Recently, federated learning (FL) techniques have enabled multiple users to train machine learning models collaboratively without data sharing. However, existing FL algorithms suffer from the communication bottleneck due to network bandwidth pressure and/or low bandwidth utilization of the participating clients in both centralized and decentralized architectures. To deal with the communication problem while preserving the convergence performance, we introduce a communication-efficient decentralized FL framework GossipFL. In GossipFL, we 1) design a novel sparsification algorithm to enable that each client only needs to communicate with one peer with a highly sparsified model, and 2) propose a new and novel gossip matrix generation algorithm that can better utilize the bandwidth resources while preserving the convergence property. We also theoretically prove that GossipFL has convergence guarantees. We conduct experiments with three convolutional neural networks on two datasets (IID and non-IID) under two distributed environments (14 clients and 100 clients) to verify the effectiveness of GossipFL. Experimental results show that GossipFL takes less communication traffic for 38.5% and less communication time for $49.8$ % than state-of-the-art solutions while achieving comparative model accuracy.

With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.

Vehicular ad-hoc network enhances driving safety and enables various intelligent transportation applications by adopting the revolutionary vehicular wireless communication technology. This has attracted a lot of attentions from both academia and industry in recent years. Given the sophistication of vehicular manufacturing and the heterogeneity of intelligent transport terminals, performing vehicular authentication is of great importance. The existing schemes have largely considered vehicle security and authentication within a single administrative domain, which lacks supervision of the authority and entity in the intelligent transportation system. In this article, we propose a multidomain vehicular authentication architecture by introducing blockchain technique to build distributed trust and share cross-domain information among multiple administrative domains. To guarantee the anonymity and traceability, a pseudonym-based privacy-preserving authentication method is proposed. Specifically, considering the supervision of authority and the resilience to key escrow, we design a two-phase pseudonym distribution mechanism with the assistance of a roadside unit (RSU) proxy. We conduct in-depth security analysis by comparing with existing works and deploy experiments to show the efficiency and feasibility of the proposed scheme in the multidomain scenario.

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust has become increasingly apparent and received considerable scholarly attention in recent years. With the occurrence of various security incidents, data leakage accidents, and service fraud, which seriously affects the quality of service and system efficiency of IoT, trust is fast becoming a key issue in IoT system. In addition to the security and efficiency, trust further contains the reliability, attack resistance, fairness, flexibility, and incentive, resulting in a wide range of new researches from the trust management framework to the quantification method. In this work, various dimensions of trust, including definition, composition, aggregation, and computation are introduced and analyzed. We focus on comprehensive comparison of the state-of-the-art trust management researches and the related applications. Besides, we explore and discuss the important challenges, including performance bottleneck, bidirectional trust, dynamic changes of context, privacy preserving, and cross-domain issue. Some potential enabling technologies for trust management are further analyzed. The objective of this article is to comprehend the trust issue and the composition of trust management in IoT, and illustrate the difference of existing work, thereby motivating further research interest in this field.

Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg⁻¹, R² = 44.24%), soil AP (RMSE = 40.808 mg kg⁻¹, R² = 42.91%) and soil AK (RMSE = 67.464 mg kg⁻¹, R² = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg⁻¹, R² = 68.51%), soil AP (RMSE = 29.62 mg kg⁻¹, R² = 69.30%) and soil AK (RMSE = 49.67 mg kg⁻¹ and R² = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

This paper proposes a new closed-form design of wideband infinite impulse response digital integrators using numerical integration rules that constructed via trigonometric interpolation. The proposed method can be used to design integrators satisfying prescribed frequency-domain specifications. Design examples with various specifications are given. Simulation comparisons with existing methods are presented to demonstrate the effectiveness of the new design method. The proposed method is experimentally verified on two real applications, Rogowski current transducer and strapdown inertial navigation system. The results show that our method can efficiently improve the integration bandwidth under low sampling rates compared to existing ones, and hence is attractive for resource-constrained applications that require wideband integration.