Integrated Sensing and Communication (ISAC) is driving the evolution of edge intelligence. In ISAC-enabled wireless edge networks, federated learning (FL) is crucial for realizing edge intelligence by supporting the networks with privacy protection, efficient data management, and dynamic adaptability. Specifically, FL allows distributed computing nodes (e.g., sensor devices) to first train local models by using data collected or sensed via ISAC and subsequently send them to one or multiple aggregation nodes for global model collaboration. However, traditional FL frameworks face significant challenges in the ISAC scenarios. For example, the privacy sensitivity of heterogeneous sensor data and the lack of transparency in model parameter exchange make it difficult to ensure the credibility of local and global models. Sharding distributed ledger technology (DLT), which divides the ledger into smaller and manageable shards, offers a potential solution to address these challenges by utilizing multi-node trust capabilities to facilitate distributed consensus during FL training. In this paper, we propose a trusted FL framework that incorporates sharding DLT within ISAC-enabled wireless edge networks to enhance both model training and consensus performance. Specifically, we develop a theoretical model to examine the interactions between model training performance and network capacities of sensing nodes (e.g., storage, computing, and communication capabilities) based on ISAC’s real-time channel state information. Based on this theoretical model, we design a trusted clustering scheme for aggregating local models. Numerical results demonstrate that in ISAC-enabled wireless edge networks, our proposed scheme significantly increases network throughput for model transmission while ensuring optimal model learning performance compared to some classical baselines.

Federated learning (FL) is integral to advancing edge intelligence by enabling collaborative machine learning. In FL-empowered edge networks, computing nodes first train local models and then send them to an or multiple aggregation node(s) for global model collaboration. However, the trustworthiness of both local and global models in conventional FL frameworks is compromised due to inadequate model security and transparency. Distributed ledger technique (DLT) can address this issue by leveraging multi-nodes trust capabilities to support distributed consensus. However, model training and consensus performance of DLT may significantly degrade due to instability and resource constraints of edge networks. Sharding technique provides an effective approach by dividing the ledger into smaller and manageable shards. In this paper, to improve model training and consensus performance, we propose a trusted FL framework by incorporating sharding DLT into FL frameworks. We construct a theoretical model to investigate the relationship between model training performance, consensus efficiency, and capacity of edge nodes regarding storage, computing and communications. Based on the theoretical model, we propose a trusted clustering scheme to aggregate local models. Numerical results show that our proposed scheme significantly improves network throughput for transmitting models while guaranteeing model learning performance in comparison with some classical baselines.

Hierarchical clustering learning (HCL) extends traditional parameter server-based distributed learning by clustering heterogeneous user equipments (UEs) via cluster nodes (CN s) located at the edge of the network. Currently, most vanilla model dissemination strategies in distributed learning rely on one-to-many transmissions, inevitably consuming excessive precious bandwidth resources. Consequently, communication-efficiency becomes crucial for HCL in resource-constrained edge networks. In this paper, we propose a multistage cooperative model dissemination strategy to sequentially determine the subsets of CNs that can concurrently transmit models during individual scheduling stages, thereby improving communication efficiency in HCL. First, we formulate the strategy design as an optimization problem to minimize the maximum completion time of the slowest straggler in a communication round of HCL. Then, we design an online learning algorithm, called sequential combinatorial multiarmed bandit (SCMAB) to make sequential and combinatorial decisions in individual stages. Numerical results demonstrate the superiority of our proposed strategy over some benchmarks in terms of communication efficiency.

Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.

Due to the distributed characteristics of federated learning (FL), the vulnerability of the global model and the coordination of devices are the main obstacle. As a promising solution of decentralization, scalability, and security, leveraging the blockchain in FL has attracted much attention in recent years. However, the traditional consensus mechanisms designed for blockchain-like proof of work (PoW) would cause extreme resource consumption, which reduces the efficiency of FL greatly, especially when the participating devices are wireless and resource-limited. In order to address device asynchrony and anomaly detection in FL while avoiding the extra resource consumption caused by blockchain, this article introduces a framework for empowering FL using direct acyclic graph (DAG)-based blockchain systematically (DAG-FL). Accordingly, DAG-FL is first introduced from a three-layer architecture in detail, and then, two algorithms DAG-FL Controlling and DAG-FL Updating are designed running on different nodes to elaborate the operation of the DAG-FL consensus mechanism. After that, a Poisson process model is formulated to discuss that how to set deployment parameters to maintain DAG-FL stably in different FL tasks. The extensive simulations and experiments show that DAG-FL can achieve better performance in terms of training efficiency and model accuracy compared with the typical existing on-device FL systems as the benchmarks.

Mobile crowdsensing (MCS) is a promising paradigm where sensor-embedded mobile devices are exploited for collecting and sharing environmental data. In MCS, the participating mobile devices collect the required data, (pre-)process the data and transmit the data and/or pre-processing results to the server for further processing. In wireless edge networks, transmission and/or processing of sensed data may be unsuccessful due to unstable wireless channels, limited bandwidth, energy and computation resources. To optimize MCS performance, it is imperative to jointly design the data sensing, processing and transmission policies under resource constraints. In this paper, we propose a joint sensing, communication and computation (JSCC) framework for multi-dimensional resource constrained MCS systems. We formulate the JSCC design as an optimization problem, by jointly controlling the data sensing, transmission and computation offloading processes in the system. Simulation results show that the proposed JSCC framework significantly outperforms several baseline solutions without jointly considering data sensing-transmission-computation and/or multi-dimensional resource limitations.

Blockchain has been regarded as one of the promising technologies to address trust concern in data-driven mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. However, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to bridge the gap between MCS and blockchain. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize normal nodes to maintain trustworthiness of interactions. Assisted by the trust assessment, trust decision is further made to filter untrusted nodes from participating in blockchain process. Simulation experiments are conducted to validate the effectiveness and efficiency of the proposed TPM-enabled blockchain in terms of contribution rate and consensus accuracy.

Beyond fifth-generation (B5G) and future networks face the challenges of spectral, energy and cost efficiency for large-scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in B5G era. Unlike existing work on AmBC which mostly focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of radio frequency (RF), backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association, from the perspective of networking. Assuming whether channel information is available a-priori, we propose online and offline access control strategies respectively. For offline access control strategy, we leverage the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the concave one, and design a distributed access control strategy called DCA-S. Furthermore, for the case that channel information is assumed to be unknown in advance due to the dynamics of primary and backscatter networks, we design a combinatorial multi-armed bandit (CMAB) access control strategy (CMAB-S). Numerical results show that the proposed DCA-S and CMAB-S can achieve significant performance improvement of the system in both cases of available and unavailable channel information compared with benchmark schemes.

The beyond fifth-generation (B5G) and future wireless networks face the challenges of spectral, energy and cost efficiency for large scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in beyond B5G era. In this paper, we consider a multi-nodes scenario where a backscatter network is symbiotic with primary network consisting of multiple ambient radio frequency (RF) sources, thereby allowing the system to use the appropriate RF to support high throughput and wide coverage for IoT devices. Unlike existing work on AmBC, which focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of RF, backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association of BDs and IoT devices, for multi-RF backscatter network from the perspective of maximizing device transmission rate. Under the guarantee of quality of service (QoS), we develop an access control strategy with aim of maximizing the weighted sum of primary and backscatter transmission rates, and design a distributed access control strategy called DCA-S, by using the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the solvable concave subproblems. Numerical results show that the proposed DCA-S can achieve significantly performance improvement of the system compared with benchmark schemes.

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

In Mobile Edge Computing based Internet of Vehicle (MEC-IoV), how to assign suitable task to MEC to satisfy the requirement under the constraints is the most important issue. Usually, in the practical system, due to the random movement and changing traffic load, the connection between vehicle user and MEC server is uncertain, and this makes it difficult to assign the suitable task in offloading. Considering the uncertainty and the corresponding impact, this paper proposes a stochastic programming methodology to make offloading policy. To this end, the task offloading problem is formulated as a Multi-Stage Stochastic Programming (MSSP) minimizing the expected cost function caused by offloading while considering the energy consumption. The experimental results show that the proposed MSSP method for offloading decision-making can effectively reduce the total cost in stochastic scenarios.