LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move

Haque, Khandaker Foysal; Abdelgawad, Ahmed; Yanambaka, Venkata P.; Yelamarthi, Kumar

doi:10.3390/s20236876

Cited by 39 publications

(28 citation statements)

References 69 publications

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“…Several access technologies have already emerged to support V2X communication, such as wireless local area networks (WLAN), including IEEE 802.11p/ WAVE [21][22][23] and its evolution IEEE 802.11bd [24], wireless wide area networks, cellular technologies, such as LTE/5G [21,25,26], as well as LoRa [27][28][29][30] and Bluetooth. These wireless access technologies offer diverse communication options among different smart things.…”

Section: Internet Of Vehicles (Iov)mentioning

confidence: 99%

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

et al. 2022

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Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

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Section: Internet Of Vehicles (Iov)mentioning

confidence: 99%

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

et al. 2022

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“…Effective use of sensors greatly depends on adopted wireless communication technology. The authors in [118] proposed a vehicle-to-everything (V2X) communication architecture with LoRaWAN wireless technology. The authors mentioned that LoRaWAN plays an important role in developing and solving fundamental challenges such as reliability, data handover, time criticality, modularity, and energy efficiency with vehicles on the move for V2X.…”

Section: Smart Transportation (Intelligent Transportation)mentioning

confidence: 99%

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

et al. 2021

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Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

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“…In addition, the use of cooperative V2X downloading was a typical solution of driving experience [14], in order to achieve better energy efficiency, low emissions and resource sharing. Moreover, the reliability and effectiveness of V2X communication greatly depends on communication architecture, and authors in [15] proposed a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The implicit cooperative positioning (ICP) algorithm that exploits the V2V connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging, was investigated in [16] to enhance the autonomous vehicle localization accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

Osman

Abdelsalam

2021

Applied Sciences

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Recent autonomous intelligent transportation systems commonly adopt vehicular communication. Efficient communication between autonomous vehicles-to-everything (AV2X) is mandatory to ensure road safety by decreasing traffic jamming, approaching emergency vehicle warning, and assisting in low visibility traffic. In this paper, a new adaptive AV2X model, based on a novel optimization method to enhance the connectivity of the vehicular networks, is proposed. The presented model optimizes the inter-vehicle position to communicate with the autonomous vehicle (AV) or to relay information to everything. Based on the system quality-of-service (QoS) being achieved, a decision will be taken whether the transmitting AV communicates directly to the destination or through cooperative communication. To achieve the given objectives, the best position of the relay-vehicle issue was mathematically formulated as a constrained optimization problem to enhance the communication between AV2X under different environmental conditions. To illustrate the effectiveness of the proposed model, the following factors are considered: distribution of vehicles, vehicle density, vehicle mobility and speed. Simulation results show how the proposed model outperforms other previous models and enhances system performance in terms of four benchmark aspects: throughput (S), packet loss rate (PLR), packet delivery ratio (PDR) and average delivery latency (DL).

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LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move

Cited by 39 publications

References 69 publications

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

A Novel Adaptive Approach for Autonomous Vehicle Based on Optimization Technique for Enhancing the Communication between Autonomous Vehicle-to-Everything through Cooperative Communication

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