In VANET, although data sharing among vehicles improves safety and traffic, malicious data providers might share false information to compromise its effectiveness. One typical solution to address this issue is to manage the trustworthiness of data providers by using feedback received from data users quantifying the veracity of the shared data. However, data users might behave maliciously and share untruthful feedback. This article introduces TrCoin, a blockchain-based trust management system, for VANET which tackles both fake data and untruthful feedback. TrCoin uses an honesty value calculation algorithm to distinguish honest from malicious data users and filters out the feedback shared by malicious data users. TrCoin then runs a trust value calculation algorithm to compute the trust values of data providers based on the truthful feedback shared by honest data users. Extensive simulations are conducted to demonstrate the effectiveness of TrCoin under various attack scenarios with false data injection and untruthful feedback.

NLIP or Natural Language Interaction Protocol is being defined by a group of researchers that enables a universal, standards-based application-level protocol to work across AI Enabled Services. NLIP leverages the capabilities of large language models to transform unstructured natural language to a structured representation at the endpoints, replacing multiple individual application protocols with a single one. The design of such a protocol must necessarily include security considerations, paying significant attention to protocol integrity, privacy, data governance and cybersecurity defenses. In this paper we discuss the approaches we have introduced to maintain these security elements of the protocol. The security of the protocol requires not only taking into consideration the needs of communication flow on the wire, but also to handle the security requirements of the endpoints. This requires appropriate support for functions like authentication and authorization, where some of these services can be provided by a third-party service provider. Furthermore, many existing security protocols and paradigms are already supported by existing software services which NLIP may utilize which we need to be able to leverage them at server endpoints. An application-level protocol needs to leverage existing services while still ensuring adequate security at the application level. We discuss the challenges in designing security for an application-level protocol like NLIP and discuss how we have addressed these problems to ensure a secure implementation of NLIP.

The advent of large language models (LLMs) has made an interactive natural language interaction feasible between machines in a manner that did not exist before. An implication is that a natural language interface can replace many mobile applications that are used today. Just like the advent of the browser in 1990s simplified technology by replacing a plethora of client-side applications with a single standard application, a common natural language interaction protocol can potentially replace the plethora of mobile applications that exists today, providing a universal application layer protocol. Convergence to a universal application layer protocol would bring significant benefits to all segments of society - consumers can use a single application for various interactions, businesses will have a simpler maintenance burden for their IT infrastructure, and integration among different businesses can be streamlined. A set of researchers from several different universities and industries have come together to define this protocol, and address the research challenges involved in defining and using it. This protocol must support authentication, secure communication, multi-modal interaction, exchange privacy and trustwor- thiness policies, among others. This protocol must be open-source and developed in a community environment, along with freely available implementations. The initial specification of the protocol is being built using a REST interface over HTTPS using JSON messages for interactions. In this paper, we will provide the motivation, requirements, use-cases, an initial specification and the current state of NLIP.

This paper presents an Augmented Reality (AR)-infused firewall game - AugmentWall, designed to enhance middle school cybersecurity education through interactive gameplay. Developed using a participatory design approach, the game underwent iterative refinements and was playtested with 37 students across two schools to assess engagement, comprehension, and perceived educational value. This study examines how pre-game motivation influences engagement and how students perceive the game’s clarity and effectiveness in teaching firewall concepts. Findings indicate that intrinsic motivation significantly impacted engagement, with highly motivated students demonstrating greater sustained participation. The game effectively reinforced firewall rule comprehension, supported by structured decision monitoring. However, prior cybersecurity knowledge was inversely related to self-reported learning gains (r = -0.34, p = 0.106), suggesting that novice learners benefited most, while experienced students may require adaptive challenges. These results highlight the potential of AR-based cybersecurity education to enhance engagement and learning while emphasizing the need for scalable difficulty adjustments to accommodate diverse learners. Future iterations should explore personalized learning pathways and extended gameplay features to optimize long-term impact.

Emotions are an integral part of being human, and experiencing a range of emotions is what makes life rich and vibrant. From basic emotions like anger, fear, happiness, and sadness to more complex ones like excitement and grief, emotions help us express ourselves and connect with the world around us. In recent years, researchers have begun adopting virtual reality (VR) technology to evoke emotions as realistically as possible and quantify the strength of emotions from the electroencephalogram (EEG) signals measured from the brain to understand human emotions in realistic situations better. This is achieved by creating a sense of presence in the virtual environment, the feeling that the user is there. For instance, [6] studied the excitement of a rollercoaster ride in VR, and [5] studied the fear of navigating in a VR cave.

Low-latency traffic prediction is vital for smart city traffic management. Federated Learning has emerged as a promising technique for Traffic Prediction (FLTP), offering several advantages such as privacy preservation, reduced communication overhead, improved prediction accuracy, and enhanced adaptability to changing traffic conditions. However, majority of the current FLTP frameworks lack a real-time model updating scheme, which hinders their ability to continuously incorporate new incoming traffic data and adapt effectively to the changing dynamics of traffic trends. Another concern with the existing FLTP frameworks is their reliance on the conventional FL model aggregation method, which involves assigning an identical model (i.e., the global model) to all traffic monitoring devices to predict their individual local traffic trends, thereby neglecting the non-IID characteristics of traffic data collected in different locations. Building upon these findings and harnessing insights from reinforcement learning, we propose NeighborFL, an individualized real-time federated learning scheme that introduces a haversine distance-based and error-driven, personalized local models grouping heuristic from the perspective of each individual traffic node. This approach allows NeighborFL to create location-aware and tailored prediction models for each client while fostering collaborative learning. Simulations demonstrate the effectiveness of NeighborFL, offering improved real-time prediction accuracy over three baseline models, with one experimental setting showing a 16.9

The Metaverse, an immersive virtual world, has emerged as a shared space where people engage in various activities ranging from social interactions to commerce. Cryptocurrencies [3] and Non-Fungible Tokens (NFTs) [6] play pivotal roles within this virtual realm, reshaping interactions and transactions. Cryptocurrencies, utilizing cryptographic techniques for security, enable decentralized and secure transactions, and NFTs represent ownership or proof of authenticity of unique digital assets through the blockchain technology. While NFTs and cryptocurrencies offer innovative opportunities for ownership, trading, and monetization within the metaverse, their use also introduces potential risks and negative consequences, such as financial scams and fraud, highlighting the need for users to exercise caution and diligence in their virtual transactions.

Autonomous vehicles (AVs) present significant potential and challenges within transportation systems, necessitating sophisticated computational processes for perceiving environments, decision-making, and control. These processes, organized into AV pipelines, must operate under strict, dynamically varying deadlines to ensure safety and efficiency. Traditional software systems, such as those based on the Robot Operating System (ROS), struggle to manage these dynamic deadlines, often leading to either compromised performance or under-utilization of resources. The Dynamic Deadline-Driven (D3) Execution Model was developed to address these issues by centralizing deadline management and enabling AV systems to adapt computation in real-time based on environmental feedback. However, the D3 model lacks mechanisms for accurately predicting deadlines across diverse scenarios and handling extremely short response times required in emergencies. To address these issues, this paper integrates deep learning models within the D3 framework, enhancing its ability to set precise deadlines and respond effectively in critical situations. By deploying two deep neural network (DNN) models across different modules of the AV pipeline, the augmented system significantly improves safety, predictive accuracy, and latency minimization. Experimental analysis in the CARLA simulation environment demonstrates that the deep learning-enhanced D3 model outperforms traditional approaches, reducing collision rates and improving system responsiveness in long-distance driving and complex urban scenarios.

Spurred by technological advancements and consumer demands, the automotive industry is moving towards software-defined vehicles. To accommodate the complexities of software that execute on a system of networked ECUs (Electronic Control Units) and sensors, service-oriented architecture (SOA) becomes a viable architecture. SOME/IP (Scalable Service-Oriented MiddlewarE over IP) is a middleware standardized by AUTOSAR that implements SOA for automotive systems. By leveraging the unique features of the QUIC (Quick UDP Internet Connections) transport protocol, we design "Quick" SOME/IP by substituting the TCP used in SOME/IP with QUIC. In addition to supporting the four communications patterns offered by SOME/IP, Quick SOME/IP introduces fault tolerance streaming as the fifth communication pattern, which facilitates simultaneous data transmissions from one sender to one receiver over two physically disjoint paths to tolerate the failure of one path. Using the Nexus open-source C++ library implementation of QUIC, we modified vsomeip, an open-source C++ implementation of SOME/IP using TCP, to implement qsomeip. Our experiments demonstrated that qsomeip outperforms vsomeip in terms of reduced connection latency and enhanced security with the new capability of fault tolerant streaming.

Integrating mixed reality (MR) with artificial intelligence (AI) technologies, including vision, language, audio, reasoning, and planning, enables the AI-powered MR assistant [1] to substantially elevate human efficiency. This enhancement comes from situational awareness, quick access to essential information, and support in learning new skills in the right context throughout everyday tasks. This blend transforms interactions with both the virtual and physical environments, catering to a range of skill levels and personal preferences. For instance, computer vision enables the understanding of the user's environment, allowing for the provision of timely and relevant digital overlays in MR systems. At the same time, language models enhance comprehension of contextual information and support voice-activated dialogue to answer user questions. However, as AI-driven MR systems advance, they also unveil new vulnerabilities, posing a threat to user safety by potentially exposing them to grave dangers [5, 6].

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients’ anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA’s signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios’ requirements while safeguarding EHR and is robust against unauthorized retrievals.

This tutorial paper analyzes the traffic characteristics of immersive experiences with extended reality (XR) technologies, including Augmented reality (AR), virtual reality (VR), and mixed reality (MR). The current trend in XR applications is to offload the computation and rendering to an external server and use wireless communications between the XR head-mounted display (HMD) and the access points. This paradigm becomes essential owing to (1) its high flexibility (in terms of user mobility) compared to remote rendering through a wired connection, and (2) the high computing power available on the server compared to local rendering (on HMD). The requirements to facilitate a pleasant XR experience are analyzed in three aspects: capacity (throughput), latency, and reliability. For capacity, two VR experiences are analyzed: a human eye-like experience and an experience with the Oculus Quest 2 HMD. For latency, the key components of the motion-to-photon (MTP) delay are discussed. For reliability, the maximum packet loss rate (or the minimum packet delivery rate) is studied for different XR scenarios. Specifically, the paper reviews optimization techniques that were proposed to reduce the latency, conserve the bandwidth, extend the scalability, and/or increase the reliability to satisfy the stringent requirements of the emerging XR applications.

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

With the increasing demand for advanced autonomous driving, the available communication resources may become constrained over different geographic areas. In addition, due to dynamic channel variations and imperfect cell deployments, guaranteeing the required communication resources for data hungry and delay-sensitive applications in autonomous vehicles (AVs), along their entire trips, becomes challenging. To address these issues, the paper investigates the feasibility of a hybrid system-optimum and user-equilibrium AV traffic framework subject to communication constraints, as well as its performance gain. Within such a framework, the paper introduces the problems of communication-constrained routing (CCR) and traffic control (CCTC) in the context of infrastructure-assisted autonomous driving and presents respective solutions. For CCR, an efficient two-layered routing scheme is proposed which can provide optimal trip duration. Simulation results show that the routing scheme achieves a good balance between longer duration of communication coverage and acceptable source-to-destination travel time. For CCTC, it is shown that there exists an optimal AV speed on each road segment, as well as an optimal inter-AV distance and an optimal number of AVs in each cell, to maximize the road-network AV throughput within a single cell. Moreover, spectrum allocation is used to achieve Pareto-optimal road-network throughput across cells, and a new key performance index (KPI) is defined to evaluate the traffic control capability of cellular systems. Simulation results validate the improvement of AV throughput via the proposed CCTC solution.

Reliability and determinism are crucial to meet the requirements of real-time applications. The IEEE Time-Sensitive Networking (TSN) Task Group has introduced standards to facilitate the stringent timing requirements of TSN applications, which include IEEE 802.1Qbv (scheduling and traffic shaping) that helps enforce the computed transmission schedule. However, designing appropriate scheduling algorithms for TSN is left open. Owing to its high flexibility, many efforts have taken advantage of the centralized controller of TSN to build incremental end-to-end scheduling algorithms. Although these algorithms increase flexibility, they pose certain scheduling threats that could cause performance degradation or severe damage in certain real-time scenarios. In this paper, we discuss the threats of Denial of Scheduling and Blindness of Scheduler and evaluate their impacts based on the factors of attack intensity, attack start time, and attack strategy. Then, we propose a reactive solution to mitigate the effects of these threats.

Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facili-tating privacy-preserving and decentralized real-time traffic flow prediction.

<p>In VANET, although data sharing among vehicles improves safety and traffic, malicious data providers might share false information to compromise its effectiveness. One typical solution to address this issue is to manage the trustworthiness of data providers by using feedback received from data users quantifying the veracity of the shared data. However, data users might behave maliciously and share untruthful feedback. This paper introduces TrCoin, a blockchain-based trust management system, for VANET which tackles both fake data and untruthful feedback. TrCoin uses an honesty value calculation algorithm to distinguish honest from malicious data users and filters out the feedback shared by the malicious data users. TrCoin then runs a trust value calculation algorithm to compute the trust values of data providers based on the truthful feedback shared by honest data users. Extensive simulations are conducted to demonstrate the effectiveness of TrCoin under various attack scenarios with false data injection and untruthful feedback.</p>

Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.

With the rising of remote work and schooling, the adaption of emerging technologies to teach the concepts of cybersecurity becomes critical. In this work, we present the concept, design, and prototype of a Mixed Reality-based cybersecurity education application on phishing, so that school children could be exposed to the subject remotely and practice to differentiate malicious from genuine messages.