关键词： mobile edge computing   DDoS   machine learning   cyber security  

摘要： In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC's services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder-Multi-Layer Perceptron (AE-MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE-MLP model leverages autoencoders' feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE-MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques.

关键词： Task analysis   Computational modeling   Stochastic processes   Autonomous vehicles   Geometry   Servers   Costs   Vehicular network   distributed computation   mobile edge computing   stochastic geometry  

摘要： With the advancement of the Internet of Vehicles (IoV), delay-sensitive vehicular applications have flourished. Among them, the autonomous driving technology is a focal point. For autonomous driving vehicles, efficiently and timely processing the ever-increasing data is critical. In real traffic scenes, the task-processing efficiency is closely related to the traffic flows. However, the traffic flow modeling is always ignored or considered roughly in the most existing studies. For this issue, a traffic model based on a stochastic geometry framework is proposed to simulate a real traffic environment of autonomous driving vehicles. To reduce the cost of processing tasks, a distributed computation offloading scheme based on mobile edge computing (MEC) is proposed by soliciting nearby vehicles and roadside units (RSUs) with rich computing resources. For the average cost minimization optimization problem, we divide the NP-hard problem into several sub-problems and take advantage of the Lagrange multiplier with KKT constraints to solve by optimizing task splitting ratios. We compare the proposed traffic model with some common ones and also consider the pros and cons of different computation offloading strategies. Simulation results show that the proposed strategy outperforms other benchmarks and the proposed modeling method is rational.

关键词： Mobile edge computing   Task dependency   Computing offloading   Directed acyclic graph  

摘要： Mobile edge computing offloads computing-intensive applications from resource-constrained terminal devices to adjacent edge servers to meet users' latency and energy consumption requirements. Most existing studies do not consider the dependencies between applications, leading to the wastage of computing resources. As the number of request users increases, edge servers with limited resources cannot meet the needs of all users. However, there are a large number of idle computing resources on the user side that are not utilized. Aiming at the problem of computing offloading of dependent tasks in this scenario, we establish an end-edge collaboration-dependent task offloading model and propose an offloading algorithm that balances task completion time and energy consumption. Firstly, we solve the problem of collaboratively matching request users by considering user mobility and computing requirements. Secondly, we determine the scheduling order of tasks according to the dependencies between tasks. Finally, we propose a hybrid artificial bee colony algorithm to solve the problem of task offloading. The results show that our algorithm saves 19.9% in average task completion time compared to an offloading strategy that does not consider device-to-device.

关键词： Mobile applications   Optimization   Smart phones   Vehicles   Urban areas   Sensors   Real-time systems   Mobile edge computing   urban traffic optimization   vehicle-to-infrastructure   cyber-physical system  

摘要： The stop-and-go traffic pattern at urban intersections leads to excessive energy use due to frequent vehicle braking, idling, and acceleration. This pattern, amplified by the growing use of automobiles, adversely affects city sustainability, causing delays, pollution, and increased carbon emissions. Addressing this, we introduce a mobile edge computing framework utilizing Internet of Things (IoT) and edge computing. This framework incorporates real-time vehicle-to-infrastructure communication and intelligent speed control algorithms into a mobile app, targeting speed optimization at signalized intersections to alleviate the negative impacts of stop-and-go traffic at urban intersections. The framework comprises three components: a cyberinfrastructure-based messaging system for real-time traffic and signal data from IoT-connected signal controllers and traffic sensors;a speed optimization algorithm generating speed advisories using phone-based sensor data (like GPS and driving directions), signal phase and timing information, and roadside vehicle detector data;and an ad-hoc mobile computing environment turning smartphones into edge devices for hosting the algorithm. This enables intelligent speed advisory along signalized corridors. The paper presents the detailed design and implementation of the proposed framework, highlighting its practicality, utility, and energy-saving potential. Our studies, including traffic simulations, real-vehicle lab experiments, evaluation surveys, and field tests, demonstrate its robustness and effectiveness. Specifically, simulations indicate that using the mobile app universally could lead to a 24% energy reduction in urban transportation systems.

关键词： Costs   Delays   Quality of service   Optimization   Servers   Cloud computing   Trajectory   Cost-efficient   mobile edge computing   online service provisioning   quality -of-service (QoS)  

摘要： The vigorous development of IoT technology has spawned a series of applications that are delay-sensitive or resource-intensive. Mobile edge computing is an emerging paradigm that provides services between end devices and traditional cloud data centers to users. However, with the continuously increasing investment of demands, it is nontrivial to maintain a higher quality-of-service (QoS) under the erratic activities of mobile users. In this paper, we investigate the service provisioning and updating problem under the multiple-users scenario by improving the performance of services with long-term cost constraints. We first decouple the original long-term optimization problem into a per-slot deterministic one by using Lyapunov optimization. Then, we propose two service updating decision strategies by considering the trajectory prediction conditions of users. Based on that, we design an online strategy by utilizing the committed horizon control method looking forward to multiple slots predictions. We prove the performance bound of our online strategy theoretically in terms of the trade-off between delay and cost. Extensive experiments demonstrate the superior performance of the proposed algorithm.

关键词： smart sustainable city   6G   non-terrestrial networks   vision-aided wireless communication   mobile edge computing   deep learning   integrated sensing and communication  

摘要： With the advancement of information and communication technologies (ICTs), the way we live and communicate with each other is changing rapidly. As urban environments continue to evolve, the smart sustainable city (SSC) has sparked considerable attention. We are hoping for a new era in which numerous devices and machines including vehicles, sensors, and robots are all connected to communicate, respond, and operate in real time. The next-generation communication system, the sixth generation (6G), is expected to play a crucial role in improving the efficiency of urban operations and services. In this paper, we first provide the recent trends and key features of standardization in the SSC. To make the future SSC, we highlight key candidate technologies of 6G such as non-terrestrial networks, advanced mobile edge computing, vision-aided wireless communication, artificial intelligence (AI)-based wireless communication, and integrated sensing and communication. We put forth the main technical challenges given to each prime technology along with the potential benefits to pave the way for 6G-enabled SSC. We further address how the potential benefits of prime technologies enable various urban practice cases for 6G-enabled SSC.

关键词： Mobile edge computing   video analysis   video stream offloading   location privacy   usage pattern privacy  

摘要： In Mobile Edge Computing (MEC), the collaboration between end devices and servers guarantees the low-latency and high-accuracy video stream analysis. However, such paradigm of video stream offloading poses a serious threat to the location privacy and the usage pattern privacy of end devices. The existing works offer strict privacy guarantee for users, but they do not take the features of video stream into consideration, thus leading to the relatively higher computation cost. To tackle this issue, we propose a personalized and differential privacy-aware video stream offloading scheme that supports users personalized and time-varying privacy requirements, provides corresponding differential privacy preservation, and generates minimal latency and energy cost. Specifically, we formulate an NP-hard optimization that jointly optimizes the video frame rate, frame resolution and offloading ratio to maximize the analysis accuracy of video stream and minimize the energy cost and the latency subject to the channel bandwidth, computing resources, and personalized and time-varying privacy requirements. Then, we design a online learning-based and personalized privacy-aware video stream offloading algorithm for the optimization problem and thereby obtain the optimal video stream offloading scheme. Last, the extensive experimental results validate the superior performance of the proposed scheme, compared to the three latest existing works.

关键词： DenseNet   detection   mobile edge computing   parking spots   smart cities  

摘要： Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT-based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO-based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

关键词： Internet of medical things   Mobile edge computing   Wireless body area network   Task offloading   Federated reinforcement learning  

摘要： The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intelligent healthcare in the forthcoming 6G era. Healthcare services and applications need ultra-reliable data transfer and processing with ultra-low latency and energy usage. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies enabled IoMT to handle large amounts of data sensing, transmission, and processing while maintaining good QoS. Traditional frame aggregation (FA) systems in WBAN, on the other hand, create an excessive number of control frames during data transmission, resulting in significant latency and energy consumption, as well as a lack of flexibility. A Federated Reinforcement Learning (FRL) based TO Approach is recommended in this research. In the beginning, different types of service-related information were separated into queues with equal QoS needs. The duration of the FA was then automatically determined by the aggregation vertex based on energy consumption, latency, and throughput using FRL. Finally, based on the existing status, the amount of tasks offloaded was determined. The simulation results demonstrate that, as compared to the baseline schemes, the suggested FRLTO efficiently reduces energy consumption and latency while enhancing throughput and total WBAN utilization. Numerical results show that the proposed scheme improves the throughput by 37.06% and reduced the energy consumption by around 69.84% and time delay by about 6.23%, as compared to the state-of-the-art existing baseline schemes.

关键词： mobile edge computing   data consistency verification   reputation model   homomorphic hash   incentive mechanism  

摘要： The emergence of mobile edge computing (MEC) has facilitated the development of data caching technology, which enables application vendors to cache frequently used data on the edge servers close to the user, thereby providing low-latency data access services. However, in an unstable MEC environment, the multi-replica data cached by different edge servers is prone to corruption, making it crucial to verify the consistency of multi-replica data on different edge servers. Although the existing research realizes data integrity verification based on the cooperation of multiple edge servers, the integrity proof generated by multiple copies of data is the same, which has low verification efficiency and is vulnerable to attacks such as replay and replace. To address the above issues, based on homomorphic hash and sampling algorithms, this paper proposes an efficient and lightweight multi-replica integrity verification algorithm, which has significantly less storage cost and computational cost and can resist forgery and replay and replace attacks. Based on the verification algorithm, this paper further proposes a multi-replica edge data integrity collaborative audit scheme EDI-C based on the reputation model. EDI-C realizes the efficient collaborative audit of multiple edge servers in a distributed discrete environment through an incentive mechanism to avoid the trust problem of both sides caused by centralized audit. Also, it supports batch auditing of multiple copies of original data files at the same time through parallel processing and data block auditing technology, which not only significantly improves the verification efficiency but also realizes the accurate location and repair of corrupted data at the data block level. Finally, the security analyses and performance evaluation show the security and practicability of EDI-C. Compared with the representative schemes, the results show that EDI-C can ensure the integrity verification of cached data more efficiently in an