Recently, Blockchain-based applications have become immensely popular because of limited reliance on a single entity, unlike a centralized system. However, reaching a consensus among blockchain networks is a challenging and vital aspect of blockchain-based applications. There are various types of blockchain networks for different kinds of application scenarios. Among all of them, the consensus algorithm is the most crucial part of reaching an agreement in the complex blockchain network. Over the years, researchers have focused on dealing with the challenges like distributed computing, storage, transaction speed, security, validity, interoperability, and many more. However, only some of them are appropriate for all domains. Therefore, this paper presents an extensive study of different types of consensus protocols used in existing blockchain solutions with the strength and limitations of each algorithm. We also provide an inherent comparison among different algorithms to understand consensus protocol selection better. Moreover, we investigate operational and interoperability issues in existing blockchain-based applications to understand challenges and provide recommendations for future developers.

Blockchain technology is emerging as a pivotal framework to enhance the security of internet-based systems, especially as advancements in machine learning (ML), artificial intelligence (AI), and cyber–physical systems such as smart grids and IoT applications in healthcare continue to accelerate. Although these innovations promise significant improvements, security remains a critical challenge. Blockchain offers a secure foundation for integrating diverse technologies; however, vulnerabilities—including adversarial exploits—can undermine performance and compromise application reliability. To address these risks effectively, it is essential to comprehensively analyze the vulnerability landscape of blockchain systems. This paper contributes in two key ways. First, it presents a unique layer-based framework for analyzing and illustrating security attacks within blockchain architectures. Second, it introduces a novel taxonomy that classifies existing research on blockchain vulnerability detection. Our analysis reveals that while ML and deep learning offer promising approaches for detecting vulnerabilities, their effectiveness often depends on access to extensive and high-quality datasets. Additionally, the layer-based framework demonstrates that vulnerabilities span all layers of a blockchain system, with attacks frequently targeting the consensus process, network integrity, and smart contract code. Overall, this paper provides a comprehensive overview of blockchain security threats and detection methods, emphasizing the need for a multifaceted approach to safeguard these evolving systems.

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T* ≥ 1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this article, we first propose a scalable data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee and compare with the state-of-the-art ML methods to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T^*\geq1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T^*\geq1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.