With the rapid development of communication technology, connected vehicles (CV) have the potential, through the sharing of data, to enhance vehicle safety and reduce vehicle energy consumption and emissions. Numerous research efforts are quantifying the impacts of CV applications, assuming instant and accurate communication among vehicles, devices, pedestrians, infrastructure, the network, the cloud, and the grid, collectively known as V2X (vehicle-to-everything). The use of cellular vehicle-to-everything (C-V2X), to share data is emerging as an efficient means to achieve this objective. C-V2X releases 14 and 15 utilize the 4G LTE technology and release 16 utilizes the new 5G new radio (NR) technology. C-V2X can function without network infrastructure coverage and has a better communication range, improved latency, and greater data rates compared to older technologies. Such highly efficient interchange of information among all participating parts in a CV environment will not only provide timely data to enhance the capacity of the transportation system but can also be used to develop applications that enhance vehicle safety and minimize negative environmental impacts. However, before the full benefits of CV can be achieved, there is a need to thoroughly investigate the effectiveness, strengths, and weaknesses of different CV applications, the communication protocols, the varied results with different CV market penetration rates (MPRs), the interaction of CVs and human driven vehicles, the integration of multiple applications, and the errors and latencies associated with data communication. This paper reviews existing literature on the environmental, mobility and safety impacts of CV applications, identifies the gaps in our current research of CVs and recommends future research directions.
Radio Resource Management and Path Planning in Intelligent Transportation Systems via Reinforcement Learning for Environmental Sustainability
Tarbiat Modares University（塔比亚特·莫达雷斯大学）；Doha University for Science and Technology（多哈科技大学）；TU Braunschweig（布伦瑞克工业大学）；Institute of Communications Technology（通信技术研究所）
EICO: Energy-Harvesting Long-Range Environmental Sensor Nodes with Energy-Information Dynamic Co-Optimization
在实现电池寿命、信息准确性、传输距离和成本等严格设计目标方面的挑战推动了对传统电池供电设备的能量采集传感器节点的深入研究。当传感器网络需要跨越诸如农田和偏远地形的广阔区域时，远程通信的固有功率密集性质进一步放大了这一挑战。太阳能是无线传感器节点的常见能源，然而，由于季节变化和天气条件引起的功率波动，太阳能并不可靠。本文解决了这些问题，提出了一个永久供电，能量收集传感器节点，利用最小尺寸的太阳能电池，并能够通过动态协同优化能源消耗和信息传输，称为能量信息动态协同优化（EICO）的远程通信。这种能量信息智能是通过基于从收集器和电荷存储元件可用的能量总量的信息传输的自适应占空比来实现的，以优化传感器节点的能量消耗，同时采用传感器内分析（ISA）来最小化信息的丢失。这是第一个被报道的尺寸<35 cm 2的传感器节点，它能够以高达1包/秒的连续信息传送率在> 1 Km上进行远程通信，这是由EICO和ISA实现的。
A Survey on STAR-RIS Enabled Joint Communications and Sensing: Fundamentals, Recent Advances and Research Challenges
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠与信任跨学科中心）；University of Essex（埃塞克斯大学）；Institute for AI Industrial Technology Research, Hubei Engineering University（湖北工程学院人工智能工业技术研究 institute）
Holographic Surface Enabled Integrated Sensing and Communications
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；University of Cambridge（剑桥大学）；Princeton University（普林斯顿大学）；Peking University（北京大学）；Southeast University（东南大学）；Datang Mobile Communications Equipment Co., Ltd（大唐移动通信设备有限公司）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）；Weizmann Institute of Science（魏茨曼科学研究所）
