Search Results (41)

In the Internet of Things (IoT) era, Mobile Edge Computing (MEC) significantly enhances the efficiency of smart devices but is limited by battery life issues. Wireless Power Transfer (WPT) addresses this issue by providing a stable energy supply. However, effectively managing overall energy consumption remains a critical and under-addressed aspect for ensuring the network’s sustainable operation and growth. In this paper, we consider a WPT-MEC network with user cooperation to migrate the double near–far effect for the mobile node (MD) far from the base station. We formulate the problem of maximizing long-term computation rates under a power consumption constraint as a multi-stage stochastic optimization (MSSO) problem. This approach is tailored for a sustainable WPT-MEC network, considering the dynamic and varying MEC network environment, including randomness in task arrivals and fluctuating channels. We introduce a virtual queue to transform the time-average energy constraint into a queue stability problem. Using the Lyapunov optimization technique, we decouple the stochastic optimization problem into a deterministic problem for each time slot, which can be further transformed into a convex problem and solved efficiently. Our proposed algorithm works efficiently online without requiring further system information. Extensive simulation results demonstrate that our proposed algorithm outperforms baseline schemes, achieving approximately 4% enhancement while maintain the queues stability. Rigorous mathematical analysis and experimental results show that our algorithm achieves $\left[O(1/V),O\left(V\right)\right]$ trade-off between computation rate and queue stability. Full article

Synthetic Aperture Radar (SAR) has the advantage of continuous observation throughout the day and in all weather conditions, and is used in a wide range of military and civil applications. Among these, the detection of ships at sea is an important research topic. Ships in SAR images are characterized by dense alignment, an arbitrary orientation and multiple scales. The existing detection algorithms are unable to solve these problems effectively. To address these issues, A YOLOV8-based oriented ship detection and classification method using SAR imaging with lightweight receptor field feature convolution, bottleneck transformers and a probabilistic intersection-over-union network (R-LRBPNet) is proposed in this paper. First, a CSP bottleneck with two bottleneck transformer (C2fBT) modules based on bottleneck transformers is proposed; this is an improved feature fusion module that integrates the global spatial features of bottleneck transformers and the rich channel features of C2f. This effectively reduces the negative impact of densely arranged scenarios. Second, we propose an angle decoupling module. This module uses probabilistic intersection-over-union (ProbIoU) and distribution focal loss (DFL) methods to compute the rotated intersection-over-union (RIoU), which effectively alleviates the problem of angle regression and the imbalance between angle regression and other regression tasks. Third, the lightweight receptive field feature convolution (LRFConv) is designed to replace the conventional convolution in the neck. This module can dynamically adjust the receptive field according to the target scale and calculate the feature pixel weights based on the input feature map. Through this module, the network can efficiently extract details and important information about ships to improve the classification performance of the ship. We conducted extensive experiments on the complex scene SAR dataset SRSDD and SSDD+. The experimental results show that R-LRBPNet has only 6.8 MB of model memory, which can achieve 78.2% detection accuracy, 64.2% recall, a 70.51 F1-Score and 71.85% mAP on the SRSDD dataset. Full article

The Internet of Things (IoT) consists of millions of devices deployed over hundreds of thousands of different networks, providing an ever-expanding resource to improve our understanding of and interactions with the physical world. Global service discovery is key to realizing the opportunities of the IoT, spanning disparate networks and technologies to enable the sharing, discovery, and utilisation of services and data outside of the context in which they are deployed. In this paper, we present Decentralised Service Registries (DSRs), a novel trustworthy decentralised approach to global IoT service discovery and interaction, building on DSF-IoT to allow users to simply create and share public and private service registries, to register and query for relevant services, and to access both current and historical data published by the services they discover. In DSR, services are registered and discovered using signed objects that are cryptographically associated with the registry service, linked into a signature chain, and stored and queried for using a novel verifiable DHT overlay. In contrast to existing centralised and decentralised approaches, DSRs decouple registries from supporting infrastructure, provide privacy and multi-tenancy, and support the verification of registry entries and history, service information, and published data to mitigate risks of service impersonation or the alteration of data. This decentralised approach is demonstrated through the creation and use of a DSR to register and search for real-world IoT devices and their data as well as qualified using a scalable cluster-based testbench for the high-fidelity emulation of peer-to-peer applications. DSRs are evaluated against existing approaches, demonstrating the novelty and utility of DSR to address key IoT challenges and enable the sharing, discovery, and use of IoT services. Full article

Differential drive mobile robots, being widely used in several industrial and domestic applications, are increasingly demanding when concerning precision and satisfactory maneuverability. In the present paper, the problem of independently controlling the velocity and orientation angle of a differential drive mobile robot is investigated by developing an appropriate two stage nonlinear controller embedded on board and also by using the measurements of the speed and accelerator of the two wheels, as well as taking remote measurements of the orientation angle and its rate. The model of the system is presented in a nonlinear state space form that includes unknown additive terms arising from external disturbances and actuator faults. Based on the nonlinear model of the system, the respective I/O relation is derived, and a two-stage nonlinear measurable output feedback controller, analyzed into an internal and an external controller, is designed. The internal controller aims to produce a decoupled inner closed-loop system of linear form, regulating the linear velocity and angular velocity of the mobile robot independently. The internal controller is of the nonlinear PD type and uses real time measurements of the angular velocities of the active wheels of the vehicle, as well as the respective accelerations. The external controller aims toward the regulation of the orientation angle of the vehicle. It is of a linear, delayed PD feedback form, offering feedback from the remote measurements of the orientation angle and angular velocity of the vehicle, which are transmitted to the controller through a wireless network. Analytic formulae are derived for the parameters of the external controller to ensure the stability of the closed-loop system, even in the presence of the wireless transmission delays, as well as asymptotic command following for the orientation angle. To compensate for measurement noise, external disturbances, and actuator faults, a metaheuristic algorithm is proposed to evaluate the remaining free controller parameters. The performance of the proposed control scheme is evaluated through a series of computational experiments, demonstrating satisfactory behavior. Full article

This study employs unmanned aerial vehicles (UAVs) to detect mouse holes in grasslands, offering an effective tool for grassland ecological conservation. We introduce the specially designed CGT-YOLOv5n model, addressing long-standing challenges UAVs face, particularly the decreased detection accuracy in complex grassland environments due to shadows and obstructions. The model incorporates a Context Augmentation Module (CAM) focused on improving the detection of small mouse holes and mitigating the interference of shadows. Additionally, to enhance the model’s ability to recognize mouse holes of varied morphologies, we have integrated an omni-dimensional dynamic convolution (ODConv), thereby increasing the model’s adaptability to diverse image features. Furthermore, the model includes a Task-Specific Context Decoupling (TSCODE) module, independently refining the contextual semantics and spatial details for classification and regression tasks and significantly improving the detection accuracy. The empirical results show that when the intersection over union (IoU) threshold is set at 0.5, the model’s mean average precision (mAP_0.5) for detection accuracy reaches 92.8%. The mean average precision (mAP_0.5:0.95), calculated over different IoU thresholds ranging from 0.5 to 0.95 in increments of 0.05, is 46.2%. These represent improvements of 3.3% and 4.3%, respectively, compared to the original model. Thus, this model contributes significantly to grassland ecological conservation and provides an effective tool for grassland management and mouse pest control in pastoral areas. Full article

This article presents an antenna with compact and simple geometry and a low profile. Roger RT6002, with a 10 mm × 10 mm dimension, is utilized to engineer this work, offering a wideband and high gain. The antenna structure contains a patch of circular-shaped stubs and a circular stub and slot. These insertions are performed to improve the impedance bandwidth of the antenna. The antenna is investigated, and the results are analyzed in the commercially accessible electromagnetic (EM) software tool High Frequency Structure Simulator (HFSS). Afterwards, a two-port multiple–input–multiple–output (MIMO) antenna is engineered by orthogonalizing the second element to the first element. The antenna offers good value for mutual coupling of less than −20 dB. The decoupling structure or parasitic patch is placed between two MIMO elements for more refined mutual coupling of the proposed MIMO antenna. The resultant antenna offers mutual coupling of less than −32 dB. Moreover, other MIMO parameters like envelop correlation coefficient (ECC), mean effective gain (MEG), diversity gain (DG), and channel capacity loss (CCL) are also studied to recommend antennas for future applications. The hardware model is fabricated and tested to validate the results, which resembles software-generated results. Moreover, the comparison of outcomes and other important parameters is performed using published work. The outcome of this proposed work is performed using already published work. The outcomes and comparison make the presented design the best option for future 5G devices. Full article

The main task of remote sensing image target detection is to locate and classify the targets of interest in remote sensing images, which plays an important role in intelligence investigation, disaster relief, industrial application, and other fields. However, the targets in the remote sensing image scene have special problems such as special perspective, scale diversity, multi-direction, small targets, and high background complexity. In this paper, the YOLOv5 target detection algorithm is improved according to the above characteristics. Aiming at the problem of large target size span in remote sensing images, this paper uses K-Means++ clustering algorithm to eliminate the problem in which the original clustering algorithm is sensitive to initial position, noise, and outliers, and optimizes the instability caused by K-Means clustering to obtain preset anchor frames. Aiming at the redundancy of background information around the location of remote sensing image targets, the large number of small targets, and the denseness of targets, a double IoU-aware decoupling head (DDH) is introduced at the output end to replace the coupled yolo head, which eliminates the interference caused by different task sharing parameters. At the same time, the correlation between positioning accuracy and classification accuracy is improved by the IoU-aware method. The attention mechanism is introduced into the backbone network to optimize the detection and small target detection in complex backgrounds. The mAP of the improved YOLOv5 algorithm is improved by 9%, and the detection effect of small targets and dense targets is significantly improved. At the same time, the paper has achieved good results through the verification of the DIOR remote sensing data set and has also achieved good performance advantages in comparison with other models. Full article

The idea of green growth stresses the necessity for economic expansion while resolving environmental issues, notably climate change. The Internet of Things (IoT) and environmental regulations have the potential to support green growth. Therefore, this study intends to examine the empirical link between the IoT, environmental regulations, and green growth in China by utilizing the autoregressive distributed lag (ARDL) and quantile autoregressive distributed lag (QARDL) methods to analyze data from 1997 to 2021. Data are obtained from reputable local and international sources like the Organisation for Economic Co-operation and Development (OECD), World Development Indicators (WDI), the Energy Information Administration (EIA), and the National Bureau of Statistics of China. Findings derived from the baseline ARDL model prove that the IoT, environmental regulations, renewable energy consumption, and research and development (R&D) encourage long-run green growth. Likewise, the robust model also highlights that the internet, environmental policy stringency, renewable energy consumption, and R&D help encourage green growth. In the short run, environmental policy stringency and the internet are favorably linked to green growth in the robust model, and renewable energy consumption is favorably linked to green growth in the baselines model; however, environmental regulation is negatively linked to green growth. The findings from the QARDL analysis show that the impact of the IoT on promoting green growth is significant across all quantiles. On the other hand, the effects of environmental regulation are more pronounced at higher levels of green growth. These findings imply that policymakers should try to increase the role of digitalization in society by promoting the IoT and the internet to decouple economic growth and environmental pollution. Moreover, the digitalization policy should be supported by implementing strict environmental laws and regulations. Full article

Visible-light-based transmission application plays an important role in various types of sensor services for the Internet of Things (IoTs). However, in big data scenarios, current visible-light-based systems cannot achieve concurrent high-speed communication, low-speed communication, and positioning. Therefore, in this article, we propose a smart visible-light-based fusion applications system, named Fasys, to solve the above problem for the big data traffic with heterogeneity. Specifically, for low-speed data services, we propose a novel linear block coding and bit interleaving mechanism, which enhances the LED positioning accuracy and recovers the lost data bits in the interframe gap (IFG). For high-speed data services with traffic possessing burstiness, an elegant statistical reliability analysis framework in regard to latency is proposed based on martingale theory. The backlog martingale process is constructed. Leveraging stopping time theory, a tight upper bound of unreliability is obtained. An arrival abstraction and traffic allocation scheme is designed, which contributes to decouple the reliability requirement as the maximum supportable arrival load. Finally, we implement our Fasys system, and extensive experimental results show that our system can achieve consistent high-precision positioning and low-BER data communication for low-speed data services. And the proposed martingale-based traffic allocation scheme can achieve the provisioning of reliability in regard to the latency for high-speed data services. Full article

The International Thermonuclear Experimental Reactor (ITER) is a cutting-edge project that aims to develop a sustainable energy source by harnessing the power of nuclear fusion. One of the key challenges in the development of the ITER is the complex electrical grid that is required to support its operations. To address this challenge, a digital twin (DT) of the Central Solenoid (CS) Converter Power Supply grid has to be developed, and real-time simulation strategies have been proposed to monitor and study the performance of the ITER grid. Real-time simulation strategies allow for continuous feedback on the performance of the grid, enabling the quick identification and resolution of issues. However, it is not always possible to perform real-time simulation easily in a real-time simulator; therefore, specific strategies have to be implemented in the DT. This paper focuses on decoupling lines and explicit partitioning as solutions to allow the real-time simulation of the CS Converter Power Supply grid with two converter units (CUs), as required by the ITER Organization (IO). As will be shown later in this article, the proposed approach is progressive and applicable to a more complex grid with multiple CUs. The results concerning the proposed strategies will be analyzed and discussed in terms of real-time performance. Full article

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications. Full article

In this paper, we propose a method that uses the idea of decoupling and unites edge information for semantic segmentation. We build a new dual-stream CNN architecture that fully considers the interaction between the body and the edge of the object, and our method significantly improves the segmentation performance of small objects and object boundaries. The dual-stream CNN architecture mainly consists of a body-stream module and an edge-stream module, which process the feature map of the segmented object into two parts with low coupling: body features and edge features. The body stream warps the image features by learning the flow-field offset, warps the body pixels toward object inner parts, completes the generation of the body features, and enhances the object’s inner consistency. In the generation of edge features, the current state-of-the-art model processes information such as color, shape, and texture under a single network, which will ignore the recognition of important information. Our method separates the edge-processing branch in the network, i.e., the edge stream. The edge stream processes information in parallel with the body stream and effectively eliminates the noise of useless information by introducing a non-edge suppression layer to emphasize the importance of edge information. We validate our method on the large-scale public dataset Cityscapes, and our method greatly improves the segmentation performance of hard-to-segment objects and achieves state-of-the-art result. Notably, the method in this paper can achieve 82.6% mIoU on the Cityscapes with only fine-annotated data. Full article

Backscatter communication is a promising technology for the Internet of Things (IoT) systems with low-energy consumption, in which the data transmission of the backscatter devices relies on reflecting the incident signal. However, limited by the low power characteristic of the reflected signal from backscatter devices, achieving efficient data collection for the widely distributed backscatter devices is a thorny problem. Considering that unmanned aerial vehicles (UAVs) have flexible deployment capability, employing UAVs in a backscatter communication network can achieve feasible data collection for backscatter devices. In this paper, we consider a UAV-aided backscatter system and introduce Non-orthogonal multiple access (NOMA) to enable the UAV to collect signals from multiple backscatter devices simultaneously. We formulate an optimization problem to maximize the communication throughput of the considered system by jointly designing the backscatter device matching, the trajectory of the UAV, and the reflection coefficients of the backscatter devices, which is a non-convex optimization problem and challenging to solve. Hence, we decouple the original problem into three sub-problems and propose an efficient iterative algorithm based on Block Coordinate Descent (BCD) to solve them. In detail, a game-based matching algorithm is designed to ensure the transmission needs of remote backscatter devices. The UAV trajectory and reflection coefficients of backscatter devices are optimized through the Successive Convex Approximation (SCA) algorithm and relaxation algorithm. By iterative optimization of the sub-problems, the original problem is solved. The simulation results show that the proposed scheme can obtain a significant throughput gain compared to benchmark schemes. Full article

With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently. Full article

In several MIMO system applications, the deviations of some output performance variables from their nominal values are required to be controlled independently, while the other performance variables are required to remain at their nominal value. This problem, named noninteracting control with simultaneous partial output zeroing, is important in the case of the common design of multi-model systems. To this end, the problem of a common noninteracting control with simultaneous common partial output zeroing is formulated. The present paper aims to develop a solution to the problem of multi-model normal linear time-invariant systems via regular and static measurement output feedback. The present approach follows the method developed for the solution of the common I/O decoupling problem. The main results of the paper are the introduction and the formulation of the problem at hand, the establishment of the necessary and sufficient conditions for its solvability, and the derivation of the respective general solution of the controller matrices. For the resulting closed-loop system, the additional design requirement of approximate command following a simultaneous I/O stabilizability is studied using a composite norm 2 type cost function and a metaheuristic algorithm for the derivation of the free parameters of the controller. The present results are illustrated through a numerical example of a nonlinear process with two operating points. Moreover, all the above results are successfully applied to the two-model description of a robot-tracked UGV, using a common controller feeding back measurements of the motor currents and the orientation of the vehicle. Full article