In recent years, device-to-device (D2D) communication has attained significant attention in the research community. The advantages of D2D communication can be fully realized in multi-hop communication scenario. The integration of cellular and multi-hop networks not only provides guaranteed Quality of Service (QoS) and reliability as a traditional cellular network, but also has the flexibility and adaptability as a multi-hop network. Routing in such multi-hop cellular D2D networks is a critical issue, since the multi-hop network can perform worse than a traditional cellular network if wrong routing decisions are made. This is because routing in these multi-hop networks needs to take care of the node mobility, dynamic network topology, and network fragmentation, which did not exist in traditional cellular networking. This paper provides a comprehensive survey of routing in multi-hop D2D networks. Some future research directions for the routing in D2D networks are also discussed at the end of this paper.

Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.

This paper presents a new approach to design a realtime Industrial-Ethernet network with the feature to add asynchronous sending devices like laptops or personal computers to the network without affecting the realtime performance. The idea is based on the modification of switching engines with usage of standard cabling and network interface cards. Although the implementation is similar to Siemens ProfiNet IRT, we will point out advantages of our more general approach. As a research result in formal modelling, we present a way to generate bipartite conflict graphs out of a given network infrastructure and communication requests of the devices. This graphs can be colored in polynomial time, which leads to schedules for each switch. Additionally, we discuss possible hardware designs of a modified Ethernet switch.