Smart Vehicles Research Articles

SeAC: SDN-Enabled Adaptive Clustering Technique for Social-Aware Internet of Vehicles

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose : an SDN-enabled adaptive clustering technique for SIoV. uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, lowers communication and computation costs. We evaluate, and the simulation results show that for two different topologies, the adaptive approach using can produce better results in terms of a stable cluster formation.

Review of the techniques and mechanisms for enhancing trust in internet of things for smart agriculture

Smart agriculture is an emerging technology that has been developed from innovative information technologies such as AI, IoT, ML, smart vehicles in order to maximize outputs while optimizing farm inputs for better production and profit. However, these innovations have several vulnerabilities, especially, given that most of agriculture is practiced in open fields, exposed to harsh, unprotected environments. Studies have been conducted on its security issues and how to mitigate the threats from the vulnerabilities along with some studies in data privacy in smart agriculture. However, studies on trust issues of farmers on these technologies are absent. This paper looks at the security issues and how they impact on farmers’ trust on these technologies. It finally offers direction on hoe to enhance their trust on the smart farming technologies.

Joint Microservices Caching and Task Offloading Framework in VEC Based on Deep Reinforcement Learning

Vehicle capacities and intelligence are rapidly increasing, which will likely support a wide range of novel and interesting applications. However, these resources are not effectively utilized. To take advantage of these invaluable capabilities in smart vehicles, they can be used in the cloud environment and can be operated through distributed computing platforms in order to benefit from their combined processing power, storage capacity, and memory resources. Vehicular edge computing (VEC) is a promising field that allows computing tasks to be transferred from cloud servers to vehicular edge servers for processing, allowing data and apps to be placed closer to vehicles (users).
 This paper proposes a framework that combines two modules, the first one for managing microservice caching in vehicle-mounted edge networks, such that we use cluster-based caching technique to deal with the case where similar microservices are frequently requested in VEC. The second one integrates the computational capabilities of the edge servers with the capabilities of vehicles to perform task offloading in a collaborative manner.
 Our solution addresses the limitations of existing edge computing platforms during peak times by combining microservices caching with computational task offloading to improve overall system performance.

On That <em>Toy-Being</em> of Generative Art Toys

On That <em>Toy-Being</em> of Generative Art Toys

Activatable Graphene Quantum-Dot-Based Nanotransformers for Long-Period Tumor Imaging and Repeated Photodynamic Therapy.

Photodynamic therapy (PDT) is considered as an emerging therapeutic modality against cancer with high spatiotemporal selectivity because the utilized photosensitizers (PSs) are only active and toxic upon light irradiation. To maximize its effectiveness, PDT is usually applied repetitively for ablating various tumors. However, the total overdose of PSs from repeated administrations causes severe side effects. Herein, acidity-activated graphene quantum dots-based nanotransformers (GQD NT) are developed as PS vehicles for long-period tumor imaging and repeated PDT. Under the guidance of Arg-Gly-Asp peptide, GQD NT targets to tumor tissues actively, and then loosens and enlarges in tumor acidity, thus promising long tumor retention. Afterwards, GQD NT transforms into small pieces for better penetration in tumor. Upon laser irradiation, GQD NT generates mild hyperthermia that enhances cell membrane permeability and further promotes the PSs uptake. Most intriguingly, the as-prepared GQD NT not only "turns-on" fluorescence/magnetic resonance signals, but also achieves efficient repeated PDT. Notably, the total PSs dose is reduced to 3.5µmolkg-1 , which is 10-30times lower than that of other reported works. Overall, this study exploits a smart vehicle to enhance accumulation, retention, and release of PSs in tumors through programmed deformation, thus overcoming the overdose obstacle in repeated PDT.

FlexiChain 3.0: Distributed Ledger Technology-Based Intelligent Transportation for Vehicular Digital Asset Exchange in Smart Cities.

Due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. Internet of Vehicles (IoV) is the term that describes the interconnection for every single node, device, sensor, and actuator that are Internet enabled, whether attached or unattached to vehicles. A single smart vehicle will generate a huge amount of data. Concurrently, it needs an instant response to avoid accidents since vehicles are fast-moving objects. In this work, we explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS. Multiple distributed ledger networks are currently in operation. Some are used in finance or supply chains, and others are used for general decentralized applications. Despite the secure and decentralized nature of the blockchain, each of these networks has trade-offs and compromises. Based on the analysis of consensus algorithms, a conclusion has been made to design one that fits the requirements of ITS-IOV. FlexiChain 3.0 is proposed in this work to serve as a Layer0 network for different stakeholders in the IoV. A time analysis has been conducted and shows a capacity of 2.3 transactions per second, which is an acceptable speed to be used in IoV. Moreover, a security analysis was conducted as well and shows high security and high independence of the node number in terms of security level per the number of participants.

Magnetic Control of Protein Expression via Magneto-mechanical Actuation of ND-PEGylated Iron Oxide Nanocubes for Cell Therapy.

Engineered cells used as smart vehicles for delivery of secreted therapeutic proteins enable effective treatment of cancer and certain degenerative, autoimmune, and genetic disorders. However, current cell-based therapies use mostly invasive tools for tracking proteins and do not allow for controlled secretion of therapeutic proteins, which could result in unconstrained killing of surrounding healthy tissues or ineffective killing of host cancer cells. Regulating the expression of therapeutic proteins after success of therapy remains elusive. In this study, a noninvasive therapeutic approach mediated by magneto-mechanical actuation (MMA) was developed to remotely regulate the expression of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) protein, which is secreted by transduced cells. Stem cells, macrophages, and breast cancer cells were transduced with a lentiviral vector encoding the SGpL2TR protein. SGpL2TR comprises TRAIL and GpLuc domains optimized for cell-based applications. Our approach relies on the remote actuation of cubic-shape highly magnetic field responsive superparamagnetic iron oxide nanoparticles (SPIONs) coated with nitrodopamine PEG (ND-PEG), which are internalized within the cells. Cubic ND-PEG-SPIONs actuated by superlow frequency alternating current magnetic fields can translate magnetic forces into mechanical motion and in turn spur mechanosensitive cellular responses. Cubic ND-PEG-SPIONs were artificially designed to effectively operate at low magnetic field strengths (<100 mT) retaining approximately 60% of their saturation magnetization. Compared to other cells, stems cells were more sensitive to the interaction with actuated cubic ND-PEG-SPIONs, which clustered near the endoplasmic reticulum (ER). Luciferase, ELISA, and RT-qPCR analyses revealed a marked TRAIL downregulation (secretion levels were depleted down to 30%) when intracellular particles at 0.100 mg/mL Fe were actuated by magnetic fields (65 mT and 50 Hz for 30 min). Western blot studies indicated actuated, intracellular cubic ND-PEG-SPIONs can cause mild ER stress at short periods (up to 3 h) of postmagnetic field treatment thus leading to the unfolded protein response. We observed that the interaction of TRAIL polypeptides with ND-PEG can also contribute to this response. To prove the applicability of our approach, we used glioblastoma cells, which were exposed to TRAIL secreted from stem cells. We demonstrated that in the absence of MMA treatment, TRAIL essentially killed glioblastoma cells indiscriminately, but when treated with MMA, we were able to control the cell killing rate by adjusting the magnetic doses. This approach can expand the capabilities of stem cells to serve as smart vehicles for delivery of therapeutic proteins in a controlled manner without using interfering and expensive drugs, while retaining their potential to regenerate damaged tissue after treatment. This approach brings forth new alternatives to regulate protein expression noninvasively for cell therapy and other cancer therapies.

Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles

Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited payment budget, and profit competition among SVs. In this paper, we propose a novel dynamic FL-based economic framework for an IoV network to address these challenges. Specifically, the VSP first implements an SV selection method to determine a set of the best SVs for the FL process according to the significance of their current locations and information history at each learning round. Then, each selected SV can collect on-road information and propose a payment contract to the VSP based on its collected QoI. For that, we develop a multi-principal one-agent contract-based policy to maximize the profits of the VSP and learning SVs under the VSP's limited payment budget and asymmetric information between the VSP and SVs. Through experimental results using real-world on-road datasets, we show that our framework can converge 57% faster (even with only 10% of active SVs in the network) and obtain much higher social welfare of the network (up to 27.2 times) compared with those of other baseline FL methods.

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.

Electric Vehicle Smart Charging Coordination on a Residential Distribution Grid

Electric Vehicle Smart Charging Coordination on a Residential Distribution Grid

Developed Smart Vehicle Tracking System using GPS and GSM Modem

&nbsp;Fast technological development has streamlined daily living. On the other hand, as a result of a shortage of emergency services, the growth of technology has also increased the incidence of traffic accidents, which cause enormous loss of life and property. Systems for monitoring vehicles and detecting accidents are highly dependable and secure, and they are especially useful for remotely monitoring vehicle activity. In this paper, based on the Global Positioning System (GPS) and the Global System for Mobile (GSM) Communication has been implementing, an effective system for monitoring vehicle movement and identifying any accidents that occur has been established GSM. The suggested method successfully made use of widely available hardware elements that integrate a smartphone and a vehicle unit that is mounted within the car. The proposed system would use General Packet Radio Service (GPRS) to automatically communicate and store vehicle data to the Internet of Things (IoT) every 10 seconds. In addition, the system will create an SMS Short Message Service (SMS) and send it to the user's smartphone in response to an SMS request received with the vehicle's current position information. At the user side, it has been an android smartphone for software application connected to the Thinger.io platform that has been used to view up-to-date car information. Only the authorized user who has access to the Thinger.io platform and the login and password may access this application due to its security. The suggested system's accuracy has been evaluated. The findings show that the suggested method can follow the vehicle high accurately.

Driver Behavior Recognition Based on Neural Networks Theory

The article deals with the problem of driver behavior while driving the vehicle. Driver distraction can lead to serious accidents that threaten human life and public property around the world. Solving the problem of preventing dangerous driving behavior will reduce the risk of getting into an accident in the future. Thus, there is a need for a smart vehicle that will support driver behavior recognition functionality. A possible solution to the problem using an artificial neural network for automatic recognition of driver behavior on a real set of driver behavior data is considered. The high accuracy and efficiency of the developed model recognition is obtained.

Secure and Fast Emergency Road Healthcare Service Based on Blockchain Technology for Smart Cities

Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of recent technologies such as the Internet of Things (IoT) allows us to find solutions to ensure rapid movement by road in emergencies. Integrating the healthcare sector and smart vehicles, IoT ensures this objective. This integration gives rise to two paradigms: the Internet of Vehicles (IoV) and the Internet of Medical Things (IoMT), where smart devices collect medical data from patients and transmit them to medical staff in real time. These data are extremely sensitive and must be managed securely. This paper proposes a system design that brings together the three concepts of Blockchain technology (BC), IoMT and IoV to address the problem mentioned above. The designed system is composed of three main parts: a list of hospitals, patient electronic medical record (EMR) and a network of connected ambulances. It allows the road user in the case of an accident to report their position to the nearby health services and ambulances.

PEPA: Paillier cryptosystem-based efficient privacy-preserving authentication scheme for VANETs

The use of the Internet of Things is increasing dramatically, creating a network of objects from sensors to smart appliances, such as smart vehicles. Vehicular Ad-hoc Networks (VANETs), as a special network, enables connected vehicles to communicate with other entities and provide a variety of stable communication services by infrastructures on both sides of the road for vehicles. However, the open-medium nature of communications and the high-speed mobility of vehicles may bring several potential security and privacy issues. To solve the issues, this paper proposes a Paillier cryptosystem-based efficient privacy-preserving authentication scheme (PEPA) for VANETs. The proposed PEPA supports batch verification of presentation tokens and allows users to interact with other entities without disclosing any sensitive information. In addition to this, multiple authorities are adopted to reveal the real identities of malicious users via cooperation. PEPA is more feasible than other schemes since it relies on a realistic tamper-proof device rather than an ideal one. System analysis demonstrates that the proposed PEPA achieves strong and verifiable privacy-preserving authentication and satisfies the common security and privacy requirements, such as unlinkability, accountability, and so on. Performance analysis and simulation demonstrate that the proposed PEPA offers better performance compared with traditional ones.

Pairing-Free Certificateless Aggregate Signcryption Scheme for Vehicular Sensor Networks

Smart vehicle applications play an increasingly significant role in constructing intelligent transportation systems. Vehicles with sensors are able to form temporary vehicular sensor networks dynamically, in which a mass of data are collected, shared, and aggregated to the traffic administration for improving road safety and efficiency. In such networks, vehicles exchange urgent or sensitive information with others in open wireless channels. To ensure secure and privacy-preserving communications, we develop a new pairing-free certificateless aggregate signcryption scheme based on the elliptic curve cryptosystem. The certificateless setting prevents the proposed scheme from both certificate management and key escrow issues. Aggregate signcryption without pairings realizes the aggregation and simultaneous authentication of multisource data by efficient cryptographic computing. The proposed scheme is proved to achieve confidentiality and unforgeability with the random oracle model. The security analysis shows that the proposed scheme provides forward secrecy, immunity to various known attacks, and pseudonym-based conditional privacy for vehicles. The performance analysis demonstrates that it satisfies more security properties with higher computation efficiency and lower communication overhead than state-of-the-art schemes.

Dual size/charge-switchable and multi-responsive gelatin-based nanocluster for targeted anti-tumor therapy

Biopolymers with excellent biocompatibility and biodegradability show great potential for designing drug nanocarriers, while it's difficult to fabricate smart vehicles with multiple switching (size, surface, shape) based on biopolymers alone. Here, we report a dual size/charge-switchable and multi-responsive doxorubicin-loaded gelatin-based nanocluster (DOX-icluster) for improved tumor penetration and targeted anti-tumor therapy. The DOX-icluster was electrostatically assembled from folic acid and dimethylmaleic anhydride modified gelatin (FA-GelDMA) and small-sized DOX-loaded NH2 modified hollow mesoporous organosilicon nanoparticles (DOX-HMON-NH2). DOX-icluster had an initial size of about 199 nm at neutral pH. After accumulation in tumor tissue, the DMA bond of FA-GelDMA was cleaved and gelatin was degraded by matrix metalloproteinase (MMP-2), thus 48 nm and positively charged DOX-HMON-NH2 was released to facilitate penetration and cell internalization. DOX-HMON-NH2 was further degraded by intracellular glutathione (GSH) with releasing 48.1 % of DOX. The cellular uptake results indicated that the fabricated icluster promoted the uptake of DOX by 4T1 cells. With enhanced penetration efficacy, the tumor spheroids volume treated with DOX-icluster was reduced to 15.1 % on day 7. This cytocompatible multi-responsive gelatin-based icluster with size-shrinking and charge-reversible characteristics may be used as a significant drug carrier for tumor therapy.

Vehicle Monitoring with Web-Based Applications Utilizing High-Precision Positioning

Motorized vehicles have facilitated various human activities in everyday life. As technology develops, various features in driving have been added to the vehicle itself. Starting with the aim of adding comfort, even to increase the safety in the use of vehicles. In the current digital era, the vehicle monitoring process has been widely applied, especially in a number of smart vehicles that are integrated with a monitoring system from various vendors. A web-based application or commonly known as webapp, designed by PT. Telkom Indonesia, Tbk. named IndiCar, is an example of a monitoring system, which can be applied to vehicles that are commonly used daily. This is in line with the main goal of IndiCar, which is the digitalization of public vehicles, into smart vehicles. The implementation of monitoring using the IndiCar webapp can make it easier for vehicle users, both in personal and institutional use, to track and diagnose a vehicle, regarding a number of factors such as precise location, track traveled, to the condition of various vehicle components, utilizing a number of sensors applied to the IndiCar system.

Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach

Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.

Lyapunov-guided optimal service placement in vehicular edge computing

Vehicular Edge Computing (VEC) brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles. However, the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests, thus restricting the performance of VEC system. Besides, a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server. To strengthen the performance of VEC, in this paper we propose to place services beforehand at the edge server, e.g., by deploying the services/tasks-oriented data (e.g., related libraries and databases) in advance at the network edge, instead of downloading them from the remote data center or offloading them from vehicles during the runtime. In this paper, we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline. Specifically, the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization. Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution. The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.

Chat With ChatGPT on Interactive Engines for Intelligent Driving

ChatGPT, a chatbot launched by OpenAI, is making waves across the internet recently. Its potential capabilities and how it can impact various industries have been widely discussed. In the field of smart vehicles, integrating human-machine interaction systems with ChatGPT can be a game changer. For example, Jidu Auto, the electric vehicle arm of Baidu, announced in February 2023 that they will integrate ERNIE Bot [1], Baidu's ChatGPT-like service, into their cars. While ChatGPT has boosted the development of intelligent interaction, some limitations still exist that hinder its practical application. In this article, we discuss its potential applications in intelligent vehicles and analyze the challenges it faces.