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As far as mobile edge computing is concerned, it is necessary to ensure the data integrity of latency-sensitive applications during the process of computing. While certain research programs have demonstrated efficacy, challenges persist, including the inefficient utilization of computing resources, network backhaul issues, and the occurrence of false-negative detections. To solve these problems, an integrity protection scheme is proposed in this paper on the basis of data right confirmation (DRC). Under this scheme, a two-layer consensus algorithm is developed. The outer algorithm is applied to establish a data authorization mechanism by marking the original data source to avoid the false negative results caused by network attacks from the data source. In addition, blockchain-based mobile edge computing (BMEC) technology is applied to enable data sharing in the context of mobile edge computing while minimizing the network backhaul of edge computing. Based on the Merkle Tree algorithm, the inner layer algorithm is capable not only of accurately locating and promptly repairing damaged data but also of verifying all servers in the mobile edge computing network either regularly or on demand. Finally, our proposal is evaluated against two existing research schemes. The experimental results show that our proposed scheme is not only effective in ensuring data integrity in mobile edge computing, but it is also capable of achieving better performance. [ABSTRACT FROM AUTHOR]

Abstract With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods. [ABSTRACT FROM AUTHOR]

Abstract Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

The evolution of science has given rise to many technologies that have changed the world. The upcoming Six-Generation (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is a paradigm technology that emerges as a key major supporter of enhancing mobility awareness. Edge computing offers improved efficiency for service migration from the edge node to the user. However, mobility management in MEC is a complex challenge as seamless handovers between edge nodes must be efficiently executed to ensure uninterrupted service for mobile devices, demanding intricate coordination and low-latency decision-making. To the best of the author's knowledge, there has been no comprehensive work on the most recent developments in mobility awareness using mobile edge computing in 6G. However, this paper aims to present a general overview of the intersection between mobility awareness and MEC over 6G networks. The general concept of MEC in 6G mobile networks is comprehensively introduced. This will highlight the integration between MEC and 6G for bringing more efficient network and service migration to the edge, reducing latency, and enhancing the user experience. Meanwhile, this survey discusses augmented reality with MEC applications. This survey discusses the integration of mobility awareness and mobile edge computing in upcoming mobile applications and emphasizes the need for 6G networks. This integration results in providing seamless communication during handovers between the serving base station and the target base station. This study contributes to understanding the upcoming trends that will enable the operation of mobility awareness and MEC operation in the 6G mobile communication. Furthermore, we delve into a comprehensive overview of the challenges and future research directions for mobility management with MEC in 6G mobile networks, underlining the complexities and potentials of integrating mobility awareness and mobile edge computing.