With the fast development of industrial IoT, a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices energy and storage space are strictly limited. Moreover, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we design a flexible and economical framework for data processing, storage and retrieval in IIoT by integrating fog computing and cloud computing. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data are used and stored locally. The non-time-sensitive data are transmitted to the cloud server to support data retrieval and mining in the future. Simulation results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

The black hole attack is one of the well-known security threats in wireless mobile ad hoc networks. The intruders utilize the loophole to carry out their malicious behaviors because the route discovery process is necessary and inevitable. Many researchers have conducted different detection techniques to propose different types of detection schemes. In this paper, we survey the existing solutions and discuss the state-of-the-art routing methods. We not only classify these proposals into single black hole attack and collaborative black hole attack but also analyze the categories of these solutions and provide a comparison table. We expect to furnish more researchers with a detailed work in anticipation.

Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and IoT applications. Computationally intensive artificial intelligence (AI) tasks are well suited to be offloaded to the Cloudlet server, but there is a lack of energy-delay optimization models specifically designed for this edge AI scenario. {\color{red}In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task}, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights (WAWs) and computing capacities of virtual machines (VMs), at the same time guaranteeing the quality of the results (QoRs). We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks.

Federated learning (FL) has emerged as a promising paradigm for enabling collaborative training of machine learning models while preserving data privacy. However, the massive heterogeneity of data and devices, communication constraints, and security threats pose significant challenges to its practical implementation. This paper provides a system review of the state-of-the-art techniques and future research directions in FL, with a focus on addressing these challenges in resource-constrained environments by a cloud–edge–end collaboration FL architecture. We first introduce the foundations of cloud–edge–end collaboration and FL. We then discuss the key technical challenges. Next, we delve into the pillars of trustworthy AI in the federated context, covering robustness, fairness, and explainability. We propose a dimension reconstruction of trusted AI and analyze the foundations of each trustworthiness pillar. Furthermore, we present a lightweight FL framework for resource-constrained edge–end devices, analyzing the core contradictions and proposing optimization paradigms. Finally, we highlight advanced topics and future research directions to provide valuable insights into the field.

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving the efficiency of future transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selections in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, to capture the characteristics of mmWave signal propagation in urban grid roads, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which is more accurate than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., the random with forward progress (RFP), the most forward with fixed radius (MFR), and the nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs' instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by exploiting the location information of AVs within the transmission range of each hop and considering the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.

Key establishment is the most fundamental cryptographic primitive in all kinds of applications where security is a concern. However, the nature of limited resources on sensor nodes restricts the use of conventional key management techniques in wireless sensor networks. Many researchers have conducted different techniques to propose different types of key distribution schemes. In this paper, we survey the existing solutions and describe the state‐of‐the‐art techniques. We not only classify these proposals into location‐independent key distribution schemes and location‐dependent key distribution schemes but also provide a comparison table. We hope to attract more research attentions on this direction. Copyright © 2011 John Wiley & Sons, Ltd.