With the continued evolution of autonomous driving, the communication demand for in-vehicle networks (IVNs) has dramatically increased, and the traditional IVN solutions have become ineligible for the needs of new in-vehicle applications. Time-sensitive network (TSN) with the characteristics of determinism, low latency, high reliability, large bandwidth, and open industry standards, has been considered as the most promising technology for the next-generation IVNs. This article summarizes the current state and methodologies of in-vehicle TSN research through the following five aspects: 1) Quality of Service (QoS) strategy; 2) reliability; 3) clock synchronization; 4) network planning; and 5) network management. The corresponding research priorities and technical challenges are also addressed, respectively.

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.

Vehicular social networks (VSNs) are considered as an emerging class of vehicular ad hoc networks (VANETs) with social attributes. In contrast to the traditional randomly distributed human social networks, vehicles normally only move along xed paths. As such, information sharing and forwarding become challenging because of the high speed of vehicles, complicated propagation environment, and quickly changing network topologies. The social characteristics and human behavior features are used to improve the efciency of information delivery among vehicles in VANETs. In this paper, we propose a dynamic periodic location encounter network (DPLEN) model that considering both time-varying and location-varying characteristics of vehicles. We calculate the line similarity for each encountered pair of vehicles based on the periodic time and location information of the encounter event, and analyze social attributes based on the network model, such as the Community structure, Betweenness Centrality and K-shell. We also use the similarity to design two routing protocols for unicast and multicast patterns. After testing the proposed model on empirically collected datasets of mobility traces, the results show that these two routing protocols are able to greatly improve the efficiency of information forwarding in the VSNs.

High quality image transmission through smart devices requires high transmission rate, throughput and low Bit Error Rate (BER). At the same time, energy efficiency is always the top issue for the battery-based smart devices such as smart phone, tablets, etc. In this paper, an adaptive Forward Error Correction (FEC) coding and cooperative relayed image transmission system is proposed, through which both transmission quality and energy efficiency could be promised under complex mobile communication channel environment. There are four steps in the proposed scheme: (1) Discrete Wavelet Transform (DWT) and wavelet based Decomposition, (2) Pixel-Position (PP) information and Pixel-Value (PV) information split based unequal image resource allocation, (3) transmission through channel fading and AWGN communication environment, (4) multiple-relays and adaptive channel coding. Comparing to traditional methods, our proposed method is more practical to transmit high quality images through battery-limited smart phone platforms