关键词： Mobile edge computing  

摘要： The rapid growth of resource-hungry and time-critical applications reflects the rise of resources needed for communication, processing, and energy consumption. Mobile edge computing (MEC) that offers cloud-computing services proximate to users at the edge of mobile network is considered to be the key technology to facilitate task scheduling closer to data-sources. The objective of this paper is to propose an improved task scheduling strategy that selects the best MEC server to process each task, which reduces the system energy consumption and delay. Therefore, we proposed an improved task scheduling strategy on the basis of non-dominated sorting genetic algorithm II (NSGA-II). To improve the performance of NSGA-II, we proposed a hierarchical search policy (NSGA-H) that eliminates the number of redundant comparisons and thus, enhances time complexity. The simulation results illustrate that the proposed strategy improves average service delay, service time, energy consumption, and system utility compared to baseline approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Mobile edge computing  

摘要： The progression of computing paradigms is characterized by the emergence of wireless-powered mobile edge computing (WP-MEC), which has significantly transformed resource-efficient and ubiquitous processing at the edges of wireless networks. Integrating intelligent reflecting surfaces (IRS) into WP-MEC systems introduces new opportunities by enhancing transmission channel quality while addressing challenging resource management issues. To tackle these challenges, this paper proposes a collaborative transmission and resource management scheme to facilitate differentiated computation capabilities. Specifically, a novel performance metric called energy cost-effectiveness is introduced to comprehensively assess the performance of IRS-aided coupling. Subsequently, a collaborative transmission mechanism is developed, incorporating communication, energy transmission, and edge computing considerations with adaptive IRS association. To further optimize system performance, two adaptable resource management strategies are devised to maximize energy cost-effectiveness by jointly optimizing IRS, communication, energy, and computation resources, tailored for both unlimited and limited computation capabilities. Comparative analysis with benchmark schemes demonstrates the superiority of the proposed collaborative transmission and resource management approach in enhancing transmission quality and reducing energy consumption. Moreover, the benefits of IRS integration, in terms of energy cost-effectiveness improvement through appropriate quantity and configuration, are highlighted, underscoring its potential to shape the future landscape of industrial Internet-of-Things. IEEE

关键词： Mobile edge computing  

摘要： The combination of Mobile Edge Computing (MEC) and sensing technologies such as Light Detection and Ranging (LiDAR) offers a viable path toward enhancing autonomous vehicle (AV) navigation and traffic monitoring in the context of intelligent transportation systems. In order to meet these needs, this article offers a methodology that investigates the use of Elevated LiDAR (ELiD) and its integration with MEC. Our work focuses on two main challenges: optimizing the placement of ELiDs to ensure extensive urban coverage and minimizing network latency by efficiently routing data to MEC servers. By proposing a heuristic for real-time task allocation, we aim to enhance safety and operational efficiency in smart cities. Our findings show a modest optimality gap where the heuristic achieves a balance between computational efficiency and minimized cloud dependency, albeit at the cost of a marginally increased latency, highlighting the nuanced trade-offs in edge-to-cloud task distribution for efficient LiDAR data processing in smart cities. © 2024 IEEE.

关键词： game training platform   virtual reality technology   mobile edge computing   genetic algorithm  

摘要： The purpose of paper is to solve the problems of difficulty in learning and low user satisfaction in the design process of the game 3D training platform. This paper used mobile edge computing and VR technology to study the construction of the game 3D modelling simulation training platform. By using mobile edge computing, users can experience faster games by improving the running speed of the 3D game training platform. The experiment proved that the game training platform built by mobile edge computing and VR technology can improve the user's game level. User A scored an average of 8.02 points, 5.22 points and 6.89 points higher than User B for 10 games of Game Q, Game W and Game P. At the same time, it can also improve the satisfaction rate of platform users. Using mobile edge computing and VR to build a game training platform can improve users' game level and users' satisfaction with the platform.

关键词： Time factors   Resource management   Cloud computing   Autonomous vehicles   Vehicle dynamics   Surveys   Servers   Connected vehicles   Multi-access edge computing   Connected vehicular   TSSCV applications   communication and computation resources   reservation request   reservation update strategy   mobile edge computing   resource trading and exchange  

摘要： Edge computing resources are crucial for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require guaranteed resource availability for real-time communication and computation to ensure safety and efficiency. With the emergence of integrated sensing and communication (ISAC) in 6G communication systems, extensive sensing data will be added to the communication data. To meet these challenges, resource reservation becomes a necessity. Resource reservation approaches can be either network-side or vehicle-side. Most existing approaches are network-side. However, these approaches are insufficient for these applications because they only guarantee resources to individual vehicles with a certain probability. Vehicle-side reservation is challenging because vehicles usually do not have sufficient information to reason about future available resources and their costs. This comprehensive survey aims to define the characteristics of TSSCV applications and to analyze existing reservation approaches and cost-effective schemes, considering different reservation scenarios in single and multiple mobile network operator (MNO) environments. In addition, it explores strategies to revise or update reservations when faced with uncertain reservation times and costs. To this end, we propose to enable resource trading and reservation exchange facilitated by blockchain smart contracts. This approach promises a secure and efficient method for resource management in future smart vehicles.

关键词： named data networking   NDN   producer mobility   mobile edge computing   MEC  

摘要： In recent years, with the rapid development of the internet and mobile communication technologies, the mobility issues in traditional IP network architecture have significantly affected the efficiency of task offloading. Therefore, named data networking (NDN) has been proposed, which provides native support for consumer mobility through its receiver-driven content retrieval model. However, producer mobility is still a challenging problem, and the current solutions all have the disadvantage of introducing additional issues. To this end, this paper proposes and comprehensively analyses a scalable mobility management mechanism MECProMo, which combines the characteristics of mobile edge computing (MEC) and NDN network, and utilises the stateful forwarding plane of NDN to promote producer mobility. Combined with MEC, a temporary forwarding path is established on the name-based NDN data plane. Simulation results of ndnSIM show that compared with other schemes, MECProMo reduces the average packet loss rate by 89.04% and 94.54% respectively.

关键词： Protocols   Servers   Security   Authentication   Passwords   Costs   Internet of Things   security   mobile edge computing   session key  

摘要： The emergence of edge computing has revolutionized the landscape of modern applications, offering remarkable advantages such as low-latency and high-bandwidth abilities by decentralizing computation closer to data sources. Despite these advantages, providing secure software regarding communication between users and mobile edge computing (MEC) servers introduces a challenge owing to the sensitive nature of the transmitted data. In response, numerous authentication protocols have been proposed to maintain the reliability of software communication within the MEC environment. In this paper, we introduce RAP-MEC, a novel technique developed to enhance software quality by setting up session key for encrypted communication between users and MEC server. RAP-MEC incorporates functionalities for password change and single sign-on, enabling seamless access to MEC server services. Leveraging physical unclonable functions, symmetric encryption, elliptic curve cryptography, and hash functions, RAP-MEC aims to significantly reduce communication cost [24.85% to 72.73%] and execution time [34.66% to 76.64%]. Through rigorous security analysis, we assess RAP-MEC's resilience against various threats, including DoS, replay, man-in-the-middle, and impersonation attacks. Our findings underscore RAP-MEC's robustness, ensuring the integrity and confidentiality of communications within the MEC environment.

关键词： mobile edge computing   multiple access   Reconfigurable intelligent surface   resource allocation   wireless power transfer  

摘要： This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading. © 2002-2012 IEEE.

关键词： computation offloading   data offloading   delay   energy consumption   execution time   Femtolet   MCC   MEC   mobile cloud computing   mobile edge computing   QoE   QoS   quality of experience   quality of service  

摘要： Mobile cloud computing (MCC) is the recent technology that to provide execution of different mobile applications. Mobile devices are flexible and portable. The key disadvantages of mobile devices are limited battery life, lower bandwidth and insufficient storage capabilities. MCC reduces the drawbacks of mobile devices. The resolve the execution delay of mobile devices mobile edge computing (MEC) concept was proposed. Offloading is an effective method to save execution time and energy both. Code or Application Offloading allows executing a task in Cloud instead of Mobile Device itself. This paper presents a comprehensive discussion on different methodologies and the apparatus used for offloading of code or application into cloud. This paper also highlights on merits and demerits of different offloading techniques. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Task analysis   Resource management   Cloud computing   Servers   Computational modeling   Optimization   Mobile handsets   Multi-access edge computing   Mobile edge computing   task offloading   resource allocation   multi-objective optimization   estimation-of-distribution model  

摘要： With the rapid growth of the mobile computing techniques, a wide variety of mobile edge computing (MEC) applications have emerged recently, aiming to provide computationally intensive and delay-sensitive network services. Through MEC, various complex tasks of mobile devices can be offloaded to the edge of network system for execution by edge servers, which greatly reduces the local computing burden. However, how to effectively allocate computational and communication resources in edge-cloud remains a challenging task, especially when multiple mobile users and edge servers are involved. In this paper, we propose a decomposition-based multi-objective optimization algorithm based on estimation-of-distribution models (MOEA/D-EoD) to deal with the task offloading and resource allocation problem in MEC. Especially, considering the features of multi-user and multi-server cloud-edge-end collaboration wireless MEC system, we construct a joint optimization model of task offloading and resource allocation, where limited communication and computational resource constraints are considered. To deal with the optimization model, we design an efficient decomposition-based algorithm, which incorporates two novel estimation-of-distribution models to deal with discrete and continuous decision variables of the problem. Experimental results obtained from benchmark test suites DTLZ and ZDT demonstrate that the proposed method exhibits significantly superior performance compared to other comparative algorithms. The proposed model and algorithm are simulated and tested on different test instances, and experimental results show the effectiveness and efficiency of our proposed method.