In this paper, we study a multiuser mobile edge computing (MEC) network, where tasks from users can be partially offloaded to multiple computational access points (CAPs). We consider practical cases where task characteristics and computational capability at the CAPs may be time-varying, thus, creating a dynamic offloading problem. To deal with this problem, we first formulate it as a Markov decision process (MDP), and then introduce the state and action spaces. We further design a novel offloading strategy based on the deep Q network (DQN), where the users can dynamically fine-tune the offloading proportion in order to ensure the system performance measured by the latency and energy consumption. Simulation results are finally presented to verify the advantages of the proposed DQN-based offloading strategy over conventional ones.

As an emerging and promising computing paradigm in the Internet of things (IoT), edge computing can significantly reduce energy consumption and enhance computation capability for resource-constrained IoT devices. Computation offloading has recently received considerable attention in edge computing. Many existing studies have investigated the computation offloading problem with independent computing tasks. However, due to the inter-task dependency in various devices that commonly happens in IoT systems, achieving energy-efficient computation offloading decisions remains a challengeable problem. In this paper, a cloud-assisted edge computing framework with a three-tier network in an IoT environment is introduced. In this framework, we first formulated an energy consumption minimization problem as a mixed integer programming problem considering two constraints, the task-dependency requirement and the completion time deadline of the IoT service. To address this problem, we then proposed an Energy-efficient Collaborative Task Computation Offloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approach to obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstrated that the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithm could effectively reduce the energy cost of IoT sensors.

In the contemporary landscape of large language models (LLMs) development, it is crucial to address the challenges of deploying these models on hardware-constrained consumer electronic devices (CEDs), especially within complex dynamic task scheduling in multi-cloud environments (MCE). We propose a novel methodology leveraging a lightweight LLM to enhance task scheduling decisions in MCE.Our approach involves creating a task scheduling expert database informed by optimization objectives to fine-tune the lightweight LLM. This enables the model to generate a schedulable candidate set of tasks based on the current state of tasks and operational conditions within CEDs across MCE, optimizing scheduling decisions and enhancing overall efficiency. Simulations using both synthetic and real-world datasets demonstrate that our method outperforms three other algorithms in cost minimization, makespan reduction, and energy consumption. In summary, our methodology empowers CEDs to optimize the utilization of multi-cloud resources and harness the capabilities of lightweight LLMs to effectively minimize makespan, operational costs, and energy consumption during the task scheduling process, thereby facilitating efficient task scheduling.

Due to the fast, dynamic, and continuous arrival of data streams in the green Internet of Things (IoT) environment, the probability distribution of data streams changes over time. In real IoT scenarios such as unmanned aerial vehicle (UAV) detection and smart light switch control, data distribution changes have reduced the trained model’s accuracy for data streams problems classification, making it challenging to detect UAV intruders and predict whether energy-saving lamps in smart buildings are on or off. In this paper, an incremental ensemble classification method is proposed to improve prediction accuracy for green IoT. Specifically, a fuzzy rule-based classifier is combined with a dynamic weighting algorithm for improving classification accuracy. Moreover, the model is updated by incrementally learning the characteristics of data streams, which can effectively handle concept drift caused by data distribution changes in data streams. Experimental evaluations of UAV intrusion detection, smart buildings, and other datasets show that the proposed approach yields 2% higher area under the curve (AUC) and geometric mean (G-mean) than existing methods on UAV Detection and Occupancy datasets and 5% higher AUC and G-mean on five benchmarking datasets. For all datasets, the proposed approach yields 50% faster average training time than other methods.

Vehicle routing problem with time window (VRPTW) is the most representative optimization problem in logistics researches, which appears to be a multi-objective optimization problem in essence. However, most previous studies transform it into a single objective optimization problem to deal with. Also, the vehicle routing models in related studies focus on minimizing the cost. However, the efficiency (e.g., the waiting time due to early arrivals) and customer satisfaction equally important as the cost for the long-term development of logistics companies. In this article, a corresponding tri-objective VRPTW model for logistics dispatching is introduced, and a decomposition-based multi-objective variable neighborhood descent algorithm (D-VND) is proposed to solve the tri-objective VRPTW. In D-VND, the tri-objective VRPTW is decomposed into multiple single-objective sub-problems via a set of uniformly distributed weight vectors and then the route-based evolutionary operator is used to generate the offspring. Meanwhile, the objective-wise neighborhood operators are incorporated into the variable neighborhood descent method as local searches to improve qualities of sub-problems. Moreover, we adopt a heuristic initialization strategy and an external archive mechanism based on fast sorting and crowding distance methods to improve the performance of D-VND. Experimental results on benchmarking datasets with different scales show that D-VND outperforms two other representative algorithms regarding convergence and diversity and yields better solutions.