration dynamic network states, uncertain node computing capabilities and task arrivals, as well as the heterogeneity of the involved entities. We then develop and compare three task assignment algorithms, based on different deep reinforcement learning (DRL) approaches, value-based, policy-based, and hybrid approaches. In addition, to reduce the search space and computation complexity of the algorithms, we propose decomposition and function approximation techniques by leveraging the structure of the underlying problem. The evaluation results show that the proposed DRL-based task assignment schemes outperform the existing algorithms, and the hybrid actor-critic scheme performs the best under dynamic MEC network environments.

We explore the future of synchronous, multiplayer mobile AR gaming through our game V-Light, which extends current mobile AR game capacities using edge computing. Mobile AR games are currently limited by on-board processing power, while offloading operations to the cloud introduces high latency costs. This is a critical issue for games needing real-time response to player input. V-Light demonstrates how mobile AR games can leverage the power of edge computing, bringing computational resources closer to the user, keeping latency low and bandwidth high. We share our development toolkit, analyze the design and development of V-Light through the lens of an existing model for shared-world mobile AR, and demonstrate that edge computing can provide a “time machine” that lets game designers prototype mobile AR games for devices that do not yet exist.

Recent advances in Blockchain-based Federated Learning (FL) aim to address the inherent limitations of traditional FL, such as single node failure and the lack of an appropriate incentive mechanism. This approach replaces the central parameter server in FL with a blockchain that stores and disseminates updated models. However, its decentralized nature introduces significant communication and storage over-head, which considerably constrains its practical application. Additionally, as it allows participants to contribute to the shared model by training locally using private data, it is especially prone to privacy leaks and poisoning attacks. This study introduces a novel framework, BlockFed, designed to substantially reduce overhead and mitigate vulnerabilities in blockchain-based FL systems.

Federated Learning (FL) has been adopted in practical network applications and plays a critical role. As FL allows participants to contribute to the global model by training locally with private data, it is known particularly vulnerable to neural backdoor attacks. This paper proposes a new defense, ScanFed, against neural backdoor attacks to FL systems. It leverages the synchronous nature of FL to effectively single out malicious neuron candidates and further validate if they indeed hijack the model's behaviors. Compared to existing neural backdoor defenses, ScanFed has the following distinct properties. First, it is extremely computation-friendly that is six orders of magnitude faster than state-of-the-art behavior-based backdoor defenses, rendering it highly suitable for large-scale FL systems. Second, it inherits the precise nature of behavior-based backdoor detection, making it significantly more effective than similarity-based defenses against advanced attacks. Third, it is robust to biased models uploaded by clients with non-IID (Independent and Identically Distributed) data, which is very common in practical FL systems. In addition, it is a plug-n-play scheme that can be seamlessly integrated into existing FL systems. To the best of our knowledge, this is the first behavior-based defense that enables scalable, efficient and accurate neural backdoor detection of FL systems in non-IID scenarios. This work delivers a ScanFed prototype and fully tests it in various settings of datasets, neural architectures, and backdoor attacks. The experiments demonstrate ScanFed achieves competitive accuracy and minimal detection time.

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed.

Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don’t Let Me Down! (DLMD), an algorithm that promotes offloading robot functions to the Cloud when possible to minimize the consumption of Edge resources. Additionally, DLMD takes the appropriate migration, traffic steering, and radio handover decisions to meet robotic service requirements as strict latency constraints. In the paper, we formulate the optimization problem that DLMD aims to solve, compare DLMD performance against the state of the art, and perform stress tests to assess DLMD performance in small & large networks. Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is optimal in a local warehousing use case; and (iii) consumes only 5% of the Edge resources upon network stress.

Blockchain and distributed ledger technology started as a decentralized infrastructure to enable and manage digital currency like Bitcoin without relying on a central authority. One of the attractive features provided by blockchain technology is its append-only “immutability” feature, which means the stored data cannot be modified or manipulated by any means once it is validated in the blockchain ledger. Such immutability helps traceability, auditing, and non-repudiation, which builds decentralized trust among un-trusted parties. Despite that, immutability if misused could lead to the permanent existence of sensitive information and misinformation in the blockchain. Incidents like broadcasting illegal content have already taken their place in blockchain systems. Such incidents call for prompt solutions for mitigation. One emerging research theme, “redactable distributed ledgers” such as redactable blockchain provides approaches for modifying ledgers with certain controllability. This article aims to survey the current research landscape about redactable distributed ledgers. We will first describe the motivations behind redactable distributed ledgers. Compared to other relevant surveys, we comprehensively summarized and briefly explained the underlying technologies for supporting redactable distributed ledgers. We mainly focused on chameleon hash-based redactable blockchain structure and classifications, with detailed comparisons and illustrations. Further, we tackled new distributed ledger structures, including the new state-of-the-art block matrix structure. Furthermore, new applications that can be enabled by redactable distributed ledgers and future research directions are discussed in detail. This article emphasizes the motivation of utilizing the redactable distributed ledgers in several critical applications to mitigate misuse of immutability features threatening the original known design of distributed ledgers.

Federated learning (FL) is a collaborative machine learning framework to enable different clients such as Internet of Things (IoT) devices to participate in a machine learning model training process, while preserving data privacy. Client selection is critical to determine the performance of FL. Most of the existing client selection methods aim to maximize the number of selected clients, who can upload their local models before the deadline, in each global iteration, thus potentially accelerating the model convergence rate. However, these methods ignore the fact that most of the IoT devices are powered by on-board batteries and harvested green energy from the environment to prolong battery life. Hence, clients selected by these methods may not have sufficient energy to upload their local models in a global iteration or are unable to participate in the training process in the near future due to battery drainage. In this paper, we propose a novel client selection, entitled “EnerGy-AwaRe CliEnt SElection for Green FeDerated Learning (GREED)”, to optimize the trade-off between maximizing the number of selected clients and minimizing the energy drawn from batteries for the selected clients, while ensuring that all the selected clients have sufficient energy to upload their local models before the deadline. The performance of GREED is validated via extensive simulations.

In addition to CPRI, new functional splits have been defined in 5G creating diverse fronthaul transport bandwidth and latency requirements. These fronthaul requirements shall be fulfilled simultaneously together with the backhaul requirements by an integrated fronthaul and backhaul transport solution. In this paper, we analyze the technical challenges to achieve an integrated transport solution in 5G and propose specific solutions to address these challenges. These solutions have been implemented and verified with pre-commercial equipment. Our results confirm that an integrated fronthaul and backhaul transport dubbed Crosshaul can meet all the requirements of 5G fronthaul and backhaul in a cost-efficient manner.

This paper investigates the task management for cooperative mobile edge computing (MEC), where a set of geographically distributed heterogeneous edge nodes not only cooperate with remote cloud data centers but also help each other to jointly process tasks and support real-time IoT applications at the edge of the network. Especially, we address the challenges in optimizing assignment of the tasks to the nodes under dynamic network environments when the task arrivals, node computing capabilities, and network states are nonstationary and unknown a priori. We propose a novel stochastic framework to model the interactions of the involved entities, including the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment problem is formulated and the algorithm is developed based on online reinforcement learning to optimize the performance for task processing while capturing various dynamics and heterogeneities of node computing capabilities and network conditions with no requirement for prior knowledge of them. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is proposed, which are incorporated with reinforcement learning to reduce the search space and computation complexity. The evaluation results demonstrate that the proposed online learning-based scheme outperforms the state-of-the-art benchmark algorithms.

Wireless data traffic, especially video traffic, continues to increase at a rapid rate. Innovative network architectures and protocols are needed to improve the efficiency of data delivery and the quality of experience (QoE) of mobile users. Mobile edge computing (MEC) is a new paradigm that integrates computing capabilities at the edge of the wireless network. This paper presents a computation‐capable and programmable wireless access network architecture to enable more efficient and robust video content delivery based on the MEC concept. It incorporates in‐network data processing and communications under a unified software‐defined networking platform. To address the multiple resource management challenges that arise in exploiting such integration, we propose a framework to optimize the QoE for multiple video streams, subject to wireless transmission capacity and in‐network computation constraints. We then propose two simplified algorithms for resource allocation. The evaluation results demonstrate the benefits of the proposed algorithms for the optimization of video content delivery.