In internet of things (IoT) networks, resource-constrained devices can offload data to mobile edge computing (MEC) servers for processing. Nevertheless, offloading incurs additional energy consumption, making energy-efficient offloading a critical issue. Furthermore, the mismatch of transmission and computation traffic rates further exacerbates energy inefficiency. Additionally, ensuring quality of service (QoS) requires not only meeting the per-slot processed data volume thresholds of devices but also satisfying their long-term energy consumption limits imposed by limited battery capacity. The coexistence of these heterogeneous constraints renders algorithm design more challenging. In this paper, we propose a safe deep reinforcement learning (Safe-DRL)-based resource allocation strategy for traffic matching of transmission and computation in QoS-guaranteed MEC-IoT networks, aiming to maximize energy efficiency. Specifically, we formulate an energy efficiency maximization problem that captures the interdependence among transmission, buffering, and computation traffic, subject to both data processing and energy consumption constraints. To reduce complexity, closed-form solutions for a subset of variables are derived via mathematical analysis. Subsequently, we develop a Safe-DRL algorithm, termed action projection augmented Lagrangian soft actor-critic (APAL-SAC), which integrates a penalty-based action projection mechanism to enforce per-slot constraints and a Lagrangian dual method to ensure long-term constraint satisfaction. Simulation results demonstrate the effectiveness of APAL-SAC.

The minimum vertex cover (MVC) problem is a classic NP-hard combinatorial optimization problem with extensive real-world applications. In this paper, we propose an efficient local search algorithm, InfVC, to solve the MVC in massive graphs, which comprises three ideas. First, we introduce an inference-driven optimization strategy that explores better feasible solutions through inference rules. Second, we develop a structural-determined perturbation strategy that is motivated by the structure features of high-quality solutions, prioritizing high-degree vertices into the candidate solution to guide the search process to some potential high-quality search area. Third, we design a self-adaptive local search framework that dynamically balances exploration and exploitation through a perturbation management mechanism. Extensive experiments demonstrate that InfVC outperforms all the state-of-the-art algorithms on almost massive instances.

Computing tasks offloaded from user devices (UDs) can be carried out by one or more mobile edge computing (MEC) servers to alleviate the computing burden of UDs. The incentive is needed to encourage MEC servers to provide their computing services to other network nodes. In this article, inspired by the fact that bargaining games have the available features of incentive, self-enforcement, and satisfaction for all participants, we propose a two-level bargaining-based incentive mechanism for task offloading and collaborative computing in MEC-enabled networks. In the first-level bargaining between UDs and local MEC server (LMECS), both UDs and LMECS try to maximize their respective offloading utilities, which are all defined as a saved-cost function considering the time and energy consumption of task execution, and computing service fees. The task offloading decision, uplink transmitting power of UDs, computing resource allocation of LMECS, and the fees paid by UDs to LMECS are jointly optimized. When large computing tasks are offloaded to LMECS, which results in LMECS overload, the second-level bargaining is proposed to achieve a computing load balance of LMECS and maximize the respective collaboration utilities of LMECS and collaborative MEC server group (CMECG), in which the optimized normalized fees paid by LMECS to CMECG for additional computing resources are obtained. The first-level and the second-level bargainings are proved to be quasi-concave and concave, respectively, and each has a unique Nash bargaining solution (NBS). The simulation results show that the proposed method gets better performance than benchmark methods.

Unlicensed cognitive-radio (CR)-assisted Internet of Vehicles (IoV) users can access licensed providers’ radio spectrum and concurrently utilize the dedicated channel for data transmission in vehicular communication. Optimizing channel access in cognitive IoV networks can help maximize available spectrum resources. This article proposes a novel sensing and communication integrated framework, dubbed as the CR-assisted IoV network (CRAV-Net), using a cluster-based hybrid optimization approach with adaptive congestion-aware modeling for dynamic high-mobility vehicular networks in an urban city context. In CRAV-Net, intelligent hybrid learning spectrum agents are introduced, which perform spectrum sensing (SS) using a deep learning (DL) model. It dynamically learns the multilevel spatial and temporal graphical features from input spectrograms through layer-by-layer propagation. It efficiently predicts the spectrum occupancy in the primary spectrum, without a priori knowledge of the radio environment. Then, to assign the vacant channels to the secondary vehicles, a support vector machine classifier is trained based on several learning features, including the vehicle stay time, vehicle density, and network capacity, to select the optimal resource route. The proposed framework achieves an overall accuracy of 99.74% in SS using the custom data set, outperforming state of the art by 12.60% at −25-dB signal-to-noise ratio. In addition, it brings a performance gain of 0.81% in SS accuracy when evaluated on real-world signals. Furthermore, in optimal network node allocation, the proposed framework achieves a mean accuracy of 98.45%, outperforming the existing methods by 0.63% and 18.32% in terms of accuracy and allocation time, respectively.

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent‐based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady‐state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large‐scale sensing delay in the CR network.

Tandem repeats, which occur frequently in genomes, are related to gene regulatory functions and various diseases. In this paper, an efficient algorithm for tandem repeat detection is proposed. In our method, the spectrogram of a DNA sequence is analyzed based on the autoregressive model. Then, significant peaks in the spectrogram are selected, and the corresponding regions in the DNA sequence are analyzed to search for tandem repeats. Experiment results show that our method has a superior performance in comparison with other algorithms.

UNLABELLED: Repetitive DNA sequences play paramount biological roles, such as gene variation and regulatory functions on gene expressions. Until now, detection of various kinds of DNA repeats accurately is still an open problem. In this article, we propose a new method and a visualization tool for detecting DNA repeats in a 2D plane of location and frequency by using optimized moving window spectral analysis. The spectrogram can display the general distribution of repetitive sequences while showing the repeat period, length and location without any prior knowledge. Experimental results demonstrate that our method is accurate and robust even under the condition of excessive mutating and interleaving. AVAILABILITY: Available on http://www.hy8.com/~tec/sw01/omwsa01.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel adaptive inverse synthetic aperture radar (ISAR) imaging technique is proposed for targets with nonuniform motion. The proposed algorithm is referred to as the generalized range-Doppler (GRD) ISAR imaging technique and is based on the fractional Fourier transform (FRFT). By utilizing this technique, clear ISAR imaging can be achieved for nonuniformly moving targets without involvement of complex motion compensation. Simulation results have proved that the new algorithm is robust and also computationally efficient as compared with previously reported algorithms such as joint time-frequency (JTF) imaging.