Transmission Power Consumption Research Articles

Hardware-software implementation of a local Wi-Fi network for the transmission of biomedical signals

The object of this study is a wireless local Wi-Fi network for broadcasting biomedical signals, its structure, and principles of construction. The task of minimizing the power consumption of a Wi-Fi transmitter has been addressed, which provides the possibility of building a wireless system for long-term monitoring of biomedical signals. As a result, a functional diagram of a wireless Holter monitoring system based on an ESP32 microcontroller was constructed, which includes a subsystem for setting up and diagnosing system units using MATLAB software packages, an ECG signal generator, and a multifunctional PCIe board from National Instruments. Evaluation criteria and methods for minimizing power consumption by an autonomous Wi-Fi transmitter have been proposed. Methods for synchronizing the working cycles of the transmitter and receiver of the Holter monitoring system were determined. A procedure for determining the optimal biosignal measurement frequency is presented, at which the distortion of ECG signals would be minimal, which means that the signal could be transmitted without losses. The concept of constructing an algorithm for implementing a program for a Wi-Fi transmitter has been developed, ensuring parallel execution of ECG signal measurement operations and their transmission over a local network. The data from semi-naturalistic tests with an experimental Holter monitoring system with a pre-setup subsystem and using external measuring devices, a computer, and the MATLAB software environment are presented. A comparative analysis of the experimental data with primary ECG signals and ECG signals at the receiver output showed a fairly stable correspondence between the input and output ECG signals. The proposed algorithms make it possible to reduce the average current consumption of the ESP32 microcontroller to 50.5 mA. The results of the study demonstrate the possibility of constructing an energy-efficient wireless system for long-term monitoring of biomedical signals based on the Wi-Fi interface

Beamforming design for Star-RIS assisted secure wireless communication system under hardware impairments

Simultaneous transmission and reflection reconfigurable intelligent surface (Star-RIS) has been recently introduced as one of the hopeful technologies in future communications. In this work, we investigate a beamforming design for Star-RIS-assisted secure wireless communications with two practical protocols, namely energy splitting (ES) and mode switching (MS). We assume that our transceivers are not ideal and have residual hardware impairments that lead to distortion noise. We aim to maximize the total secrecy rate by optimizing the beamforming in BS and Star-RIS under the limitation of transmission power consumption. The proposed non-convex optimization problem is solved via successive convex approximation and alternating optimization.

NxtSPR: A deadlock-free shortest path routing dedicated to relaying for Triplet-Based many-core Architecture

Deadlock-free routing is a significant challenge in Network-on-Chip (NoC) design as it affects the network’s latency, power consumption, and load balance, impacting the performance of multi-processor systems-on-chip. However, achieving deadlock-free routing will routinely result in expensive overhead as previous solutions either sacrifice performance or power efficiency to proactively avoid deadlocks or impose high hardware complexity to resolve deadlocks when they occur reactively. Utilizing the various characteristics of NoC to implement deadlock-free routing can be significantly more cost-effective with less impact on performance. This paper proposes a relay routing algorithm (NxtSPR) with a shortest path property and a deadlock prevention mechanism based on a synchronized Hamiltonian ring. The proposal is based on an in-depth study of the characteristics of a Triplet-Based many-core Architecture (TriBA) NoC. We establish various important topology-related theories and perform a formal verification (proof-based) for them. By utilizing the critical subgraph and apex of TriBA, NxtSPR can pre-calculate downstream nodes forwarding ports for packets by using a concise judgment strategy. This significantly reduces the computational overhead required for data transmission while optimizing the pipeline design of routers to decrease packet transmission latency and power consumption compared to other TriBA routing algorithms. We group the data transmissions according to the levels of maximum Hamiltonian edges a packet will traverse during its transmission life cycle. Independent data transmissions between groups can avoid mutual interference and resource competition, eliminating potential deadlocks. Gem5 simulation results show that, under the synthetic traffic patterns, compared to the representative (Table) and up-to-date (SPR4T) routing algorithms, NxtSPR achieves a 20.19%, 14.76%, and 5.54%, 4.66% reduction in average packet latency and per-packet power consumption, respectively. Moreover, it has an average of 18.50% and 4.34% improvement in throughput, as compared to them. PARSEC benchmark results show that NxtSPR reduces application runtime by up to a maximum of 22.30% and 12.82% compared to Table and SPR4T, and running the same applications with TriBA results in a maximum runtime reduction of 10.77% compared to 2D-Mesh.

A comprehensive and secure scheme for privacy-preserving smart meter data aggregation in the smart grid

Smart meters play a crucial role in the functioning of the smart grid by rapidly collecting and transmitting power consumption data to electricity companies. However, the real-time nature of smart meter data poses privacy risks for customers. To address this concern, encrypted aggregation of smart meter power consumption has been widely employed to protect customer privacy. In this paper, we propose an innovative scheme designed specifically for smart grids to fulfill these requirements. Our scheme demonstrates superior performance compared to existing solutions in terms of communication cost, computation, and functionality features. The proposed authentication protocol not only enables the secure sharing of power consumption data but also satisfies various security requirements, including mutual authentication, anonymity, prevention of man-in-the-middle attacks, and more. Furthermore, our framework exhibits significantly lower computing, communication, and storage overhead compared to similar schemes in the context of smart grids. This highlights the comprehensive and secure nature of our suggested framework, surpassing other existing smart grid schemes in terms of overall effectiveness and reliability.

New Methods for Optimal Power Allocation and Joint Resource Scheduling in 5G Network which Use Mobile Edge Computing

Mobile Edge Computing (MEC) is considered one of the enabling and promising technologies in 5G networks, especially with the massive data movement of various devices and the increased demand for computing. Here, computational offloading of tasks to edge clouds provides an effective, flexible, low-latency solution for mobile users in the network. However, the limited computing resources in edge clouds and the dynamic demands of mobile users make it difficult to schedule computing requests to appropriate edge clouds, and make the offloading process energetically expensive for devices. Therefore, it is very important to design an energy-efficient offloading strategy. To this end, we first formulate the transmission power allocation (PA) problem for mobile phone users to minimize power consumption. Using a quasi-convex technique, we address the (PA) problem using a (Hybrid GAPSO) algorithm resulting from combining the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) algorithm. Next, we model the joint request offloading and resource scheduling (JRORS) problem as a mixed- nonlinear program to minimize the response delay of requests. The (JRORS) problem can be divided into two problems, namely the request offloading (RO) problem and the computing resource scheduling (RS) problem. Therefore, we analyze the JRORS problem as a dual decision problem and propose a multi-objective particle swarm optimization algorithm referred as (MO-PSO). The simulation results show that (HGAPSO) can save transmission power consumption and has good convergence property, and (MO-PSO) outperforms existing methods in terms of response rate and can maintain good performance in a dynamic network.

Web-Based Digital Cell Library Characterization System

A modern approach to the design of large-scale electronic computing equipment is inconceivable without using previous similar developments in the current project. It is quite natural in this case to use previously developed digital cell libraries as a base. When a separate library cell is included in a large project, there is no need to calculate signal transmission delays, power consumption and layout of the included cell — all this is obtained in advance at the stage of characterization of library elements. This approach allows to significantly accelerate the development of the product as a whole. The stage of characterization of digital libraries requires significant time and computing resources, which is why it receives increased attention from developers of computer-aided design systems for microelectronic equipment. This article discusses some aspects of building a digital cell library characterization system. The description of the web-based library characterization system developed by the author is given. The main features that distinguish the system from the rest are an interactive approach to building the system, its web—oriented nature (i.e. the system is located on a web server and is available for communication via any web browser on the Internet), a large number of auxiliary procedures, a unique graphical representation of the results obtained. In addition to the above, it may be noted the simplicity of preparing the source data, the ease of switching between the "initial circuit"/ "circuit with extracted parasitic elements" modeling modes, as well as between the so-called "modeling edges": the normal, worst and best combination of temperature and supply voltage. To speed up calculations in the system, two modes of parallelization of modeling processes are provided at the user's choice.

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Abstract: Smart cities are comprised of intelligent objects that can collectively and automatically improve living standards, preserve lives, and function as a sustainable environment. Drones or Unmanned aerial vehicles (UAV), robotics, cognitive computing, and the Internet of Things (IoT) are mandatory to enhance the intellectual ability of smart cities by enhancing connectivity, energy efficiency, and Quality of Signal (QoS). Consequently, the integration of drones with IoT plays a crucial part in enabling a vast array of smart-city applications. Drones are undeniably the technology of the future. They glide in the air, keeping an eye on things in their metropolis. They do not need human control or operation. They capture data based on visuals and sounds by employing a variety of sensors, webcams, and mics, and then transfer it to a gateway for processing and retrieving information. Drones will let us gather vast volumes of data for processing, while also enhancing the intelligence of smart cities. The drone's signal is vulnerable to absorption, refraction, diffraction, and attenuation. Therefore, it is essential to predict the signal from the drone. This study presents an intelligent method using Deep Learning to predict the signal strength for enhancing network connectivity and delivering the desired QoS of IoTs and drone integration. This enables effective data transmission, boosts QoS for end-users/devices, and reduces data transmission power consumption.

A Near-Sensor Processing Accelerator for Approximate Local Binary Pattern Networks

In this work, a high-speed and energy-efficient comparator-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> ear- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ensor <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> ocal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> inary <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> attern accelerator architecture (NS-LBP) is proposed to execute a novel local binary pattern deep neural network. First, inspired by recent LBP networks, we design an approximate, hardware-oriented, and multiply-accumulate (MAC)-free network named Ap-LBP for efficient feature extraction, further reducing the computation complexity. Then, we develop NS-LBP as a processing-in-SRAM unit and a parallel in-memory LBP algorithm to process images near the sensor in a cache, remarkably reducing the power consumption of data transmission to an off-chip processor. Our circuit-to-application co-simulation results on MNIST and SVHN datasets demonstrate minor accuracy degradation compared to baseline CNN and LBP-network models, while NS-LBP achieves 1.25 GHz and an energy-efficiency of 37.4 TOPS/W. NS-LBP reduces energy consumption by 2.2× and execution time by a factor of 4× compared to the best recent LBP-based networks.

Resource-saving scheduling scheme for centralized target tracking in multiple radar system under automatic blanket jamming

This paper investigates the problem of Joint Radar Node Selection and Power Allocation (JRNSPA) in the Multiple Radar System (MRS) in the blanket jamming environment. Each radar node independently tracks moving target and subsequently transmits the raw observation data to the fusion center, which formulates a centralized tracking network structure. In order to establish a practical blanket jamming environment, we suppose that each target carries the self-defense jammer which automatically implements blanket jamming to the radar nodes that exceed the preset interception probability. Subsequently, the Predicted Conditional Cramer-Rao Lower Bound (PC-CRLB) is derived and utilized as the tracking accuracy criterion. Aimed at ensuring both the tracking performance and the Low Probability of Intercept (LPI) performance, the resource-saving scheduling model is formulated to minimize the transmit power consumption while meeting the requirements of tracking accuracy. Finally, the Modified Zoutendijk Method Of Feasible Directions (MZMFD)-based two-stage solution technique is adopted to solve the formulated non-convex optimization model. Simulation results show the effectiveness of the proposed JRNSPA scheme.

Harnessing 2D Ruddlesden-Popper Perovskite with Polar Organic Cation for Ultrasensitive Multibit Nonvolatile Transistor-Type Photomemristors.

Photomemristors have been regarded as one of the most promising candidates for next-generation hardware-based neuromorphic computing due to their potentials of fast data transmission and low power consumption. However, intriguingly, so far, photomemristors seldom display truly nonvolatile memory characteristics with high light sensitivity. Herein, we demonstrate ultrasensitive photomemristors utilizing two-dimensional (2D) Ruddlesden-Popper (RP) perovskites with a highly polar donor-acceptor-type push-pull organic cation, 4-(5-(2-aminoethyl)thiophen-2-yl)benzonitrile+ (EATPCN+), as charge-trapping layers. High linearity and almost zero-decay retention are observed in (EATPCN)2PbI4 devices, which are very distinct from that of the traditional 2D RP perovskite devices consisting of nonpolar organic cations, such as phenethylamine+ (PEA+) and octylamine+ (OA+), and traditional 3D perovskite devices consisting of methylamine+ (MA+). The 2-fold advantages, including desirable spatial crystal arrangement and engineered energetic band alignment, clarify the mechanism of superior performance in (EATPCN)2PbI4 devices. The optimized (EATPCN)2PbI4 photomemristor also shows a memory window of 87.9 V and an on/off ratio of 106 with a retention time of at least 2.4 × 105 s and remains unchanged after >105 writing-reading-erasing-reading endurance cycles. Very low energy consumptions of 1.12 and 6 fJ for both light stimulation and the reading process of each status update are also demonstrated. The extremely low power consumption and high photoresponsivity were simultaneously achieved. The high photosensitivity surpasses that of a state-of-the-art commercial pulse energy meter by several orders of magnitude. With their outstanding linearity and retention, rabbit images have been rebuilt by (EATPCN)2PbI4 photomemristors, which truthfully render the image without fading over time. Finally, by utilizing the powerful ∼8 bits of nonvolatile potentiation and depression levels of (EATPCN)2PbI4 photomemristors, the accuracies of the recognition tasks of CIFAR-10 image classification and MNIST handwritten digit classification have reached 89% and 94.8%, respectively. This study represents the first report of utilizing a functional donor-acceptor type of organic cation in 2D RP perovskites for high-performance photomemristors with characteristics that are not found in current halide perovskites.

Energy-Efficient and Low-Complexity Transmission Control With SWIPT-NOMA for Green Cellular Networks

In this study, we consider an energy-efficient and low-complexity transmission control in a SWIPT-NOMA-based green cellular network (GCN) that consists of a green base station (GBS) and green users (GUEs). First, we formulate a non-convex problem that minimizes transmit power consumption while supporting minimum downlink user service rate, downlink data queue stability, and user battery availability. Then, we transform the problem into a Lyapunov-drift-penalty minimization problem, which can determine a new resource allocation scheme that balances transmit power consumption and queue stability. Second, the Lyapunov-drift-penalty problem is decomposed into subchannel assignment, power allocation, and power splitting (PS) ratio control problems. The subchannel assignment problem is solved using a matching theory-based low-complexity algorithm. The power allocation and PS ratio control problems are solved using the alternating optimization (AO) approach and bisection method. This decomposed subproblem-based control also enables distributed control between the GBS and GUEs. Third, we prove the convergence, optimality, and polynomial computation complexity of the proposed algorithm. Lastly, we demonstrate that the proposed control outperforms the benchmark controls regarding transmit power consumption and the achievable rate. Owing to the optimality and low complexity, the proposed control can be efficiently applied to large-scale and distributed GCNs in sixth-generation environments.

Performances of Polymer-Dispersed Liquid Crystal Films for Smart Glass Applications.

Polymer-dispersed liquid crystal (PDLC) film is an active smart film penetrating the market due to its unique functionalities. These functional characteristics include switchable tint capabilities, which shield building residents from the sun's harmful ultraviolet (UV) rays, improve energy-saving features, and produce higher cost-efficiency. Although PDLC films are promising in several applications, there is still ambiguity on the performance of PDLC films. Particularly, the sizing effects' (such as film thickness and area) correlation with visible light transmission (VLT), ultraviolet rejection (UVR), infrared rejection (IRR), light intensity, current consumption, and apparent power consumption is not well understood. Therefore, this study investigated the sizing effects of PDLC films, including the thickness effect on VLT, UVR, IRR, light intensity, and area influence on current and apparent power consumptions. The varying applied voltage effect on the light transmittance of the PDLC film was also effectively demonstrated. A 0.1 mm PDLC film was successfully presented as a cost-efficient film with optimal parameters. Consequently, this study paves the way for a clearer understanding of PDLC films (behavior and sizing effects) in implementing economic PDLC films for large-scale adoption in commercial and residential premises.

An Overview on On-chip Network Routing Optimisation

As the number of cores in multi-core systems increases, bus-based systems face significant challenges in terms of scalability, average transmission latency and power consumption. In this context, on-chip networks emerged as a suitable communication architecture for System On Chip (SoC) based entirely on the communication ideas in computer networks and taking into account the characteristics of the system on chip in SoCs. With the introduction of on-chip networks, related research has been developed, such as on-chip network topology, communication quality of service, on-chip network routing algorithms, and on-chip network fault tolerance. He then introduces on-chip network routing algorithms and fault-tolerant routing algorithms from different perspectives, and finally points out the directions of fault-tolerant routing algorithms worthy of research.

Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System

Due to the benefits of the spectrum and energy efficiency, intelligent reflecting surfaces (IRSs) are regarded as a promising technology for future networks. In this work, we consider a single cellular network where multiple IRSs are deployed to assist the downlink transmissions from the base station (BS) to multiple user equipment (UE). Hence, we aim to jointly optimize the configuration of the BS active beamforming and reflection beamforming of the IRSs that meet the UE’s QoS while allowing the lowest transmit power consumption at the BS. Although the conventional alternating approach is widely used to find converged solutions, its applicability is restricted by high complexity, which is more severe in a dynamic environment. Consequently, an alternative approach, i.e., machine learning (ML), is adopted to find the optimal solution with lower complexity. For the static UE scenario, we propose a low-complexity optimization algorithm based on the new generalized neural network (GRNN). Meanwhile, for the dynamic UE scenario, we propose a deep reinforcement learning (DRL)-based optimization algorithm. Specifically, a deep deterministic policy gradient (DDPG)-based algorithm is designed to address the GRNN algorithm’s restrictions and efficiently handle the dynamic UE scenario. Simulation results confirm that the proposed algorithms can achieve better power-saving performance and convergence with a noteworthy reduction in the computation time compared to the alternating optimization-based approaches. In addition, our results show that the total transmit power at the BS decreases with the increasing number of reflecting units at the IRSs.

Robust Beamforming Design for IRS-Assisted Downlink Multi-User MISO-URLLC in an IIoT Scenario

Intelligent reflecting surfaces (IRS) have recently been regarded as having potential for supporting multi-user (MU) multiple-input single-output (MISO) ultra-reliable and low-latency communication (URLLC) by creating favorable wireless communication links in the industrial IoT (IIoT) scenario. Hence, we studied the joint robust design of a beamformer at a central controller (CC) and phase shifters at the IRS for minimizing the transmit power consumption based on perfect channel state information (CSI) and imperfect CSI, respectively. The design was formulated as a non-convex optimization problem, also taking into account the quality-of-service (QoS) demands of the actuator, unit-modulus constraints of the IRS, and the robustness against the impact of CSI imperfection. Subsequently, we proposed a computationally efficient iterative algorithm to obtain a suboptimal solution by exploiting the penalty method and the successive convex approximation (SCA) for perfect CSI, while the penalty method, S-procedure, and SCA were adopted for imperfect CSI. Finally, our simulation results revealed that (1) the required transmit power consumption for URLLC was significantly reduced by employing the proposed IRS instead of conventional wireless communication without IRS; and (2) the proposed algorithm was robust and effective for the beamforming design.

QoS Guaranteed Power Minimization and Beamforming for IRS-Assisted NOMA Systems

In this letter, to solve the problem of large interference among users and reduce the transmit power consumption of base station while guaranteeing target quality of service (QoS) in downlink intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems, we present a joint optimization algorithm to design active beamforming for base station and passive beamforming (phase shift matrix) for IRS based on semidefinite relaxation (SDR). The non-convex constraint of signal-to-interference-plus-noise ratio (SINR) is transformed into affine constraint by increasing the dimension of optimization variables. The two subproblems of transmit power minimization and phase shift feasibility are solved iteratively using alternating optimization. To solve the computing performance degradation of SDR in largescale problems, we further propose a low-complexity algorithm based on successive convex approximation (SCA) and the original problem is then determined iteratively by relaxing the constant modulus constraint of IRS. Simulation results show that both algorithms have lower power consumption than the existing algorithms with different number of IRS reflecting elements or transmit antennas, and the SCA algorithm can approach the performance of SDR with lower complexity.

16-channel photonic–electric co-designed silicon transmitter with ultra-low power consumption

A hybrid integrated 16-channel silicon transmitter based on co-designed photonic integrated circuits (PICs) and electrical chiplets is demonstrated. The driver in the 65 nm CMOS process employs the combination of a distributed architecture, two-tap feedforward equalization (FFE), and a push–pull output stage, exhibiting an estimated differential output swing of 4.0Vpp. The rms jitter of 2.0 ps is achieved at 50 Gb/s under nonreturn-to-zero on–off keying (NRZ-OOK) modulation. The PICs are fabricated on a standard silicon-on-insulator platform and consist of 16 parallel silicon dual-drive Mach–Zehnder modulators on a single chip. The chip-on-board co-packaged Si transmitter is constituted by the multichannel chiplets without any off-chip bias control, which significantly simplifies the system complexity. Experimentally, the open and clear optical eye diagrams of selected channels up to 50 Gb/s OOK with extinction ratios exceeding 3 dB are obtained without any digital signal processing. The power consumption of the Si transmitter with a high integration density featuring a throughput up to 800 Gb/s is only 5.35 pJ/bit, indicating a great potential for massively parallel terabit-scale optical interconnects for future hyperscale data centers and high-performance computing systems.

MR-PIPA: An Integrated Multilevel RRAM (HfO x )-Based Processing-In-Pixel Accelerator

This work paves the way to realize a processing-in-pixel (PIP) accelerator based on a multilevel HfOx resistive random access memory (RRAM) as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing at edge devices. The proposed design intrinsically implements and supports a coarse-grained convolution operation in low-bit-width neural networks (NNs) leveraging a novel compute-pixel with nonvolatile weight storage at the sensor side. Our evaluations show that such a design can remarkably reduce the power consumption of data conversion and transmission to an off-chip processor maintaining accuracy compared with the recent in-sensor computing designs. Our proposed design, namely an integrated multilevel RRAM (HfOx)-based processing-in-pixel accelerator (MR-PIPA), achieves a frame rate of 1000 and efficiency of ~1.89 TOp/s/W, while it substantially reduces data conversion and transmission energy by ~84% compared to a baseline at the cost of minor accuracy degradation.

A New Certificateless Signcryption Scheme for Securing Internet of Vehicles in the 5G Era

The application of digital signature technology to the Internet of vehicles (IoV) is affected by its network and communication environment. In the 5G era, the influx of a large number of intelligent devices into the mobile Internet requires a low transmission delay and power consumption as well as high-security requirements. To the best of our knowledge, a well-designed solution in which signcryption technology is used has not been proposed in the IoV research area. Motivated by the fact, a certificateless signcryption scheme based on the elliptic curve digital signature algorithm, in which pseudonym and timestamp mechanism are also considered, has been designed in this paper. We prove that the scheme proposed by us can be reduced to solving the difficulty of the computational Diffie–Hellman problem with a standard model, showing that the scheme meets requirements on both security and efficiency, which provides a comparative analysis with the state-of-the-art schemes in terms of security analysis, computational cost, and communication cost, demonstrating that the scheme proposed by us is suitable to be deployed in the IoV environment, which is of the characteristics of high-speed vehicle movement.