An overview of millimeter waves challenges in 5G vehicle-to-everything networks

International audienceThe Automotive Vehicle to Everything (V2X) technology is one of the most important innovations that the world will see in the years to come. This paradigm will support many advanced services such as object detection and recognition, risk identification and avoidance, car platooning. These services will require several keys among them, the high data transmission rates of the order of gigabits per driving hour, and high reliability, and high speed for transition of data, which may be available through the capabilities of the new architecture for the next generation of wireless communications 5G and the wide bandwidth of the millimeter wave (mm Wave) which is deemed to be a real solution for the V2X requirements. However, the challenges related to the reliability/latency and security of the V2X system and nature of mm wave communication require several solutions either for natural challenges such as High path loss propagation, penetrating disability or for the technical challenges. This paper provides an overview of the V2X communication technology investigates the V2X challenges including the mm wave and and finally presents some efficient solutions

Ultra reliable 5G mmWAve communications for V2X scénarios

The Automotive Vehicle to Everything (V2X)technology is one of the most important innovations that theworld will see in the years to come. This paradigm will supportmany advanced services such as object detection and recognition,risk identification and avoidance, car platooning. These serviceswill require several keys among them, the high data transmissionrates of the order of gigabits per driving hour, and highreliability, and high speed for transition of data, which may beavailable through the capabilities of the new architecture for thenext generation of wireless communications 5G and the widebandwidth of the millimeter wave (mm Wave) which is deemed tobe a real solution for the V2X requirements. However, thechallenges related to the reliability/latency and security of theV2X system and nature of mm wave communication requireseveral solutions either for natural challenges such as High pathloss propagation, penetrating disability or for the technicalchallenges. This paper provides an overview of the V2Xcommunication technology investigates the V2X challengesincluding the mm wave and and finally presents some efficientsolutions

Energy Performance of LDPC Scheme in Multi-Hop Wireless Sensor Network with Two base Stations Model

Conservation of the energy is one of the main design issues in wireless sensor networks. The limited battery power of each sensor node is a challenging task in deploying this type of network. The challenge is crucial in reliable wireless network when implementing efficient error correcting scheme with energy consuming routing protocol. In this work, we investigated the energy performance of LDPC code in multi-hop wireless sensor network. We proposed a model of two base stations to prolong the lifetime and build a reliable and energy-efficient network. Through performed MATLAB simulations, we examine the energy effectiveness of multiple base stations model on reliable wireless sensor network performance in different network dimensions

On the performance of adaptive coding schemes for energy efficient and reliable clustered wireless sensor networks

Clustering is the key for energy constrained wireless sensor networks (WSNs). Energy optimization and communication reliability are the most important consideration in designing efficient clustered WSN. In lossy environment, channel coding is mandatory to ensure reliable and efficient communication. This reliability is compromised by additional energy of coding and decoding in cluster heads. In this paper, we investigated the trade-offbetween reliability and energy efficiency and proposed adaptive FEC/FWD and FEC/ARQ coding frameworks for clustered WSNs. The proposed schemes consider channel condition and inter-node distance to decide the adequate channel coding usage. Simulation results show that both the proposed frameworks are energy efficient compared to ARQ schemes and FEC schemes, and suitable to prolong the clustered network lifespan as well as improve the reliability

Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

We consider monitoring applications in multihop wireless sensor networks (WSNs), where nodes rely on limited batteries so that energy efficiency and reliability are of paramount importance. Typically, lossy compression is aimed at saving transmission energy, yet affects the quality of transmitted data over lossy channels. Accordingly, using error correction coding (ECC) along with compression is required to guarantee both energy efficiency and high-fidelity reconstruction. In this paper, we analyze the energy efficiency of the joint use of lossy compression along with ECC, with the twofold objective of extending the network lifetime and assuring reliability. Specifically, we consider an adaptive joint lossy source-channel coding (JLSCC) system, where the energy efficiency and reliability performances depend on both the compression and the coding rates. Therein, we first carry out a performance analysis of JLSCC, considering realistic models of communication and computational energies, when the communication is performed over a Rayleigh fading channel. Then, we evaluate the performance of the JLSCC system compared to lossy compression and ECC systems in both end-to-end and multihop communications. Our results reveal that an adaptive JLSCC results in substantial energy saving while guaranteeing the required reliability performance, compared to both lossy compression and channel coding systems, that cannot be efficient for both energy and reliability. Instead, the JLSCC system is proved to be energy efficient for small distance end-to-end communication and large-scale multihop network, while leading to satisfactory reliability performance

UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI

A Reinforcement Learning Based Transmission Parameter Selection and Energy Management for Long Range Internet of Things

Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions