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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.

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.

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.

The rapid development of IoT-based services has resulted in an exponential increase in the number of connected smart mobile devices (SMDs). Processing the massive data generated by the large number of SMDs is becoming a big problem for mobile devices, servers, and wireless communication channels. A Multi-access Edge Computing (MEC) paradigm partially mitigates this problem by deploying edge server nodes at the edge of wireless networks nearby SMDs, but the challenge still remains due to the limited computation capacity of MEC servers and the bandwidth of wireless channels. In addition, the dependency of tasks generated by applications on SMDs increases the complexity of the problem. In this paper, we propose a constrained multiobjective computation offloading optimization solution to resolve the problem of task dependency under limited resources. This solution improves the Quality of Service (QoS) through minimizing the latency, energy consumption, and rate of task failure caused by limited resources. We propose a two-staged hybrid computation offloading optimization method to solve the problem. In the first stage, the computation offloading decisions are made based on the preferences of tasks. Then, in the second stage, nearly optimal solutions are found using the modified Non-Dominated Sorting Genetic Algorithm (NSGA-III). The overall efficiency of the proposed method is increased owing to the preference-based algorithm reinforcing the NSGA-III algorithm by generating a better initial population. The results of extensive experiments show that the efficiency of the proposed method is significantly better than the existing methods.

This paper investigates the wireless powered mobile edge computing (WP-MEC) system, which consists of an energy transmitter (ET) with multiple antennas, an MEC server and multi wireless devices (WDs) with single antenna. The ET transfers energy to WDs via energy beamforming. Each WD harvests energy to power its operations, i.e., computing tasks locally and offloading partial tasks to the MEC server. A practical nonlinear energy harvesting (EH) model is adopted to describe the relationship between energy transmitting and harvesting. In the context of the nonlinear EH model, energy constraints between harvesting and comsumption at WDs is analyzed and an optimization problem to maximize the weighted sum computation rate of the system is formulated. The transmitting power allocation among different energy beams at the ET and computation offloading at WDs are jointly optimized to make full use of energy and computing resources. Since the nonlinear EH model is nonconvex, it is hard to solve the formulated problem. By exploiting the successive convex approximation method, an iterative optimizing scheme called JOEO (Joint Offloading and Energy Optimization) is proposed. Simulation results in the final part indicate the convergence and superiority of the JOEO scheme over other benchmark schemes under different system parameters.

The widespread acceptability of mobile devices in present times have caused their applications to be increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very prevalent among users but the execution results in dissipating many of the device end resources. Mobile cloud computing (MCC) has a solution to this problem by offloading certain parts of the application to cloud. At the first place, one might find computation offloading quite promising in terms of saving device end resources but eventually may result in being the other way around if performed in a static manner. Frequent changes in device end resources and computing environment variables may lead to a reduction in the efficiency of offloading techniques and even cause a drop in the quality of service for applications involving the use of real-time information. In order to overcome this problem, the authors propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end parameters, network conditions, and cloud resources. The article also talks about the proposed algorithm that depicts the workflow of the offloading model. The proposed model is simulated using the CloudSim simulator. In the end, the authors illustrate the working of the proposed system along with the simulated results.