Explore the vast range of titles available.

The rapid growth in vehicle data fosters the development of vehicular computational offloading. By exploiting the superiority of non-orthogonal multiple access (NOMA) in the offloading process, NOMA-enabled vehicular computation offloading enables collaborative transmissions of multiple fog nodes and is promising for distributed wireless communications environments. Nonetheless, to the best of our knowledge, the problem of task allocation and resource allocation in NOMA-enabled multipoint collaborative offloading in Vehicular Fog Computing (VFC) still remains open. With this consideration, the NOMA-enabled vehicular fog computing problem is investigated in this work, as well as an energy-efficient NOMA-enabled vehicular computing offloading scheme is proposed. The scheme combines task vehicles, main fog access points (F-APs), idle vehicles, and auxiliary F-APs to collaborate on tasks. However, the corresponding optimization problem turns out to be a non-convex issue. To this end, through in-depth mathematical analysis, the monotonicity of NOMA power allocation is discussed to find the optimal power allocation coefficient. Furthermore, an interior-point method based on successive convex approximation (SCA) is proposed to obtain the sub-optimal solution of task division and bandwidth allocation. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

With the development of Internet of Things (IoT) devices and the growth of users’ demand for computation and real-time services, artificial intelligence has been applied to reduce the system cost for future network systems. To meet the demand of network services, the paradigm of edge networks is increasingly shifting towards the joint design of computation, communication and caching services. This paper investigates a multi-user cache-enabled mobile edge computing (MEC) network and proposes an intelligent particle swarm optimization (PSO) based offloading strategy with cache mechanism. In each time slot, the server selects one file among multiple ones to pre-store, according to the proposed cache replacement strategy. In the next time slot, the requested files by the users needn’t to be computed and offloaded, if these files have been cached in the server. For the files that have not been cached in the server, PSO algorithm is adopted to find an appropriate offloading ratio to implement the partial offloading. Simulation results are finally presented to validate the proposed studies. In particular, we can find that incorporating the proposed cache replacement strategy into the computation offloading can effectively reduce the system latency and energy consumption for the future networks.

The hierarchical local-fog-cloud computing-enabled paradigm applied in fog radio access networks (F-RANs) has been considered as a promising architecture to cope with the surge of mobile intelligent applications. Meanwhile, non-orthogonal multiple access (NOMA) technology with superior spectrum efficiency has been widely utilized to provide transmission services for offloading tasks in F-RANs. A comprehensive understanding of the computation offloading performance of NOMA-based F-RANs is thus essential to provide guidelines for F-RANs planning and resources scheduling. In this paper, we first develop a mathematical framework to analyze the ability of NOMA to support computation offloading in F-RANs. Then, the expression for a lower bound of the average computation offloading probability is provided based on stochastic geometry and order statistics, and the relation between the computation offloading probability and the average task execution latency as well as energy consumption is further derived by queuing theory. Finally, extensive numerical simulations are executed to verify the accuracy of our theoretical results. The numerical results show that the average computation offloading probability is nearly linear with most of key network parameters, such as the coverage radius of cellular networks, the density of fog radio access points (F-APs), the number of users sharing the same spectrum resources, and the task request rate, respectively.

The network nodes in the Ocean Internet of Things exhibit strong heterogeneous characteristics, which introduce complex and high-dimensional constraints for the optimization of task offloading in ocean mobile edge computing. To prevent data from being intercepted by malicious attackers during computation offloading and to protect client privacy, an offloading framework named the Enhanced Genetic Algorithm for Secure Computation Offloading is proposed. The population initialization and crossover operators are optimized before the initialization phase to accelerate convergence and avoid the risk of excessive resource consumption due to cyclic scheduling. An elitism strategy is also introduced, along with dynamic mutation rate adjustment, to enhance the search efficiency and global optimization capability of the algorithm. The superiority of the proposed framework is validated through simulation experiments and a comprehensive comparison with other benchmark algorithms. Compared to the Simulated Annealing, Particle Swarm Optimization algorithms, the Min-Min algorithm, Basic Genetic Algorithm and Proximal Policy Optimization, the proposed scheduling algorithm demonstrates a significant enhancement in secure capacity during local computation, with improvements of 47.60%, 42.16%, 15%, 25% and 16.67%, respectively. Additionally, it achieves an increase in security during the computation offloading process by 16.54%, 16.22%, 14.29%, 12.56% and 10.02% respectively.

Cooperative computation offloading (CCO) is a technique to improve computation offloading performance in edge networks through collaboration between edge nodes. CCO can achieve better resource utilization, balance the computational load and further reduce delay to improve the service experience of user equipment (UE). In this paper, we investigate the problem of collaborative computing task offloading scheme and computing resource allocation in mobile edge computing and propose a data compression cooperation computing offloading (DCCO) scheme. To reduce the amount of data transmitted on the UE offloading link, we introduce a Data Compression into CCO and present the computational offloading strategy, collaborative offloading and computational resource allocation problems with the goal of minimizing the weighted sum of delay and energy consumption of the UE under the constraints of UE delay and energy consumption. And an improved genetic algorithm is proposed to solve the problem which is a non-convex mixed-integer problem with binary and continuous variables. The offloading strategy and computational resource allocation correspond to the genes in the genetic algorithm chromosome. The simulation results show that the DCCO scheme can reduce the offloading cost up to 11% compared with the existing schemes. It effectively improves the computation offloading performance of edge network computing.

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.

With the growth of the Internet of Things, smart devices are subsequently generating a large number of computation-intensive and latency-sensitive tasks. Mobile edge computing can provide resources in close proximity, greatly reducing service latency and alleviating congestion in mobile core networks. Due to the instability of the mobile edge computing environment, it was difficult to guarantee the quality of service for users. To address this problem, a hybrid computation offloading framework based on Deep Deterministic Policy Gradient (DDPG) in IoT is proposed. The framework is a system consisting of edge servers and user devices. It is used to acquire the environment state through Software Defined Network technologies and generate the offloading strategy by Deep Deterministic Policy Gradient. The optimization objectives in this paper include the total system overhead of the mobile edge computing system, and considering both network load and computational load, an optimal offloading strategy can be obtained to enable users to obtain a better quality of service. Finally, the experimental results show that the algorithm outperforms the comparison algorithm and can reduce the system latency by 20%, while the network load and computational load are also more stable.

Mobile cloud computing requires specialized application development models that can facilitate development of cloud-enabled applications. This paper presents a mathematical model to calculate the computation offloading cost (time and energy consumption) of mobile cloud application models to facilitate in the development of mobile cloud computing simulators. It demonstrates the usage of the proposed model, and shows the impact of the cost incurring parameters on the overall computational time and energy consumption of the applications. The proposed model can assist cloud-powered applications to make context-aware offloading decisions and facilitate the development of mobile cloud computing simulators, which unfortunately, does not exist to date.

With the deployment of the 6G space-air-ground integrated network, IoT devices face challenges such as limited computational capacity, insufficient energy, and security risks. To address these issues, this paper proposes a security-aware multi-agent reinforcement learning framework for secure computation offloading ⁠—SMPPO. The approach combines Proximal Policy Optimization with an adaptive clustering algorithm and utilizes a weighted sum method for multi-objective optimization to balance security, energy consumption, and latency. By introducing a task security cost quantification model and the concept of \"Security Provided,\" this paper quantifies the security levels under different encryption schemes. The adaptive clustering algorithm dynamically adjusts encryption schemes based on network load, ensuring data confidentiality while improving the feasibility of offloading tasks. Experimental results demonstrate that the proposed method effectively reduces task drop rates while balancing energy consumption and latency, validating its effectiveness in enhancing both security and resource efficiency.

With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As a computing paradigm that could effectively reduce the task processing time consumption of user device and the cost of cloud server, edge computing has become an indispensable part of intelligent transportation system. However, how to reduce the load imbalance of edge computing server while ensuring that the task processing of intelligent transportation device takes less time consumption and energy consumption has become a challenge. In order to tackle this challenge, the computation offloading decision-making problem in the intelligent transportation edge computing scenario was modeled as a multi-objective optimization problem in this paper, and an adaptive multi-objective optimization algorithm (E-NSGA-III) based on NSGA-III was used to solve this problem, and comparative experiment with other methods was made. Experimental results show that compared with NSGA-II, MOEA/D and NSGA-III, proposed algorithm (E-NSGA-III) in this paper can mostly reduce time consumption by 14.28%, 18.42% and 9.82%, energy consumption by 5.59%, 6.79% and 4.83%, and load balancing variances by 21.73%, 33.46% and 18.25%.