With the rapid development of computing power and artificial intelligence, IoT devices equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy many conveniences with intelligent devices, such as face recognition, video understanding, and motion estimation. Currently, deep neural networks are the mainstream technology in intelligent mobile applications. Inspired by DNN model partition schemes, the paradigm of edge computing could be utilized collaboratively to improve the effectiveness of intelligent task execution in IoT devices. However, due to the dynamics of the wireless network environment and the increasing number of IoT devices, a DNN partition policy without adequate consideration would pose a significant challenge to the efficiency of task inference. Moreover, the shortage and high rental cost of edge computing resources make the optimization of DNN-based task execution more difficult. To cope with those situations, we propose a joint method by a self-adaptive DNN partition with cost-effective resource allocation to facilitate collaborative computation between IoT devices and edge servers. Our proposed online algorithm can be proved to ensure the overall rental cost within an upper bound above the optimal solution while guaranteeing the latency for DNN-based task inference. To evaluate the performance of our strategy, we conduct extensive trace-driven illustrative studies and show that the proposed method can achieve sub-optimal results and outperforms other alternative methods.

The mobile edge computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in mobile terminals by offloading computation-intensive and delay-sensitive tasks to nearby edge nodes. However, limited computation resources in edge nodes may not be sufficient to serve excessive offloading tasks exceeding the computation capacities of edge nodes. Therefore, multiple edge clouds with a complementary central cloud coordinated to serve users is the efficient architecture to satisfy users’ Quality-of-Service (QoS) requirements while trying to minimize some network service providers’ cost. We study a dynamic, decentralized resource-allocation strategy based on evolutionary game theory to deal with task offloading to multiple heterogeneous edge nodes and central clouds among multi-users. In our strategy, the resource competition among multi-users is modeled by the process of replicator dynamics. During the process, our strategy can achieve one evolutionary equilibrium, meeting users’ QoS requirements under resource constraints of edge nodes. The stability and fairness of this strategy is also proved by mathematical analysis. Illustrative studies show the effectiveness of our proposed strategy, outperforming other alternative methods.

Within the paradigm of forthcoming 6G network infrastructures, unmanned aerial vehicles (UAVs), functioning as principal conveyances, are projected to emerge as pivotal enablers in the nascent domain of the low-altitude economy. UAVs are poised to embrace various innovative applications, including latency-sensitive and compute-intensive services. However, UAVs are constrained by their energy capacity and computational resources, rendering them insufficient for fulfilling the increasingly rigorous service demands in the future. To address these challenges, our investigation focuses on the innovative UAV-based Vehicular Edge Computing (UVEC) framework, incorporating Vehicular Edge Computing (VEC) in UAV systems to bolster service reliability. A UAV can enhance its mission duration by dynamically selecting suitable vehicles for computation offloading and adaptively adjusting the task offloading ratio between vehicles and the edge server. By integrating vehicle selection and task offloading scheduling in the UVEC framework, we investigate the optimization of energy efficiency while satisfying the statistical delay and the buffer constraints for UAVs. To deal with the proposed problem, a distributed algorithm is designed by jointly considering the vehicle selection for task offloading radio to vehicles and the edge server. The stochastic network calculus (SNC) is employed to derive performance bounds for the statistical delay and constraints, enabling robust analysis and optimization of network performance. After that, we leverage linear transformation techniques to reformulate the original problem into a linear framework, enabling the application of the Alternating Direction Method of Multipliers (ADMM) algorithm to efficiently solve the transformed problem. Theoretical analysis and simulation results show that our algorithm converges while effectively satisfying service reliability constraints within the desired targets, outperforming benchmark schemes in terms of efficiency while meeting task delay and error-rate bounded constraints

With the proliferation of 5G networks, mobile edge computing (MEC) has emerged as a promising technology to fulfill the stringent requirements for reliability-aware services in the Internet of Things (IoT). However, in such networks, the wireless channel states and the task arrivals are stochastic and hard to predict well. Under this scenario, tasks generated from mobile devices would pile up in the transmission queue and edge computing queue when offloading to the edge cloud via a 5G network, resulting in quality degradation for reliability-aware services. To tackle the above challenges, we introduce non-orthogonal multiple access (NOMA) in MEC to meet the requirements of ultra-reliable and low-latency communications (URLLC), in which task queuing delay violation probability and transmission error probability are both considered. Furthermore, we explore the closed-form expression based on the effective capacity (EC) to derive the performance boundary of service reliability under a general model that multiple data sources are from different IoT devices and tasks are offloaded through two-stage transmission-computing tandem queues. Based on the above mathematical analysis for service reliability, we propose an efficient strategy combining power allocation and task offloading to reduce energy consumption for all devices in NOMA-enabled MEC. Extensive simulation studies are further conducted to validate the advantage of our strategy and show the significant performance gain of nearly up to 20% over other alternatives.

In the scenario of smart healthcare applications, the Internet of Medical Things (IoMT) devices, equipped with limited resources, would offload numerous computation-heavy tasks to an edge server through 5G networks. However, IoMT devices should usually move around different diagnostic areas in smart healthcare systems, leading to the dynamics of the uplink channel quality. Moreover, the burst generation of a substantial number of tasks from IoMT devices can result in congestion within the computing queue of the edge server, and heterogeneous services in IoMT devices make it hard to collect global information for a central controller to get the optimal optimization for all IoMT devices. So, how to determine task offloading among IoMT devices in a distributed scenario of smart healthcare applications should be considered appropriately and comprehensively. In this article, we investigate task offloading in mobile edge computing (MEC) through wireless networks. To improve the utilization of wireless resources, nonorthogonal multiple access (NOMA) is adopted in 5G networks. We first formulate the mobility of IoMT devices as a hidden Markov model (HMM) and the problem of task offloading policy as a Distributed partial Markov decision process (Dec-POMDP). Then, we propose a mobility-aware method based on Multiagent reinforcement learning for task offloading in 5G NOMA-enabled networks. In our approach, task offloading scheduling for each IoMT device in NOMA-enabled 5G networks is considered to improve energy efficiency and guarantee service quality. Besides, the time complexity and the existence of a Nash equilibrium for our proposed Dec-POMDP method are theoretically derived. Simulations are conducted to show that our algorithm outperforms other alternative methods in energy consumption under the delay constraint.

With the emergence of Industry 5.0, there has been a significant surge in the need for intelligent services within the realm of smart devices. Currently, deep neural networks (DNNs) have become the predominant technology in driving advancements in intelligent applications. With the collaboration of mobile edge computing (MEC), resource-constraint smart devices, such as the industrial Internet of Things (IIoT) devices, can meet the requirement of high computing for DNN-based inference by computation offloading. In the task offloading strategy obtained by a central decision-maker with global information, all devices in the MEC can get the optimal optimization for DNN inference acceleration. However, in a practical environment, central decision-making may get into trouble, such as information synchronization delay, irrational behavior of devices, and privacy leakage. In this paper, we explore the optimization of distributed task offloading for smart devices to deal with these challenges regarding DNN inference acceleration, considering the character of an early exit in the DNN model to balance the accuracy and latency. In our system model, the optimization is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). Each smart device performs its strategy, including task offloading decision and DNN branch selection with local observation, and cooperatively optimizes the overall Quality of Experience for DNN inference. Based on the model of Dec-POMDP, we propose one algorithm based on Multi-agent Reinforcement Learning to solve the above problem. In our algorithm, we utilize the advanced function based on the counterfactual baseline to guide policy gradient learning to overcome the credit allocation problem in cooperative optimization. In addition, LSTM is introduced to improve the robustness of the algorithm. Finally, detailed performance evaluation and comparison are performed to show the effectiveness of our strategy.