Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.

Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.

Over the last decade, the management of healthcare records has been revolutionized due to the need for accurate and cost-efficient patient-centric care alongside the technological advances. Currently, Electronic Health Records (EHRs) are managed using a client-server architecture by which healthcare providers retain the data stewardship. However, this approach suffers from security and privacy issues, a single point of failure, data fragmentation and vulnerability. The data replication, immutability, transparency, security and privacy features of blockchain have a promising future in the healthcare domain addressing the existing issues. In this paper, we propose BlockHR, a healthcare records management framework for healthcare providers and patients enabling better prognosis/diagnosis and follow-up. We analyze the effectiveness of BlockHR in providing security and privacy compared to the client-server approach. We also evaluate the performance of BlockHR versus the client-server approach. Our experimental results demonstrate that client-server approach takes 2.6 times less execution time for data write operation compared to BlockHR. The data retrieval for BlockHR is 20 times faster compared to the client-server approach. We analyze the impact of increasing number of medical records in our evaluation.

Cloud Internet of Things (IoT) is a novel paradigm, where the limitations of IoT associated devices in terms of storage, data access, scalability, networking and computing, and complex analysis are solved through use of the cloud computing infrastructure. The pervasive adoption of cloud in the IoT framework, makes the underlying data centers exacerbate problems like the environmental carbon footprint and operational costs which arise from the high energy consumption of computing servers. Several works proposed virtual machine placement and task scheduling algorithms to reduce the energy consumption of the underlying cloud infrastructure. However, each algorithm uses a different environment, experimental setup, power consumption model and workload for its evaluation, making it difficult to compare among them. In this paper, we give a classification and evaluation of 13 different algorithms using a unified setup, with the aim of achieving an objective comparison. The workload used for the evaluation is selected to typify IoT applications, such as connected vehicles, wide area measurement systems for the power grid, and smart meters for advanced meter infrastructure. The detailed performance analysis is elaborated in this paper.