Roadside Units Deployment (RSUD) is of great importance to smart transportation with the Internet of Things (IoT). It is believed to be not feasible for RSUD to cover and perceive the whole area due to the high installation and maintenance costs. The candidate locations set of RSUD may be huge for a future urban area with vehicle-to-everything (V2X) networks. Most of the previous studies tried to maximize the Roadside Units (RSU) coverage only and made few reports on emergency scenarios, such as accidents happening. We tried to find better candidate locations of RSUD in some grid road networks with equal length streets, and then chose some of these locations for final installation with a given budget to minimize the average reporting time of emergency messages in V2X networks. Firstly, we analyzed candidate locations of RSUD for different cases of RSUs and vehicles. Then we proposed a message dissemination model for RSUD with the V2X network, and a center-rule-based neighborhood search algorithm (CNSA for short). In this algorithm, we generated initial solutions with the center rule and then obtained better neighbor solutions. Numerical simulation results from small-scale urban streets showed that the proposed algorithm performs well on execution time. Simulation results with Veins and Simulation of Urban Mobility) (SUMO) verified the proposed model and CNSA for evaluating the RSUD scheme by distance instead of accident reporting time in urban areas with large-scale traffic flow.

Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical.

In this paper, we present a terminal slidingmode control method for the projective synchronization of unmodeled hyperjerk systems subject to parameter perturbation and external disturbances. We leverage fuzzy recurrent neural networks to identify unknown hyperjerk systems. We propose a control law for projective synchronization via the adaptive estimation of the unknown bounds of parameter perturbation and external disturbances. We theoretically prove that the proposed control law is able to achieve chattering-free projective synchronization in finite time. Finally, we elaborate on simulation results to demonstrate the efficacy of the methods.

With the increasing requirements to protect intellectual property, more and more attention is paid to the research of patent strategy. Intellectual property-oriented patent search is different from normal literature retrieval as its with professional field characteristics and diversified information retrieval. Therefore, this paper first summarizes the main research status of traditional information-based patent retrieval methods, semantic-based patent retrieval methods and knowledge graph-based patent retrieval methods, and then introduces representative patent retrieval applications. Finally, it suggested some ideas for further research on intellectual properties search from patents by discussing the application of deep learning in patent retrieval.