Traffic State Estimation is an important function of Intelligent Transportation Systems. Not surprisingly, multiple methodologies have been suggested by researchers to estimate or infer traffic parameters such as flow, density, and space-mean speed. Of particular interest in this work are methods involving moving observers (MO) which utilize vehicles in traffic to estimate traffic state parameters based on mobility properties of the vehicles encountered.The main contribution of this work is to devise an MO–based protocol to estimate Edie’s generalized density using only vehicle to vehicle communications. To the best of our knowledge, ours is the first MO-based protocol to estimate Edie’s generalized density reported in the literature.We have verified our protocol empirically by using simulated datasets generated from SUMO. Our simulations test the method using flows of 1000, 2000, and 3000 vehicles per hour and various percentages of enabled mobile observers. Our findings show that even at only 5% penetration rate and with flows of 2000 vehicles per hour, the mean relative difference is just over 10%.

Smart mobility increasingly relies on real-time analytics, cooperative perception, and latency-sensitive computation near the network edge. Vehicular cloud computing is attractive because it can exploit the unused computational resources of vehicles dispersed across a city. However, effective utilization is challenged by mobility-driven churn, intermittent wireless links, and the lack of coordination infrastructure that can reliably support bidding, input delivery, monitoring, and output collection at scale. This paper proposes an integrated framework that couples (i) rolling-horizon placement of mobile roadside units (mRSUs) with (ii) a truthful, reliability-aware reverse auction for vehicular task offloading. The city contains many stationary RSUs that provide baseline coverage, while a smaller fleet of mRSUs dynamically repositions to demand hotspots and coverage gaps. We formulate mRSU placement as a rolling-horizon mixed-integer optimization that maximizes incremental coordination coverage while minimizing mobility cost and fairness penalties, with redundancy constraints for resilience. On top of this hybrid RSU layer, a multi-attribute sealed-bid reverse auction allocates stochastic tasks to heterogeneous vehicles while accounting for readiness, departure uncertainty, compute reliability, and link success probability. The auction mechanism is shown to satisfy individual rationality, incentive compatibility under fixed admissibility, and budget balance, while deliberately trading allocation efficiency for deadline-robust task completion. A preemptive migration and re-auction policy further mitigates failures due to mobility and link degradation. Simulation results demonstrate improved coverage efficiency, reduced fairness penalty accumulation, and stronger coordination robustness compared to static and reactive baselines.

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO2 emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously absent in the literature. We identify optimal powertrain efficiency values that are directly derived from publicly available vehicle specifications, ensuring transparency and accessibility. Our findings demonstrate that this simple, physics-based model accurately estimates fuel consumption and CO2 emissions for standard EPA cycles and can be effectively generalized to user-defined scenarios. This establishes a computationally efficient, interpretable, and robust method for environmental impact assessment, policy evaluation, and real-time emissions estimation.

As vehicles evolve into mobile computers, future transportation systems will depend on secure data sharing and collaborative computation between cars and roadside infrastructure. Ensuring security, privacy, and accountability in such dynamic networks is challenging because vehicles continuously join, leave, and relay data under intermittent connectivity. This paper introduces VERA-VANET (Verifiable Encrypted Routing and Attestation), a cryptographic protocol that makes vehicular communication both secure and mathematically verifiable. When an Access Point (AP) disseminates encrypted job chunks, each vehicle processes only the data it is authorized to handle. Every packet is signed and acknowledged, producing compact, aggregatable proofs of delivery verifiable by the AP or cloud. For correctness, vehicles attach lightweight zero-knowledge proofs that confirm computations without revealing data. Under disconnections, vehicles safely store, carry, forward, and trace encrypted chunks through cryptographic receipts, making forgery and undetected tampering infeasible. VERA-VANET combines asymmetric encryption, aggregate signatures, and zero-knowledge attestations, and integrates with IEEE 802.11p / C-V2X using trusted hardware for secure key storage.

Large Language Models (LLMs) are increasingly being adopted in sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. To address these shortcomings, we propose BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into Qwen 2.5 LLM workflow. Here, blockchain not only anchors the authenticity of retrieved documents, but also enforces dynamic, tamper-proof access and authorization policies and preserves transparent, immutable records of model interactions. This decentralized infrastructure ensures that the LLM operates on verified inputs while maintaining privacy and compliance with regulatory standards. A layered security module, combined with reinforcement learning, is further tailored to detect privacy risks and to mitigate hallucinations in real time. A prototype implementation, with simulated healthcare data, achieved an $86.25 \%$ privacy preservation rate and an $88.33 \%$ hallucination mitigation rate, significantly outperforming conventional LLM deployments. These results demonstrate the potential of combining blockchain functionality with LLM, resulting in robust, secure, transparent, and trustworthy AI systems.

Blockchain technologies have revolutionized data storage by ensuring immutability, decentralized control, and enhanced system availability. However, current blockchain frameworks often fall short in providing sufficient privacy and control to data owners. As privacy concerns grow, highlighted by regulations such as the GDPR’s ‘Right to be Forgotten’, there is an increasing demand for blockchain architectures that grant data owners greater control while allowing flexible and efficient participation of nodes as miners. In this paper, we introduce MerkleChain, an innovative redactable ledger architecture designed to address these challenges. Built upon Merkle trees and chameleon hashes, our system ensures tamper-evidence for individual data owners and preserves privacy. The architecture allows miners to store only the Merkle trees and chameleon hashes of data they subscribe to, enhancing both data privacy and system efficiency. In addition, it supports parallel processing of individual owner-controlled Merkle trees, leading to increased throughput. MerkleChain is adaptable to a range of applications, and we demonstrate its potential in a healthcare setting.

With the increase in the popularity of online marketplaces, the relevance of customer reviews to business success has also increased. This is especially important in decentralized markets with no central controlling authority. Current online review systems are plagued by several problems, including lack of incentives for reviews, subjective reviews, and collusion between buyers and sellers to unduly influence seller reputation. In this paper, we propose methods to detect sybil attacks on marketplace reputation systems. Since sybil attacks are very complex to detect, especially in decentralized systems, we suggest a collection of metrics to detect the sybils, provide a new weighted system for potential sybils, and illustrate their efficacy using example scenarios. For three chosen threat models, we generate synthetic data. We also employ 26 machine learning classifiers that are trained and tested on the synthetic data. The initial results are quite encouraging. We conclude that a combination of techniques are necessary to detect complex sybil attacks on marketplaces.

With the increase in the use of sensors and drones to monitor soil and crop conditions in vast agricultural lands as well as for situational-monitoring of natural disasters such as forest fires and increasing sea levels, there is a need for systems that can support long-term monitoring with minimal cost. In this paper, we propose a novel architecture that combines sensor, drone, blockchain, and machine learning technologies to support a long-term monitoring system for vast areas of land. Long-term monitoring is achieved by employing multigenerational sensors that have been deployed at the start but are woken up in stages as and when needed to replace or reinforce existing sensors. A swarm of drones periodically scans the landscape to identify areas that need immediate attention (e.g., dry soil, pests or insects, humidity, temperature, etc.). Drones store and process the data using onboard units and make real-time decisions regarding changing path plans or changing scanning frequencies. Optionally, data is sent to the base station that fuses the information from multiple drones in the swarm, and inputs the fused data to a machine-learning model that recommends the needed actions. The actions may include obtaining additional data, awakening more sensors, actions to handle any situation by signaling for external actions such as cloud seeding, controlled chemical spraying from a drone, etc., in agricultural applications. Using simulated scenarios, we have employed three prediction algorithms—the Prophet model, CNN, and LSTM—to determine the effectiveness of these algorithms in predicting sensors values in the simulated scenarios. The proposed architecture is scalable, supports long-term monitoring, and is effective in predicting future conditions to enable preemptive actions.

Blockchain technologies and the associated smart contracts are found to be suitable for building reputation management systems in decentralized marketplaces. In this paper, we provide a brief overview of RaaS, a framework to manage Reputation as a Service in decentralized marketplaces. RaaS framework has blockchains and smart contracts as its foundation to guarantee transparency and immutability of reviews. In particular, we address the performance issues faced by such blockchain-based reputation systems and propose two schemes that address the performance challenges. We provide analytical performance predictions and verify, by extensive simulations, the accuracy of our analytical predictions. Our schemes are shown to result in more efficient query execution with enhanced block structure schemes, with an improvement of up to 81% in query execution responses when compared to earlier work.

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our second contribution is to extend Laplace’s Law of Succession in a way that provides a trust measure in a seller’s future performance in terms of their past reputation scores. Our third main contribution is to illustrate three applications of the proposed trust and reputation service. Here, we begin by discussing an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation score to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance due, say, to overcoming an initial learning curve. We provide a discounting scheme where older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed the accuracy of our analytical predictions.

In this paper, we investigate mechanisms to provide information-based support for successful surveillance and recon-naissance missions, especially in the context of a coalition of autonomous organizations deploying UAVs. Our goal is to identify and address technical challenges in providing such informational support. To this end, as an illustration, we provide a description of a rescue and search mission scenario involving a swarm of UAVs and identify the challenges in carrying out such a coordinated mission. We then formalize five key challenges and propose solutions for the same. An infrastructure, including blockchains, smart contracts, satellite communication, and ground controllers, is proposed. The relevance of keeping track of any rogue UAVs is emphasized and a possible reputation system applicable to either individual UAVs or the coalition members is identified. To improve scalability and enhance fault tolerance, four types of configurations of the UAVs in a swarm are discussed. Finally, the use of machine learning tools to enhance mission success in a swarm environment is also discussed.

In the realm of vehicular cloud computing, the Flexible Time Datacenter (FTDC) addresses the dynamic and unpredictable nature of vehicle availability and computational resource allocation. This paper presents the FTDC framework, which leverages a truthful reverse auction mechanism for efficient and strategic resource allocation between vehicles and tasks. The framework ensures that vehicles with computational resources are effectively matched with tasks based on their specific requirements. A key component of this framework is its robust task mitigation strategy, which addresses challenges arising from unexpected vehicle departures or task interruptions. This strategy involves dynamic task reallocation, where tasks are promptly reassigned to alternative vehicles or re-auctioned to ensure deadlines are met. The system incorporates penalties for premature departures and incentives for reliable behavior to maintain system integrity and efficiency. The FTDC framework is evaluated through a series of simulations that demonstrate its effectiveness in managing dynamic task allocation, resource utilization, and system reliability. The results highlight the framework's ability to adapt to unpredictable conditions while maintaining high performance and minimizing task delays.

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we identify the challenges that must be overcome and propose possible technical solutions for them. We also derive analytical expressions for the success probability of an autonomous UAV mission and describe a machine-learning model to train the UAV.

Accommodating pedestrians crossing midblock has been shown to have harmful environmental consequences because of increased fuel consumption and CO2 emissions. Somewhat surprisingly, no studies were devoted to mitigating the environmental impact of midblock crossing. Our main contribution is to propose schemes that mitigate the increased fuel consumption and CO2 emissions due to pedestrian midblock crossing by leveraging information about the location and expected duration of the crossing. This information is shared in a timely manner with approaching cars. We evaluated the impact of car decisions on fuel consumption and emissions by exploring potential trajectories that cars may take as a result of messages received. Our extensive simulations showed that timely dissemination of pedestrian crossing information to approaching vehicles can reduce fuel consumption and emissions by up to 16.7

The moving observer method, proposed almost 70 years ago, was found to yield traffic parameter estimates that are, in terms of accuracy, very similar to the estimates obtained by using the classical stationary observer method. Over the years, the moving observer method was validated either experimentally or else statistically by showing that the traffic estimates it produces are statistically indistinguishable from those produced by the stationary observer method. However, to the best of our knowledge, the moving observer method was not justified theoretically. The main contribution of this work is to offer the first theoretical justification of the moving observer method.

Recent statistics reveal an alarming increase in accidents involving pedestrians (especially children) crossing the street. A common philosophy of existing pedestrian detection approaches is that this task should be undertaken by the moving cars 1 themselves. In sharp departure from this philosophy, we propose to enlist the help of cars parked along the sidewalk to detect and protect crossing pedestrians. In support of this goal, we propose ADOPT: a system for Alerting Drivers to Occluded Pedestrian Traffic. ADOPT lays the theoretical foundations of a system that uses parked cars to: (1) detect the presence of a group of crossing pedestrians – a crossing cohort; (2) predict the time the last member of the cohort takes to clear the street; (3) send alert messages to those approaching cars that may reach the crossing area while pedestrians are still in the street; and, (4) show how approaching cars can adjust their speed, given several simultaneous crossing locations. Importantly, in ADOPT all communications occur over very short distances and at very low power. Our extensive simulations using SUMO-generated pedestrian and car traffic have shown the effectiveness of ADOPT in detecting and protecting crossing pedestrians. • Detecting the presence of a group of crossing pedestrians – a crossing cohort. • Predicting the time the last member of the cohort takes to clear the street. • Sending alert messages to those approaching cars that may reach the crossing area while pedestrians are still in the street. • Showing how approaching cars can adjust their speed, given several simultaneous crossing locations.

Motivated by the success of conventional cloud computing, vehicular clouds were introduced as a group of vehicles whose corporate computing, sensing, communication and physical resources can be coordinated and dynamically allocated to authorized users. One of the attributes that set vehicular clouds apart from conventional clouds is resource volatility. As vehicles enter and leave the cloud, new compute resources become available while others depart, creating a volatile environment where the task of reasoning about fundamental performance metrics becomes very challenging. Just as in conventional clouds, job completion time ranks high among the fundamental quantitative performance figures of merit. With this in mind, the main contribution of this work is to offer easy-to-compute approximations of job completion time in a dynamic vehicular cloud model involving vehicles on a highway. We assume estimates of the first moment of the time it takes the job to execute without any overhead attributable to the working of the vehicular cloud. A comprehensive set of simulations have shown that our approximations are very accurate.