Citywide incidents such as crimes, accidents, and public safety threats contribute to substantial societal disruption and economic loss. Accurate prediction of such incidents can significantly aid city administrators in proactive response planning. Existing approaches model the incident prediction as a spatio-temporal task, but often neglect theinter-regionspatial long-tailed distribution of incidents. This uneven distribution introduces spatial bias in learning which causes models to overfit regions with frequent incidents (head regions) while underfit the regions with occasional incidents (tail regions). Furthermore, model learning is hindered byintra-regioncategory imbalance, where certain incident types (e.g., theft) dominate over rarer categories (e.g., robbery) within the same region. To address inter and intra region challenges, we propose an approach namedSLIP(SpatialLong-tailIncidentPrediction). Specifically, for inter-region skewness, SLIP adopts a multi-expert design comprising a common feature extraction backbone followed by three expert branches. In addition, to mitigate the intra-region category imbalance, we utilizes a variant of focal loss, particularly for positive-negative imbalance. SLIP outperforms spatio-temporal state-of-the-art methods by 2-11% in Macro F1, 4-11% in Micro F1, and 1-11% in Severity Weighted F1 across Los Angeles and Chicago cities for the urban crime dataset. Additionally, we incorporate fairness metrics into the evaluation and present a comprehensive comparison of spatio-temporal incident prediction.

The Internet of Vehicles (IoV), facilitated by vehicle communication devices, provides a plethora of applications, including traffic alerts and real-time updates, within intelligent transportation systems. Integrating Unmanned Aerial Vehicles (UAVs) allows IoV to reach new heights, offering benefits such as reduced infrastructure load, rapid emergency response, resource management, and enhanced network performance. Yet, this interconnected world faces substantial security and performance challenges, especially in ensuring secure authentication and efficiency and protecting user privacy. To address these concerns, this paper proposes UMAPE, a secure and efficient authentication protocol assisted by UAVs in an IoV environment. The UMAPE employs elliptic curve cryptography with a hash function to ensure lightweight, more secure and efficient authentication between vehicles, UAVs and the Road Side Units (RSUs), which act as an edge node with admissible overhead. For security, a formal security proof using a random oracle model, which proves the robustness of the session key, and a formal security verification by the Scyther simulation tool. Furthermore, a performance evaluation is performed to assess the efficiency of the protocol, and the findings indicate that the UMAPE reduced computation and energy overhead with, on average, fewer 45.80% computation and 45.05% energy overhead compared to state-of-the-art solutions.

Redactable blockchain presents a solution for fraudulent transaction modification and storage overload by allowing limited data manipulation using Chameleon Hash Functions (CHF). However, CHF-based redactable blockchain encounters significant security challenges like misuse of edit rights, node desynchronization, higher transaction verification, and redaction latency. To solve these challenges, we propose SERB, a secure and efficient redactable blockchain protocol. SERB utilizes a Node Selection, Redactable Consensus, and novel Parameterized Chameleon Hash Function (pCHF) for proper use of edit rights and node synchronization with minimization of transaction verification and redaction latency. The security analysis shows that SERB is secured against tampering attacks, collusion attacks, and forged attacks. The performance analysis indicates that the SERB reduces transaction verification latency by 20% and minimizes redaction latency, such as transaction modification by 25 % and block deletion by$\mathbf{5 0 \%}$compared to state-of-the-art approaches.

Vision-based motion planning is a crucial task in Autonomous Driving (AD). Recent advancements in urban AD show that integrating Imitation Learning (IL) with Deep Reinforcement Learning (DRL) improves decision-making to be more like humans. However, IL methods depend on expert demonstrations to learn the optimal policy. The main drawback of this approach is the assumption that expert demonstrations are always optimal, which is not always true in real-world settings. This creates challenges in adapting to diverse weather conditions and dynamic traffic scenarios, often resulting in higher collision rates and increased risks to pedestrian safety. To address these challenges, we propose a Diffusion-Guided Deep Reinforcement Learning (DGDRL) framework that integrates a diffusion model with a Soft Actor-Critic DRL method to effectively mitigate environmental uncertainties and enable self-learning beyond the training maps for new tasks. This framework follows a novel modified partially observable Markov decision process (mPOMDP) to choose optimal action from original and diffusion-generated observations, ensuring that the policy behavior remains consistent with the current action. We use the CARLA NoCrash benchmark to train and evaluate the proposed framework. The method is validated in diverse urban environments (e.g., empty, regular, and dense) across multiple towns. Additionally, we compare our model against state-of-the-art techniques to ensure robustness and generalizability to new environments. The project page and code are available at the link https://autovisionproject.github.io/project/.

The rapid increase in road traffic has significantly contributed to urban air pollution, excessive fuel consumption, and toxic emissions, necessitating sustainable mobility solutions. This paper introduces the Sustainable Route Recommendation System (SRRS), a smart system that helps find the best driving routes to reduce carbon dioxide (CO2) and nitrogen oxide (NOx) emissions, save fuel, and shorten travel time. SRRS combines two important algorithms: the first is a YOLO-based model that quickly identifies and sorts vehicles into heavy and light types, and the second is a SUMO-based traffic simulation that suggests the best routes based on the type of vehicle. By intelligently distributing traffic, the system reduces congestion and enhances sustainability. Simulations at Apex Circle, Jaipur, show that SRRS reduces CO2emissions by 30.05%, NOxemissions by 31.97%, and fuel consumption by 30.2% compared to conventional methods. These findings establish SRRS as a promising solution for sustainable urban mobility, reducing environmental impact while improving traffic efficiency.

In the current ridesharing scenario, finding a compatible passenger is highly challenging and largely dependent on chance. Existing algorithms prioritize the shortest route without considering future requests or traffic conditions, which reduces the likelihood of matching with another compatible passenger. This uncertainty leads to increased congestion along shortest routes and fewer ridesharing trips overall. This paper proposes a route recommendation strategy that goes beyond the shortest route, aiming to address these issues. The proposed strategy results in higher demand, reduced congestion, broader coverage of points of interests, and an increased probability of finding compatible passengers during a trip. To achieve this, we introduce a time-series forecasting method leveraging a multi-task long short-term memory model to predict demand and traffic patterns in city-zone neighborhoods. These predictions are then used to recommend optimized routes. To evaluate our approach, we tested it on three datasets containing trip and traffic details from New York City, Los Angeles, and Shenzhen. Our model demonstrated 96% accuracy and a 2% RMSE loss in predicting the expected number of passengers. Furthermore, during route recommendations, we observed a 23% increase in passenger count for 97% of trips and a reduction in travel time for the shortest path in 60% of trips. In light of the above experimentation, we believe that while our approach recommends a longer route than the shortest one (for 40% cases), it helps taxi drivers to find compatible passengers on most trips which increases the profit of ridesharing services, and reduces the waiting time for passengers.

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. Through detailed empirical experiments using a testbed, simulations with OMNeT++, and theoretical assessments grounded in real-world datasets, we demonstrate the distinct advantages of our methodology. Furthermore, when benchmarked against existing solutions, our technique performs better in model interpretability, delay minimization, rapid convergence, reducing complexity, and minimizing memory footprint.

The process of transferring land records (LRs) and ownership between users is facilitated by a LR transfer system. This system encompasses a series of procedures, including conducting title search, establishing agreements, executing legal documentation, verifying and transferring ownership, and updating LRs. Despite its importance, the system encounters notable challenges, such as insufficient tamper-proof record-keeping, lack of system compatibility, time-consuming processes, and the presence of intermediaries and brokers leading to potentially fraudulent claims. To address these challenges, a novel solution calledLandChainis proposed in this article. TheLandChainutilizes MultiChain, consisting of MainChain and SideChain, to securely transfer LRs among users, such as buyers, sellers, land donors, and owners. TheLandChainincorporates innovative algorithms like record forwarder selection (RFS), trust establishment (TE), and record transfer and confirmation (RTC). Furthermore,LandChainverifies the legitimacy of users before transferring LRs through the verify user legitimacy algorithm. Security analysis showsLandChainis secure from double-spending, liveness, Sybil, replay, and man-in-the-middle attacks. The implementation ofLandChainis developed and tested on the docker engine platform. According to performance analysis, theLandChainreduces record confirmation latency by 18% (MultiChain) and 50% (Blockchain).LandChainalso increases throughput by 34% (MultiChain) and 45% (Blockchain) when compared to state-of-the-art approaches.

This research introduces PISTON, a novel protocol designed to enhance the security, efficiency, and performance of Internet of Vehicles (IoV) networks. PISTON integrates advanced authentication mechanisms utilizing Physically Unclonable Functions (PUFs) and multifactor authentication with dynamic challenges and zero-knowledge proof-based authentication to ensure robust security and mitigate various cyber threats, including Denial-of-Service (DoS) attacks. The protocol further incorporates sleep-wake scheduling, priority-based scheduling, and adaptive modulation and coding to optimize network performance. The communication overhead in PISTON is derived through a formula that incorporates latency, energy consumption, and throughput, demonstrating the protocol’s efficiency in dynamic vehicular environments. Comparative analysis against existing protocols highlights PISTON’s superiority in seamless handover, provable security, and DoS attack resilience. Experimental results show that PISTON reduces energy consumption by 30% and achieves 20% higher data throughput while maintaining low latency, essential for real-time IoV applications. The empirical findings underscore PISTON’s advancements in establishing a new benchmark for future IoV deployments, ensuring secure, energy-efficient, low-latency, and high-throughput communication.

As remote sensing capabilities continue to advance, there is a growing interest in leveraging computer vision and natural language processing for enhanced interpretation of remote sensing scenes. This paper explores the integration of textual information with images to augment traditional disaster classification methods. Our approach utilizes a predefined vision-language model to generate descriptive captions for images, fostering a more nuanced understanding of the remote sensing data. Next, we seamlessly integrate the generated textual information with image data through multimodal training, employing a multimodal deep learning method for disaster classification. The system categorizes input data into predefined disaster categories, presenting a comprehensive and accurate approach to emergency response system development. Experimental evaluations conducted on the AIDER dataset (Aerial Image Database for Emergency Response applications) showcase the efficacy of our approach, demonstrating improved accuracy compare to unimodal approach and reliability in disaster classification. This research contributes to the advancement of intelligent emergency response systems by harnessing the synergy between vision and language in the context of remote sensing.

The rapid advancement in deep learning-based object detection methods has made them a prevalent choice for real-time applications. Families of object detectors, including one-stage detectors, two-stage detectors, and region-based CNN networks, offer superior performance in accurately detecting objects. Despite their high accuracy, the complex design and black-box functionality of these models are not directly transferable in aerial imagery. Also, raise questions among users regarding the transparency of the algorithm in locating objects. Consequently, to demystify the decision process of these models, there is a need for Explainable AI (XAI) tools. XAI enables an understanding of the significance of each pixel in an image, shedding light on the contributions that lead to the model’s final output. In this context, this work will present an efficient, explainable, multi-scale vehicle detection network from high resolution aerial imagery, named as EVDNet. The EVDNet model has trained with two publicly available aerial image benchmark dataset DOTA and VEDAI. To enhance interpretability, we leverage XAI method using GradCam. The experimental results not only showcase the effectiveness and performance of the EVDNet model but also provide valuable insights into the object detection process. This research contributes to bridging the gap between complex object detection models and user understanding, offering a more transparent and interpretable approach to high-resolution aerial imagery analysis.

Deep learning (DL) based cognitive radio networks (CRN) serve as a potential solution to the dilemma posed by spectrum limits and the rising demand for vehicular ad hoc networks (VANETs) routing services. However, the unpre-dictability of VANET restricts the generalization potential of DL-based techniques. Variations in traffic volume, road topologies, and radio propagation characteristics affect the training data significantly. Therefore, in this paper, we propose RF -CVN, a recurrent reinforcement learning (RRL) technique to sense the spectrum and discover a trustworthy path between the source and the destination using belief transmission (i.e., channel conditions, interference levels, and vehicle locations). We first devise a deep recurrent Q network for a multi-channel access scheme for unlicensed users to use available channels. The RRL allows the Q function to learn hidden states in partial observation or highly time-correlated network sensing cases. Later, the trust values are used to gain a more nuanced understanding of the network state, thereby enhancing the efficiency and reliability of the routing process. In this work, we argue that trust should be an integral part of the routing process and, therefore, design a trust mechanism to select a path. The trust mechanism aims to detect those spectrums that over-utilize or under-utilize their channel capacity during the local training. The outcomes of our simulations indicate that our RF -CVN routing method outperforms traditional routing systems based on cognitive radio-based vehicular ad hoc networks in terms of network performance and spectrum sensing efficiency.

Finance is the backbone of any organization or government. The government's financial health relies heavily on managing its FinTech applications. In the current FinTech system, user financial information is stored centrally. In the centralized system, the information security risks are high. Thus, a decentralized system is required for better security, trust and safety management in the FinTech information system. Blockchain is a decentralized technology that can solve the issues of traditional FinTech applications like banking. In the blockchain, consensus protocols are responsible for maintaining a consistent copy of the blockchain at each node of the blockchain network. However, these protocols are not suitable for regular currency transactions due to high latency and low throughput. Therefore, to achieve reliability, low latency, and high throughput, we have introduced a new protocol called FinBlock. In FinBlock, the pipeline concept is introduced in blockchain to increase the transaction throughput. Additionally, the number of message broadcasts is also reduced, which further improves the latency. Moreover, FinBlock achieves a speedup of 3 with respect to traditional practical byzantine fault tolerance (PBFT) consensus protocol. The result showed that FinBlock achieved better transaction processing time, transaction throughput, and message count than the traditional PBFT, even with hundreds of nodes in the network with reduced message complexity.

Humans relocate for a variety of reasons, including employment, study, tourism, family, and health. However, in COVID-19, the government imposed restrictions such as lockdowns, travel bans, and quarantine regulations, preventing many people from traveling for work, study, or leisure; thus, human mobility exhibits distinct patterns than ordinary movements. In this article, we analyze the effect of COVID-19 on interregion human mobility using curated Twitter data and propose a framework namedCo-Movefor human mobility prediction. There were three challenges in predicting mobility: 1) heterogenous data; 2) short and long-term periodic patterns; and 3) complex intercorrelation. To address these challenges, the framework comprises parallel multiscale convolution and long short-term memory components. Extensive experiments on real-life mobility datasets show the mean square error (MSE) of 0.0179, RMSE of 0.129, mean absolute error (MAE) of 0.1075, and outperform baseline models.

The integration of software-defined networking (SDN) and cloud radio access networks (CRANs) into vehicular ad hoc networks (VANETs) presents intricate challenges to achieving stringent service level objectives (SLOs). These objectives include optimizing data flow and resource management, achieving low latency and rapid response times, and ensuring network resilience under fluctuating conditions. Traditional load balancing and clustering approaches, designed for more static environments, fall short in the dynamic and variable context of VANETs. This necessitates a paradigm shift towards more adaptive and robust strategies to meet these advanced SLOs reliably. This paper proposes a software-defined vehicular fog computing (SDFC) framework that refines resource allocation in VANETs. Our SDFC framework utilizes an intelligent controller placement that strategically positions decision-making entities within the network to optimize data flow and resource distribution. This placement is governed by a dynamic clustering algorithm that responds to variable network conditions, an advancement over the static mappings used by traditional methods. By incorporating parallel processing principles, the framework ensures that computational tasks are distributed effectively across network nodes, reducing bottlenecks and enhancing overall network agility. Empirical evaluations (testbed) and simulation results of our framework indicate a substantial increase in network efficiency: a 28% improvement in average response time, a 23% decrease in network latency, and a 25% faster convergence to optimal resource distribution compared to state-of-the-art methods. These improvements testify to the framework's ability to underscore its potential to refine operational efficacy within VANETs.

Despite numerous research efforts, vehicular networks strive to provide primary facilities for the Internet of Vehicles (IoVs), which are higher data rates, robust connectivity, scalability, security, and privacy facets. In this paper, we proposed a decentralized approach SecEdge to efficiently integrate the Vehicular Cloud’s (V-cloud) concept with the idea of edge-computing in IoV that consider Roadside Connecting Nodes (RSCNs) as an intelligent edge. Furthermore, the heterogeneity and highly dynamic network structure of IoV raise many security and privacy issues while designing the hybrid approach. SecEdge focuses on secure inter-vehicle/intra-vehicle communications using one-way hash functions and secure storage/retrieval of data in/from the upper layer of the architecture. Thus, the system becomes more reliable, safe, and scalable for the drivers as well as the passengers. By providing secure data storage, we can overcome the data leakage problem that occurs due to side-channel attacks and weak security parameters. The qualitative and quantitative analysis depicts that the SecEdge has reduced the computation cost and energy consumption by up to 75% to other state-of-the-art methods. The security analysis provides the formal security proof based on the Random Oracle Model (ROM) and ProVerif tool that shows the security strength in terms of privacy preservation, location tracing, and revocation along with the different security attacks such as man-in-the-middle attack, impersonation, denial-of-service attack, repudiation, replay attack, modification, etc.

The Internet of Vehicles (IoV) faces significant challenges related to secure authentication, efficient communication, and privacy preservation due to the high mobility of vehicles, the need for real-time data processing, varying quality of communication links, and the diverse range of devices and protocols requiring interoperability. These challenges are further complicated by the large-scale, dynamic, and heterogeneous nature of IoV systems. Traditional approaches using Road Side Connecting Nodes (RSCNs) face challenges like limited range, high costs, and single points of failure. Drone-assisted IoV (DIoV) networks address these issues by using Unmanned Aerial Vehicles (UAVs) as mobile edge nodes, enhancing connectivity, extending coverage, and improving adaptability and resilience. To address these challenges, we propose SECURE, a drone-assisted, Physically Unclonable Function (PUF)-based authentication and privacy-preserving protocol integrated with edge computing. This architecture replaces RSCNs with edge nodes and incorporates UAVs as mobile edge nodes, providing extended coverage, reduced latency, and enhanced adaptability. The PUFs in SECURE generate unique hardware-based cryptographic keys, adding an additional layer of security, while edge computing offloads computational tasks, improves network efficiency, and further reduces latency. The formal security analysis, conducted using the Random Oracle Model (ROM), proves the robustness of the session key against active and passive adversaries. Furthermore, informal security analysis demonstrates that SECURE effectively resists various security attacks, while achieving confidentiality, integrity, and authenticity in DIoV. In SECURE, we have considered two types of devices for experiments: NVIDIA Jetson Xavier NX and Raspberry Pi 4. The performance analysis, considering the results from Jetson Xavier NX, demonstrates that SECURE achieves maximum upto approximately 82.1% less communication cost and 78% faster computation time compared to the state-of-the-art schemes.

In an emergency healthcare situation, delay between injury and treatment is one of the most critical parameters with regard to survivability. Reduction in diagnosis/pre-treatment time by processing real-time ambulance data while en route to hospital can cut back the delay in treatment of the patient. However, several research challenges arise in accessing real-time patient data from ambulance to hospital while moving along different Road Side Units (RSUs). Due to the severity of medical data, there is a need to minimize computational losses along with costs due to migration and ambulance perceived latency. Considering the above scenarios, this paper formulates an average cost minimization problem keeping latency, energy, and loss function into deliberation as NP-hard. To solve the formulated problem, Minimum Cost Algorithm (MCA) using Federated Averaging (FedAvg) algorithm utilizing RSUs for effectively transferring real-time patient data to hospitals has been proposed considering above stated constraints altogether. Moreover, to handle imbalances in health data across different hospitals during processing, FedAvg algorithm combines augmentation techniques. Through experimental and prototype demonstration, the efficacy of proposed framework is shown by achieving$12.5 \%, 27 \%,$and$38 \%$reduction in an average total cost compared to other state-of-the-art techniques on real-world data sets, respectively.

With an increasing number of accidents and inefficient resources to inform authorities and hospitals quickly, there is a rise in the number of death cases because of traffic accidents. As per a report [9], in India, a total of 151,113 deaths have been reported in 480,652 traffic accidents in 2019, resulting in an average of 17 deaths per hour! As we continue to make progress towards building smart cities, the expectations to predict the anomalies such as traffic accidents also rise. In this paper, we have built a highly secure and increasingly accurate accident prediction system, which we believe brings us one step closer to Intelligent Transportation Systems. Our system runs a trained Machine Learning algorithm and can be updated by participants collaboratively, which makes it a system that can potentially achieve the highest accuracy following secure protocol. Besides, data breaches and changes in models’ parameters are prevented with the use of blockchain. To avoid malicious participants, we have designed an astute incentive mechanism. To validate the claims and our system’s performance, we have further used a dataset from the UK govt website with information about accidents, vehicles, and casualties. We have experimented with various machine learning models as participants of the system hosted on a blockchain.

As connectivity in electric vehicles (EVs) expands, so does their vulnerability to cyber threats within the Internet of Vehicles (IoV). This study introduces a potent Intrusion Detection System (IDS) that leverages distributed edge computing, Convolutional Neural Networks (CNNs), and ensemble techniques to address this concern in EV systems. Performance evaluations on reputable IoV security datasets have demonstrated this IDS’s efficacy, delivering detection rates and F1-scores over 100%. These results affirm the system’s potential to significantly enhance the cybersecurity of both internal and external vehicular networks in the context of connected EVs.

This paper investigates the feasibility of cache content prediction and coherence in the context of secure communication and search. We introduce a distributed multi-tier mobility-assisted edge intelligence based caching framework for the Internet of Vehicles (IoVs), called CacheIn. The proposed framework leverages user preferences, data correlations, and mobility information to prefetch content to the IoV edge. To enable content management based on mobility, we propose a novel Normalized Hidden Markov Model (NM-HMM) that anticipates a vehicle's future position. The framework also utilizes a mobility-aware collaborative filtering-based federated learning (FL) technique to enhance cache hit, reduce latency, and protect user privacy. To ensure secure cross-domain data sharing and mitigate the risk of data breaches, we also propose an extended ciphertext policy attribute-based encryption (ECP-ABE) mechanism. Compared to content popularity-based caching schemes, CacheIn achieves up to 80%, 38%, and 55% improvement in cache hit ratio for different cache sizes, vehicle densities, and cache lookup scenarios. Moreover, our approach reduces key generation, encryption, and decryption times by 35 %.

With the advent of cross-chain, moving assets across blockchain is now possible on a decentralized network. In a decentralized environment, asset transfer to a random blockchain would eliminate committing to a single blockchain. Many domains, such as banking, smart healthcare, smart homes, and the industrial internet of things (IIoT), benefit from cross-chain applications of blockchain. Cross-chain implementation is still in its infancy and confronts issues in preserving many of the asset's features when transferred across the network. Using cross-chain for asset transfers necessitates the presence of five essential characteristics: non-repudiation, unlinkability, confidentiality, atomicity, and interoperability. We proposed CrossLedger, a new Cross-chain based technique for asset transfer that includes all of the features mentioned above. To keep the qualities described above, the CrossLedger uses a novel Asset Forwarder Selection (AFS), Trust Establishment (TE), and Asset Transfer and Confirmation (ATC) algorithms. The proof of characteristics demonstrates that CrossLedger supports all the aforementioned features for asset transfer. The security analysis proved that CrossLedger is protected from double-spending, liveness, and Sybil attacks.

Deep learning-based object detection methods in natural image datasets have demonstrated remarkable accuracy and lower error rates than those of humans. As a result, they have gained significant attention in the field of remote sensing imagery. However, direct transferablity of these methods in remote sensing images face challenges such as scale variations, complex object distributions, and arbitrary orientations. In order to address these challenges, we propose the transformer-based object detector named as DG-YOLOT where we use a guided self-attention mechanism with YOLOv5 to enhance the potentiality of training with minimum computation. Instead of employing uniform size patches like the conventional vision transformer, we leverage density map patches which facilitates the extraction of diverse contextual information related to objects within the image, enhancing the differentiation capability of our model. Through extensive experiments conducted on the DOTAv2.0 dataset, our proposed model has demonstrated superior performance with 57.91% mean Average Precision (mAP) compared to other state-of-the-art object detectors.

The Internet of Vehicles (IoV) enables vehicles and Road-Side-Unit to communicate and exchange data, promising to improve transportation safety, efficiency, and convenience. However, to realize the full potential of IoV, secure and fast traffic accident information (TAI) communication among IoV nodes are essential. The Practical Byzantine Fault Tolerance (PBFT)-based blockchain for IoV secures TAI; it is prone to high message overhead and transaction latency, which causes TAI transmission between IoV nodes to be delayed. This paper proposes a new consensus algorithm, named HN-mPBFT (Healthy Node based Modified PBFT), specifically designed for IoV. The HN-mPBFT consensus method reduces message overhead and transaction delay by reducing PBFT consensus phases while achieving consensus among healthy nodes. According to security analysis, HN-mPBFT is secure from Sybil and DDoS attacks. Performance analysis shows that HN-mPBFT reduces transaction latency by 50%.

The growth of the Internet of Vehicles (IoV) has introduced new challenges and opportunities. Among the most crucial considerations is ensuring the safety of passengers and the environment. Connected vehicles offer numerous benefits, but they are also at risk of fire incidents caused by various factors such as electrical failures, fuel leaks, and collisions. These events can result in devastating outcomes, including property loss, injury, and even loss of life. The conventional fire detection systems employed in vehicles are large, expensive, and consume a considerable amount of power, making them incompatible with the resource-limited environment of the IoV. The present work overcomes these limitations with the introduction of FlameNet, a custom-designed neural network for fire detection. FlameNet not only outperforms existing solutions but also boasts a lightweight design, which contributes to its high computational efficiency, allowing it to run smoothly on low-cost embedded devices with a frame rate of 28 frames per second. Accuracy, recall, precision, and F-measure were used to assess the model’s efficiency on both industry-standard fire datasets and a custom-built test set. The results showed that FlameNet performed well on both datasets, with its performance being better on the standard fire test dataset due to its limited image diversity. The performance of the model is encouraging, and the IoT functionality allows immediate visual feedback and a fire alarm in the event of an emergency.

Online vehicle-for-hire service firms such as Ola, Uber, Gett, Lyft, Hailo, Didi, and GrabCab rely on driver recommendations. Several factors should be considered when suggesting a driver for the trip, including ride cost, driver-vehicle-passenger safety, reliability, robustness, and comfort. Multi-task learning has become more widespread in recommender systems in recent years. Our study employs federated multi-objective multitask learning to develop a driver recommendation system that considers drivers’ cognitive-physical stress and driving behavior while respecting users’ privacy. We use publicly available datasets from UAH-DriveSet, HCI Lab, and PhysioNet for training, testing, and evaluating our system. Our main contribution is a unique framework for driver suggestion based on data-driven federated multi-task learning that achieves the best F-measure accuracy. Our proposed system outperforms baseline and state-of-the-art deep learning models. It detects driver stress and behavior with 95% and 96% F-measures, respectively.

The performance of vehicular edge computing (VEC) depends on the effective optimization of task offloading. However, uneven distribution of vehicular traffic, rapidly changing network conditions, and stochastic nature of vehicular networks motivate us to innovate approaches to efficient resource management while maintaining system's stability. To address these challenges, we propose a novel queue length-based stochastic task migration strategy that leverages model predictive control (MPC) and Lyapunov optimization techniques. Our approach employs the queue length at the edge node as the criterion for offloading decisions. The MPC controller dynamically allocates the processing power and bandwidth resources to vehicles based on their current requirements, facilitating prompt offloading decisions. The Lyapunov optimization ensures long-term system stability. Our method also incorporates dynamic request selection from multi-dimensional queuing load optimization and ensures fair and efficient load distribution, thereby enhancing edge server utilization. We evaluate the performance of our proposed approach via simulation experiments and demonstrate its superiority by reducing the queue length at the edge node and adhering to delay constraints of vehicular networks.

Vehicular Cloud Computing (VCC) extends the scope of smart transportation applications through sharing vehicular data and resources. It is thus pivotal to transmit, save, and extract vital data with adequate security and privacy protections. There is yet no lightweight vehicular data exchange mechanism for resource-constrained environments that simultaneously offers secure and low-cost message transmission, data storage, and information searching. State-of-the-art vehicular communication protocols are also vulnerable to crucial security attacks and require more computational resources during the implementation. We, therefore, propose secure and efficient message communication, data storage, and information searching protocols (named QueryCom) for smart transportation. The QueryCom is designed using SHA-512 and Advanced Encryption Standard (AES-256), preserving security and user anonymity attributes. Further, an index-based sequential searching method is used to effectually obtain information from the Vehicular Cloud (VC) database. We evaluate QueryCom based on the security proof and attacks analysis to confirm its security robustness against crucial security attributes. The experimental testbed results (on Raspberry Pi 3B+) are discussed to analyze the computational proficiency based on different performance parameters, i.e., time complexity (to search information from the VC database), computation time, storage cost, communication overhead, and energy consumption.

The fully dynamic dense environment of the Internet of Vehicles (IoV) and conditions like high traffic jams, collisions, and uneven road conditions increase the communication messages. Consequently, the computation and communication costs of the IoV network are increased. Therefore, IoV needs a lightweight and efficient security solution along with effective communications. Existing state-of-the-art schemes in IoV are generally based on cryptographic methods that use session keys, public keys, elliptic curves, and Message Authentication Codes (MAC). In contrast to the other schemes, MAC-based schemes provide feasible security solutions having high performance with less overhead. The existing MAC-based authentication schemes are vulnerable to the key disclosure issue along with some security attacks such as side-channel attacks, Distributed Denial of Services (DDoS), etc. Due to the side channel attack, a data leakage problem occurs, which is a serious concern in security and privacy facets. We propose a novel MAC-based authentication scheme (NoMAS) that mitigates the above-discussed challenges and provides all the benefits of MAC-based schemes. The NoMAS scheme offers a solution for key disclosure issues by providing Hard Key and Soft Key Updates (HKU and SKU) and data leakage problems using encryption along with high performance. It reduces the computational overhead maximum to 99.60% and the communication overhead maximum to approximately 81% compared to the existing schemes. In the security analysis, We have provided the formal security proof using BAN logic and the ProVerif tool and verified the correctness of the scheme.

Traditional security solutions for the edge have lots of challenges like high power consumption, communication, and computation overhead. These solutions are not feasible for highly dynamic resource-constrained (memory and processing power) Internet of Vehicles (IoV) networks. We can use Physical Unclonable Functions (PUFs) to address this issue. This paper discusses a new PUF-based anonymous mutual authentication and key exchange protocol for the IoV communication environment by combining unique, unpredictable PUFs and one-way hash functions. In the proposed protocol, a three-layered infrastructure, i.e., vehicle layer, edge computing layer, and cloud layer, is used for the IoV networks to make it efficient and improve the throughput and Quality of Service (QoS). The proposed hybrid system provides a security solution for cloning, side-channel and physical attacks along with the less computation and negligible storage cost. The implementation and performance analysis shows that the proposed protocol reduces the computation-communication overhead maximum upto 80% and 60% respectively. It also reduces the time complexity.

Urban anomalies are abnormal events such as a blocked driveway, illegal parking, noise, crime, crowd gathering, etc. affect people and policy managers drastically if not handled in time. Prediction of these anomalies in the early stages is critical for public safety and mitigation of economic losses. However, predicting urban anomalies has various challenges like complex spatio-temporal relationships, dynamic nature, and data sparsity. This paper proposes a novel end-to-end deep learning based framework, i.e., UApredictor that utilizes stacked spatial-temporal-interaction block to predict urban anomaly from multivariate time-series data. We model the problem using an attribute graph, where we represent city regions as nodes to capture inter region spatial information using a spatial transformer. Further, to capture temporal correlation, we utilize a temporal transformer, and the interaction module retains complex interaction between spatio-temporal dimensions. Besides, the attention layer is added on the top of the spatial-temporal-interaction block that captures important information for predicting urban anomaly. We use real-world NYC-Urban Anomaly, NYC-Taxi, NYC-POI, NYC-Road Network, NYC-Demographic, and NYC-Weather datasets of New York city to evaluate the urban anomaly prediction framework. The results show that our proposed framework predicts better in terms of F-measure, macro-F1, and micro-F1 than baseline and state-of-the-art models.

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, side-slipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's experience, RsSafe suggests that the driver accepts or rejects the next trip based on the predicted driving behavior. In particular, we propose a fusion architecture that learns to predict the driver's behavior for the next trip using information from multiple streams. This architecture consists of Multi-task Learning with Attention (MTLA) that captures individual drivers' personality traits to deal with the adaptability of system. We use publicly available naturalistic driving behavior analysis dataset, namely the UAHDriveSet, results show that the MTLA predicts with F-measure score of 96%; and outperforms the baseline as well as state-of-the-art models.

Improving driving safety by monitoring driver behavior is an excellent example of Advanced Driver Assistance Systems (ADAS). This paper proposes an end-to-end transformer-based driver behavior classification framework named Trans-DBC. It calculates driver behavior from the multivariate time-series smartphone telematics data by learning short and long-range temporal dependencies effectively and accurately, unlike prior data-driven deep learning models that capture information locally via convolutional or recurrent structure in an iterative manner. The extensive human-in-the-loop study on a publicly available UAH-DriveSet dataset shows that the proposed technique can classify unsafe driving behavior with a 96% of average weighted precision, recall, F-measure, and 95.38% accuracy. Our suggested model outperforms baselines and state-of-the-art models on the UAH-DriveSet dataset and five multivariate time-series datasets in driving behavior analysis.

In the digital era, the web and social networks have become an essential part of our society's daily lives. Generally, people worldwide use these networks to access or share information, but communication over these networks also has anomalous behavior. The anomalous behavior is the change in the network that is abnormal or rare occurrences that may relate to frauds, real-life events, shilling attacks, denial of service attacks, follower boosting etc. In this paper, we propose a novel method, i.e., ANOM-DGCN, which is modification of the graph convolution network. We use attributed graph that represents the network dynamics as attributes and communication as edges. We conduct experiments on publicly available datasets, such as Enron, DARPA, and TwitterSecurity, where our proposed method outperforms existing state-of-the-art models. ANOM-DGCN present results with AUC of 83% and provide spatial-temporal metadata for further analysis.

Over the period, the universal internet of vehicles (UIo V) has acquired considerable attention in enhancing drive safety, traffic management, infotainment. Due to the heteroge-neous communication, it is highly vulnerable to various types of security and privacy attacks. Security and privacy concerns in the UIo V are commonly addressed with central authentication, in which only the registration authority has the power to authenticate vehicles and other UIo V components. Some of the researchers proposed anonymous authentication techniques to re-move this dependency. However, the existing methods for anony-mous authentication endure a very high computation overhead for the certificate and signature validation process, due to which a high rate of message loss happens. In this paper, we propose a lightweight anonymous authentication scheme called LAAS for UIo V using a bilinear map and lightweight cryptographic operation (i.e., one-way hash function, XOR, concatenation) to achieve a high level of security and privacy. Additionally, we propose an idea of batch message verification. The performance analysis of LAAS demonstrates that it is lightweight and produces an efficient result in terms of computational cost and latency when compared to other existing state-of-the-art schemes for verifying signatures and certificates by holding conditional privacy in UIo V, as demonstrated in this paper.

This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges. Experimental results show that AlcFier performs efficiently, improves cluster stability, reduces transmission delays, and improves throughput compared with the state-of-the-art routing protocols.

One of the most significant map services in navigation applications is route recommendation. However, most route recommendation systems only recommend trips based on time and distance, impacting quality-of-experience and route selection. This paper introduces a novel framework, namely MARRS, a multi-objective route recommendation system based on heterogeneous urban sensing open data (i.e., crime, accident, traffic flow, road network, meteorological, calendar event, and point of interest distributions). We introduce a wide, deep, and multitask-learning (WD-MTL) framework that uses a transformer to extract spatial, temporal, and semantic correlation for predicting crime, accident, and traffic flow of particular road segment. Later, for a particular source and destination, the adaptive epsilon constraint technique is used to optimize route satisfying multiple objective functions. The experimental results demonstrate the feasibility of figuring out the safest and efficient route selection.

In recent years, the use of social media networks has increased. With the rise in social network usage, social media businesses have begun to place a greater emphasis on large-scale social network analysis. As a result, several components of the signed network have been studied and analysed. Link prediction is a crucial aspect of this signed network analysis. The majority of previous study focused on forecasting the network’s good aspects, such as favourable user connections, but the negative component(i.e., Negative Link) is just as significant as the positive (i.e., positive link). It enhances the performance of several current positive link analysis programmes. As a result, we concentrate on predicting negative ties from a social network that contains both positive and negative interactions in this research. To improve prediction precision, we use social theories such as balancing and status theory. We utilise the XGBoost classifier to determine if a particular missing link is positive or negative. Finally, we use accuracy and F1 measure measures to assess our proposed model.

An IoV enables secure transmission and storage of real-time traffic accident information among IoV devices like Vehicles, Road Side Unit (RSU), and Edge Servers to reduce unintended loss. Regardless of the advantages, accurate traffic accident prediction and decentralized data storage remain challenges for a reliable IoV system. We propose IntelligentChain, a blockchain and machine learning-based edge server for IoV that enables low-latency IoV device registration and secure traffic accident information communication between them. IntelligentChain then implements novel decentralized trust, consensus procedures for blockchain-based traffic accident information storage and predicts localized area traffic accidents using LSTM recurrent neural networks. IntelligentChain has been implemented on the Raspberry Pi 4. The experiment results illustrate that IntelligentChain achieves 90% prediction accuracy on the Kaggle dataset while decreasing transaction approval delay.

Clustering plays a vital role in establishing a more stable global network topology in Vehicular Ad Hoc NETworks (VANETs) and supports Intelligent Transportation Systems (ITS) applications and message routing. However, due to the unstable infrastructure of VANETs, cluster size and geographical span have a significant impact on maintaining cluster stability and network efficiency. Thus, this paper presents a hybrid machine learning (ML) and meta-heuristics (MH) based routing scheme called MetoidS to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate vehicle orientation-based unsupervised clustering and population based MH to provide a new direction to the taxonomy of the approaches to handling efficient route discovery and cluster maintenance challenges. To represent a real-world simulation of our approach, we have conducted the experiments using a combination of four frameworks (i.e., OMNeT++, SUMO, VEINS, and INET) that demonstrate better performance in terms of high cluster stability, enhanced throughput, high packet delivery ratio, and minimizes average transmission delay compared to the existing routing protocols used in this research.

We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV’s course is shorter than the vehicle trajectory on the ground because the UAV follows an Euclidean path. Following that path, UAVs can be 5 to 8 times faster than wheeled vehicle, in absence of wind. Highly important, the ATS simulator allows to take care of the wind impact in a realistic way. Tailwind increases UAVs speed which becomes up to 10 times faster than the wheeled vehicles, whereas the headwind and crosswind slowdown the UAVs as the traffic slowdown the wheeled vehicles. Our work proves that air traffic simulators pave the way for realistic simulations of UAVs systems.

Despite the tremendous growth of the transportation sector, the availability of systems that ensure safe, efficient, sustainable transportation reduces traffic congestion, maintenance costs, the off-road time of the vehicle, enhances driver’s experiences, and ensures a more reliable journey are very limited. The fast evolution of our economy, lack of driver training, and the grown affordability of our society are reasons for this mismatch in developing economies. We think that the inconsistency will increase and unfavorably affect our traffic structure unless intelligent algorithm-based solutions are developed and deployed. This article presents a system for providing safe, accurate, comfortable, reliable, fuel-efficient, and economical driving behavior using Machine Learning techniques like the hidden Markov model (HMM). Our proposed system recommends subsequent trips using a multi-objective optimization (MOO) technique for the driver. It provides suggestions concerning speed limits and alerts based on the driver’s behavior score and fuel efficiency. We used a publicly available UAH-DriveSet dataset captured by the driving monitoring app DriveSafe for all of our experiments. The results reveal that the proposed model predicts behavior with 95% accuracy and calculates fuel efficiency to improve driving quality and experience. This system recommends safer, more comfortable, more reliable, more efficient, and economical rides, beneficial for everyone in our society.

Revolution in the transportation industry was envisioned by self-driving vehicles, with the potential of communication with other vehicles and interaction with other units of the smart environment. With the advent of the Internet of Vehicles(IoV), which is a system that allows communication between vehicles and other static components of the environment and is a convergence of the Internet of Things (IoT), vehicles are considered as an essential part of the network having secure data sensing and processing platforms. Many vehicular ad-hoc networks (VANETs) research focused on the limited set of applications for intelligent transportation systems (ITS) and typically implied safe driving, infotainment, and traffic management. However, it still exhibits an enormous scope of enhancement in various areas of ITS. In this article, we present a new architecture for IoV, keeping in view the different aspects of ITS and discussing how it can mitigate the challenges of secure communication in IoV along with the new visionary solution for authentication. An IoV is highly dynamic, having different topological structures, uneven distribution of nodes, and, therefore, vulnerable to various security and privacy attacks. This article also presents an efficient batch verification scheme having lightweight authentication that uses bilinear map and one-way hash functions to ensure a high level of security within the limited time constraint as compared to single message verification. The proposed scheme has better performance than the existing techniques in terms of computational delay, time complexity, transmission overhead, and energy consumption.

The Bitcoin-Next Generation (NG) is a new blockchain protocol that is designed to scale the efficiency in terms of throughput and delay. Bitcoin-NG is a Byzantine fault-tolerant blockchain protocol robust to extreme churn and shares the same trust model as Bitcoin. Researchers are trying to extend their applications by integrating them with existing technologies, such as the Internet of Things (IoT). However, due to high computational power and high consensus delay, current blockchain protocols, such as Bitcoin, are not suitable for working with lightweight IoT devices. Moreover, they are not energy-efficient as the mining of blocks requires dedicated mining machines that consume electricity. However, protocols, such as Bitcoin-NG, are designed to reduce the consensus delay, but they still need high computational power and high energy. This article proposes a new mechanism for blockchain to reduce the consensus delay, reduce energy consumption, and increase the throughput by introducing a new leader election scheme in the blockchain.

By 2020, around 25 billion “things” will be connected to the Internet for a better society using different technological systems. Vehicle users have better experience by collaborating the Internet of Things (IoT) and vehicular ad-hoc network (VANET) architectures, and this emerging field is called the Internet of vehicles (IoV). Therefore, the IoV architecture will play an important role in the industry, research organization, and academics for various public and commercial applications. However, the IoV structure should ensure secure and efficient performance for vehicular communications, else an attacker may interfere in the system. In this article, we propose protected comprehensive data dissemination protocols (say IoVCom) based on one-way hash function and elliptic curve cryptography (ECC) for the IoV structure. Next, we analyze security strengths of the IoVCom against various security attacks and discuss performance results in terms of communication overhead, computation time, storage cost, and energy consumption.

Vehicles are especially capable of exchanging pertinent information with nearby vehicles. However, there are multiple challenges like secure data exchange, fast message transmission, dynamic topology, and user data protection while transmitting relevant information between moving vehicles on the road. Therefore, researchers suggested different vehicle-to-vehicle (V2V) message communication and verification mechanisms, but they are vulnerable to crucial security attacks. Furthermore, the existing V2V communication schemes relatively require high operational costs for the implementation, taking more time and computational resources for road safety and traffic data exchanges. In this article, we propose a secure and efficient V2V message communication protocol (named asVCom) for vehicle users using a low-cost function (i.e., SHA-256) while preserving user anonymity. The security proof and analysis are discussed for the VCom to confirm its security and user privacy strengths against different security attributes and attacks. The test-bed implementation results show that the VCom is comparatively efficient in the computational cost, communication overhead, storage cost, and energy consumption.

The Internet of Vehicles (IoV) offers an emerging paradigm that deals with interconnected vehicles interacting with the infrastructure, roadside units (RSUs), sensors, and mobile devices with a goal to sense, compute, store, and transmit vital information or data over a common channel while vehicles are moving. Secure and reliable communication and efficient on-device performance are thus crucial challenges in this paradigm, particularly in presence of limited computation resources. This paper presents a novel secure and energy-efficient message communication system, called MComIoV, using a one-way hash function and elliptic curve cryptography (ECC). We evaluate MComIoV through security proof and analysis against various attacks to verify its robustness. The proposed system is also implemented and tested on Raspberry Pi 3B+. Experimental results demonstrate the efficiency in computation time, storage cost, communication overhead, and energy consumption.

Cloud Computing offers storage resources as well as network and computing resources to the organizations. This eliminates the high infrastructure cost for the organizations that are using these services as they can now dynamically pay for these services, i.e., pay per use model, which is followed by most of the cloud providers. As the organization does not locally host these resources, these are comparatively far easier to manage and use than the traditional infrastructural resources. As a result of these factors, the popularity of cloud computing is increasing continuously. But this transfer of data and applications to the cloud server also creates some challenges. It poses problems that must be dealt with properly to ensure a secure cloud computing environment. As more and more sensitive data is being uploaded on the cloud in the present scenario, the privacy and security concerns associated with the data is continuously increasing. To address this, issue the data is stored on the cloud in the encrypted form. Also, as the amount of data stored is usually tremendous, so an efficient search scheme is also necessary. So here, we deal with two significant aspects of cloud computing: Encryption and Searching. We are proposing a secure and efficient encryption scheme to encrypt the data stored in the cloud as well as the queries along with a multi-keyword search scheme to search over the encrypted cloud data.

Since the advent of cloud computing, a huge number of data owners are outsourcing their sensitive data to be stored on to the cloud. To maintain privacy requirements, this sensitive data should be encrypted when it is stored on the cloud server which renders simple keyword based document retrieval schemes obsolete. In this paper, we propose a semantic multi-keyword ranked search scheme for document retrieval on cloud data that is encrypted. The proposed scheme returns not only the documents containing terms that match with our query terms but also some more documents that contain terms which are semantically similar to the query keywords. Our experimental result shows that our technique is more precise than TF-IDF/VSM(Vector Space Model) models that focus only keyword matching while simultaneously achieving faster retrieval times than other tree based TF-IDF/VSM approach as our algorithm runs on reduced dimensions.

The integration of learning architecture with SDN-based VANETs (SDVN) is beneficial for utilizing computing power by decoupling network management services from data transfer services. However, fast safety messages dissemination in a highly dynamic vehicular environment is a challenging and complex dilemma due to bi-directional traffic and the directional movement of vehicles. It is also challenging to get an effective solution against bottleneck situations and a reliable and fault-tolerant SDN network using clustering. So considering the features of adaptive learning, in this paper, we propose adaptive self-learning clustering algorithm with reinforcement routing in SDVN known as RL-SDVN. An Expectation-Maximization model is used to predict a vehicle's movement and further Q-learning model is used to route data packets, so that vehicles in the same cluster coordinate with each other to find optimum routes. We evaluate our experimental results by comparing our approach with the clustering and self-learning based schemes proposed in the past. The outcomes exhibit that the proposed scheme improved cluster stability and life-time of a cluster member vehicle with better performance in terms of low average transmission delay, and high throughput compared to the existing routing protocols used in this research.

Wireless sensor networks (WSNs) are gaining traction in day to day applications where data has to be gathered in hostile environments using low computing power sensors. As these sensors are deployed in hostile environments, strong authentication mechanisms are essential to guarantee the safety and security of the data being transferred. But, these strong authentication mechanisms are computationally heavy for the low power sensors. So, there is a need for a lightweight and secure authentication protocol for WSNs. Applications of these sensor ranges from healthcare to oil and exploration. The security of the data being gathered and transmitted is of utmost importance as they can correlate to life and death situations in some instances. So, employment of extremely secure schemes is a necessity but all these schemes are computationally very expensive and require powerful hardware. The sensors used in WSN are designed for low power environments, and longevity and hence do not possess these hardware resources to secure the communication. Hardware-level, as well as lightweight operations, have to employ for securing the communication. This procedure also preserves the integrity of data being gathered and transmitted by the sensor through these WSNs.

Ensuring secure transmission over the communication channel is a fundamental responsibility to achieve the implementation objective of universal internet of vehicles (UIoV) efficiently. Characteristics like highly dynamic topology and scalability of UIoV makes it more vulnerable to different types of privacy and security attacks. Considerable scope of improvement in terms of time complexity and performance can be observed within the existing schemes that address the privacy and security aspects of UIoV. In this paper, we present an improvised authentication and lightweight batch verification method for security and privacy in UIoV. The suggested method reduces the message loss rate, which occurred due to the response time delay by implementing some low-cost cryptographic operations like one-way hash function, concatenation, XOR, and bilinear map. Furthermore, the performance analysis proves that the proposed method is more reliable that reduces the computational delay and has a better performance in the delay-sensitive network as compared to the existing schemes. The experimental results are obtained by implementing the proposed scheme on a desktop-based configuration as well as Raspberry Pi 4.

Driver Stress and Behavior prediction is a significant feature of the Advanced Driver Assistance System. This system can improve driving safety by alerting the driver to the danger of unsafe or risky driving conditions. In this paper, we analyzed historical trip data to calculate the driving stress and its impact on different driving behavior. We used Long Short-Term Memory Fully Convolutional Network to predict the corresponding stress level of the driver. We further established a relationship between stress and driving behavior and developed an intelligent recommendation system for cab companies to recommend the driver for a subsequent trip. To meet the demand for Artificial Intelligence in the Intelligent Transportation System, we leverage Federated Learning in Vehicular Edge Computing in the proposed system architecture. It enables Road Side Units to do all computing of data on it. The model has been tested on the UAH-DriveSet dataset. We observed that the proposed model predicts the stress with an accuracy of 95% and assists in enhancing the driving quality and experience.

The source-based routing using quality of service (QoS) metrics results in alternate path discovery considering link-availability time, link costs, and path delays to overcome the barrier of information blocking on a selected path by choosing alternative routes. However, multipath selection, as well as the cost of selecting a path, make the route selection a challenging task. This paper proposes a vehicle orientation based QoS routing in vehicular ad-hoc networks (VANETs), called OBQR that exploits vehicle orientational information instead of magnitude information. The cosine similarity concept with a preliminary scalarization model converts the multi-constraint objectives into a single constraint objective to find a set of possible paths to the destination. We evaluate the performance of our approach and compare it with existing state-of-the-art schemes based on QoS routing and clustering. Experimental results demonstrate that the proposed scheme significantly improves path selection and load balancing with better QoS routing performance.

In recent years, integration of clustering architecture with software-defined networking (SDN) has emerged as is the crucial enabler for next-generation intelligent transportation services (ITS). This paper proposes a spectral clustering technique along with the deep deterministic policy gradient (DDPG) algorithm using hybrid SDN architecture, called SeScR to enhance cluster stability and route selection method. The spectral clustering is used to overcome the arbitrary node distribution of vehicular ad-hoc networks (VANETs) and provide a flexible clustering using eigenvalues of graph laplacian. Moreover, the DDPG algorithm addresses the continuous address space of VANETs and provides an actor-critic architecture for optimal routing decisions. The experimental results demonstrate that the proposed scheme improves path selection and load balancing with better performance in terms of low average transmission delay up to 15%, throughput up to 18-22%, and low computation overhead 10% compared to the existing state-of-the-art protocols used in this research.

In the Internet of Things (IoT), devices can interconnect and communicate autonomously, which requires devices to authenticate each other to exchange meaningful information. Otherwise, these things become vulnerable to various attacks. The conventional security protocols are not suitable for IoT applications due to the high computation and storage demand. Therefore, we proposed a blockchain-enabled secure storage and communication scheme for IoT applications, called BSS. The scheme ensures identification, authentication, and data integrity. Our scheme uses the security advantages of blockchain and helps to create safe zones (trust batch) where authenticated objects interconnect securely and do communication. A secure and robust trust mechanism is employed to build these batches, where each device has to authenticate itself before joining the trust batch. The obtained results satisfy the IoT security requirements with 60% reduced computation, storage and communication cost compared with state-of-the-art schemes. BSS also withstands various cyberattacks such as impersonation, message replay, man-in-the-middle, and botnet attacks.

With the rapid technological advancement of the universal internet of vehicles (UIoV), it becomes crucial to ensure safe and secure communication over the network, in an effort to achieve the implementation objective of UIoV effectively. A UIoV is characterized by highly dynamic topology, scalability, and thus vulnerable to various types of security and privacy attacks (i.e., replay attack, impersonation attack, man-in-middle attack, non-repudiation, and modification). Since the components of UIoV are constrained by numerous factors (e.g., low memory devices, low power), which makes UIoV highly susceptible. Therefore, existing schemes to address the privacy and security facets of UIoV exhibit an enormous scope of improvement in terms of time complexity and efficiency. This paper presents a lightweight authentication and batch verification scheme (LABVS) for UIoV using a bilinear map and cryptographic operations (i.e., one-way hash function, concatenation, XOR) to minimize the rate of message loss occurred due to delay in response time as in single message verification scheme. Subsequently, the scheme results in a high level of security and privacy. Moreover, the performance analysis substantiates that LABVS minimizes the computational delay and has better performance in the delay-sensitive network in terms of security and privacy as compared to the existing schemes.

Smart application has been focused more to solve real-world problems like smart home, smart street lighting system etc., but the concept of data transmission between devices require precise attention to preserve security with better performance output in device communication. The blockchain is a decentralized computation and information sharing platform to enable different players to verify on the fly. This paper presents a hyper-ledger fabric-based blockchain architecture using a one-way hash function to employ distributed data storage, to enhance the security of the smart applications, and to improve performance in the computation. The proposed protocol resists to different attacks, i.e., modification, impersonation, replay, man-in-the-middle, stolen verifies, device injection, false reputation, and appending. Moreover, performance results are comparatively better rather than relevant vehicular communication protocols.

The innovations in wireless communication technologies, cloud computing, connected vehicles, autonomous vehicles, the Internet of everything, etc. opened a way for the creation, development, advancement, and establishment of vehicular networks for emerging smart city applications. Among them, automated and connected vehicle technologies are among the most researched topics. The currently available concepts are only a fraction of what is being developed for the future. In connected vehicles, the Internet of Vehicles (IoVs) is a promising concept where cars or vehicles can use various communication technologies to communicate with the driver or other entities on the road. In this paper, we explore the future entities in a smart city to which a vehicle can communicate. Moreover, we investigate the importance of warning messages for reducing road transportation issues as IoVs already proved that it could reduce worldwide traffic issues, accident rates, transportation issues, etc. to a particular extent. In spite of several attractive features, ensuring security and lightweight property is one of the challenges in an IoV scenario. Therefore, we propose a Secure Authentication for Future Entities (SAFE) protocol, which uses cryptographic operations for enhancing security and preserving privacy.

In the fast-growing world, a significant increase in the number of vehicles has opened an opportunity to transform vehicles from a mechanical device to an intelligent system. The vehicular ad-hoc network (VANET) is designed to exchange messages with nearby devices. However, it is tough to transmit information from a sender to a receiver in a public environment due to malicious activities (by an adversary), wireless connection, mobility, and high-cost requirement. In this paper, we propose a state-of-the-art protocol, names as “VehicleChain” for smart transportation to ensure secure and cost-effective vehicle-to-vehicle and vehicle-to-infrastructure communications. The VehicleChain combines the robustness of a blockchain with elliptic-curve cryptography (ECC) to improve the system security level without increasing the computational cost. The VehicleChain is protected against different attacks, i.e., insider, server spoofing, modification, man-in-the-middle, plaintext, replay, and impersonation. Further, the proposed scheme comparatively achieves better results for different performance measures.

In the near future, one of the promising concepts which are going to change undoubtedly the whole automobile industry, vehicular networking, software industry, IT manufacturing industry, Industry, transportation sector, service provider, etc. is the Internet of Vehicles (IoV). It can have the capability to boost innovation in smart city applications(like intelligent transportation, smart health-care, smart environment etc). The IoV is a dynamic network system which allows data sharing between different entities like vehicles, their surrounding RSUs (Roadside Units), infrastructures, sensors, mobile devices, clouds, etc. It opened new opportunities in different services and applications to enhance road conditions, driver/passenger safety, transportation issues, non-safety applications, etc. However, ensuring Efficiency and security is one of the main challenges in an IoV scenario. In this paper, we introduce the new effective and efficient architecture for the next generation vehicular technologies for identify the inappropriateness of most existing work, and address the challenging issues of secure and efficient packet forwarding and energy efficient by proposing new effective and efficient scheme for VANETs using Internet of Vehicles and Mobile Femto Access Points.

The Worldwide road traffic problems, accident rates, transportation issues are increasing despite the improvements in Intelligent Transportation Systems (ITS). Internet of Vehicles (IoV) is a promising paradigm to the better and safe future of the automotive field. This complex, dynamic mobile network system allows data sharing between vehicles, its surrounding portable devices, road-side units, infrastructures, sensors, cloud, etc. However, the IoV provides several benefits and opportunities in different applications, it introduces challenges in security. In this paper, we propose an efficient authentication system for secure Vehicle-to- Vehicle (V2V) communications in an IoV scenario. The security analysis reveals that our scheme can defend against strong attacks. Our experimental outcomes on a simulated environment (on a desktop computer and Raspberry Pi) reveal that our proposed scheme can work with less computation time while ensuring integrity of the message in a strong adversarial scenario.

With the advancement in networking technologies and emerging applications that work on different protocols, the basic usage of networks, i.e., transferring by packets and global connectivity has to be maintained. The Delay Tolerant Networks (DTNs) are characterized by high end-to-end latency, frequent disconnection, and opportunistic communication over unreliable wireless links. In this paper, we designed a dynamic trust management protocol for secure routing optimization in Delay Tolerant Network(DTN) environments in the presence of well-behaved, selfish and malicious nodes. We develop a novel model-based methodology for delay tolerant networks for extreme distances and trust based techniques to ensure secure routing.

The social network being one of the most disruptive innovations of the last decade has gathered a huge amount of attention of the people. The posts of the users of the social media are used by many companies in the world to find the mentality of the users, the current trend of the market and many more things. But still, there is a latent potential in the social network. One of the aspect that we were able to discover was about finding the relationship between the users (i.e., especially, the negative link) on the social network using the posts that the users make and the reaction of the other users towards it. The prediction of the negative link can be applied in the cyber security field, to observe the aberrations in the network and further find the malicious nodes in the social network; say, if two nodes are doing things together even though there is no relation between them. It can also be used in improving the recommendation system in social media as if there is some probability between the two nodes of being the enemy or disliking each other then we can remove them from each other's recommendation list or could assign a lower weight to them in our recommendation algorithm. To achieve all this relationship between the nodes we first need to find whether the user is posting posts with positive emotion (like happy, excited, etc.) or negative emotion (like angry, sad, etc.) so that we can further analyze the mentality of the user and use it to recommend the people who we have previously classified with the similar personality. For that, we have used the sentiment analysis, which divides the users into five simple categories: Extremely +ve(i.e.,positive), +ve, Neutral, -ve (i.e.,negative) and Extremely -ve. This research paper explains the methodologies that we have used to achieve the prediction of negative links between the nodes in the social network.