The development of Industrial Internet of Things (IIoT) has completely changed traditional manufacturing industry. Industrial equipments with limited resources often cannot meet the diverse demands of numerous computing-intensive and latency-sensitive tasks. Mobile edge computing (MEC) offloads these tasks to nearby edge servers to achieve lower latency and energy consumption. However, considering the channel interferences of the network and the diverse demands of different tasks, coordinating computation offloading among multiple devices is challenging. To address this challenge, the computation offloading is formulated as a multiobjective optimization problem, and a new task model composed of scientific workflow tasks and concurrency workflow tasks is proposed to represent the multitask in the industrial environment. In addition, a two-hierarchical optimization framework is devised to optimize the bandwidth allocation and the multitask computation offloading through the dynamic bandwidth preallocation and the improved multiobjective evolutionary algorithm based on decomposition with two performance enhancing schemes. Comprehensive experiments demonstrate that the effectiveness and efficiency of our proposed framework in terms of the tradeoffs between latency and energy consumption, as well as the convergence and diversity of obtained nondominated solutions.

Recommendation system plays a significant role in helping people to get effective information from mass data. Traditional recommendation systems focus on the recommendation accuracy, which is not sufficient. In this paper, we also consider the various needs of users to achieve more diverse recommendation. However, accuracy and diversity are two conflicting goals for recommendation system. Hence, we model the recommendation system as a multi-objective optimization problem, and aim to find tradeoff solutions between the two goals. Because the rating matrix is rather sparser in recommendation, we first use singular value decomposition to get the recommendation list, then multi-objective immune algorithm is used to optimize it. The experimental results illustrate that the proposed algorithm can get more diverse and accurate recommendation results.

Unmanned aerial vehicle (UAV) and base station jointly assisted multi-access edge computing (UB-MEC) technology is a promising direction to provide flexible computing services for resource-limited devices. Due to the non-real-time observation of device loads and the dynamic nature of demand in UB-MEC, it is a highly challenging problem to make UAV respond in real time to meet user’s dynamic preferences in UB-MEC. To this end, we propose a multi-objective deep reinforcement learning (MODRL) for computation offloading and trajectory control (COTC) of UAV. First, the problem is formulated as a multi-objective Markov decision process (MOMDP), where the traditional scalar rewards are extended to vector, corresponding to the number of task data collected, the completion delay and the UAV’s energy consumption, and the weights are dynamically adjusted to meet different user preferences. Then, considering the device load information stored in UAV is non-real-time, an attentional long short-term memory (ALSTM) network is designed to predict real-time states by autofocusing important historical information. The near on-policy experience replay (NOER) reviews experiences close to on-policy can better promote learning of current strategy. The simulation results show that the proposed algorithm can obtain the action policy which meets the user’s time-varying preferences, and can achieve a good balance between different objectives under different preferences.

With the development of Internet of Vehicles (IOV) technology, minimizing network latency while performing computationally intensive tasks has become a challenge. As the most advanced data communication technology, 6G can significantly reduce the latency. Additionally, utilizing remote satellites for assistance can alleviate the computational pressure. In this paper, an Integrated Satellite-Terrestrial Internet of Vehicles (IST-IOV) environment was established with 6G networks. Firstly, we established the Edge Computation Service Provider (ECSP) to manage task allocation and data flow. Secondly, considering energy consumption, latency, and offloading process, we define utility values for vehicles and ECSP, construct a Stackelberg bi-level dynamic cooperative game. The existence of the unique Nash equilibrium in the game has also been proved by backward induction methods. Finally, based on the interaction between vehicles and ECSP, we proposed a computation offloading algorithm called Dynamic Cooperative Game Theory in IOV (DCGT-IOV). The DCGT-IOV algorithm can find the optimal offloading strategy for vehicles and the optimal pricing strategy for ECSP based on the gradient of the utility function. Simulation results demonstrate that the proposed algorithm achieves higher overall utility in various scenarios compared with other strategies and exhibits good convergence properties.