To improve safety in public transportation, a major issue is how to avoid traffic accidents. To this end, a recent report has demonstrated that more than 90% of accidents in the United States were due to drivers’ abnormal behaviors. Relevant to this observation, many recent studies have proposed to use different sensors to monitor drivers’ behaviors and apply learning algorithms to detect abnormal behaviors. Nevertheless, most existing systems are expensive and inconvenient to be deployed or significantly affected by the environment. In this article, we propose and develop a novel and effective solution, namely, SafeDriving, that collects signals from electromyography (EMG) sensors and then utilizes an effective deep-learning model to detect abnormal behaviors in real time. Specifically, we first utilize a wearable EMG sensor that can be attached to a driver’s forearm to collect a large amount of sensing data from human drivers, for which we define five typical abnormal driving behaviors (i.e., fetching forward, picking up, turning the steering wheel sharply, turning back, and touching sunroof) and label each sample accordingly. Next, using the labeled data, we design and train multiple state-of-the-art classifiers to improve the performance of SafeDriving, e.g., convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The extensive experiments demonstrate that GRU can lead to the best performance with an average accuracy of 93.94%. Based on this observation, we further investigate other important factors, such as the binding area of the sensor, the tightness of binding, the duration of the sample, etc. The proposed SafeDriving system provides an effective approach to reliably assess drivers’ driving behaviors with affordable commodity sensors and be further used in public safety.

In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices, and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficient task allocation algorithm. Using the task allocation, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.

Competitive influence maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the microlevel optimization by designing policies for one competitor to defeat its opponents. Furthermore, current studies ignore the fact that many influential nodes have their own starting prices, which may lead to inefficient budget allocation. In this article, we propose a novel competitive bidding influence maximization (CBIM) problem, where the competitors allocate budgets to bid for the seeds attributed to the platform during multiple bidding rounds. To solve the CBIM problem, we propose a fairness-aware multiagent CBIM (FMCBIM) framework. In this framework, we present a multiagent bidding particle environment (MBE) to model the competitors’ interactions and design a starting price adjustment mechanism to model the dynamic bidding environment. Moreover, we put forward a novel multiagent CBIM (MCBIM) algorithm to optimize competitors’ bidding policies. Extensive experiments on five datasets show that our work has good efficiency and effectiveness.

Although Computing Power Network (CPN) as the new network computing paradigm which can fully improve the utilization rate of decentralized computing power resources, the dynamic and heterogeneous characteristics of multivariate workloads present significant challenges to maintaining the Quality of Service (QoS) under dynamic resource scheduling. Therefore, workload prediction should be considered to ensure elastic demand services. However, the existing workload prediction methods mainly focus on (1) a single-granularity perspective, and (2) struggle to adapt to dynamic and heterogeneous multivariate workload environments, such as traditional LSTM-based or CNN-based methods that fail to capture cross-granularity dependencies under varying workload patterns. To consider above problems, we propose Multi-Granularity Workload Ensemble and Feature Inference for Multivariate Computing Power Prediction (MG-WEP), which address the problem from a multi-granularity perspective. First, we develop a mutual information feature selection method using a variational inference network to identify key features, facilitating a comprehensive exploration of the relationships among workload variables from an attribute perspective. Then, the clustering method is used to cluster similar workloads, effectively capturing the relationships among them. Furthermore, a combined ensemble prediction method is applied on all clustered workloads to improve prediction accuracy by leveraging the distinctive characteristics of each cluster from object perspective. Finally, we have fully compared the proposed algorithm with eleven comparison methods and four evaluation metrics on three real-world workload trace datasets. The results show that the proposed method has superior prediction performance.

The continuous demand for content-rich multimedia is pushing for high-speed and secure transmission approaches. In recent years, linear network coding (LNC) has been shown to be a promising technology to improve network throughput, transmission reliability, and information security. In this paper, we study the optimal transmission topology construction and LNC design for a secure multiple-source multicast to deliver the same content with integral link rates, which can be equivalent to the secure multicast problem with a virtual source, i.e., the integer secure virtual-source multicast (ISVM) problem. The objectives of the ISVM problem include the following: 1) satisfy the weakly secure requirements, 2) maximize the secure multicast rate (SMR), and 3) minimize the transmission cost when the SMR is maximized. First, we analyze the necessary and sufficient condition that there exist a transmission topology with integral link rates and a secure LNC that can achieve a given SMR $R$. Then, we model the ISVM problem as an integer linear programming based on the theoretical analysis and design an efficient transmission topology construction algorithm to solve the ISVM problem by utilizing the Lagrangian relaxation and subgradient method. We also analyze the size of finite field required to construct the deterministic LNC for a secure virtual-source multicast and the probability that the virtual-source multicast is weakly secure when using random LNC in the ISVM problem. Finally, we design upper and lower bounds for the ISVM problem and conduct extensive simulations to compare the performance of the proposed algorithms with these two bounds.