Data Queue Research Articles

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

Energy‐efficient adaptive dependent task scheduling in cooperative vehicle‐infrastructure system

AbstractIn the cooperative vehicle‐infrastructure system (CVIS), due to its computation limitation, vehicles are difficult to handle computing‐intensive delay‐sensitive tasks, so offload tasks to roadside unit (RSU) become popular. Due to the complexity of vehicles’ tasks and tasks generated by different vehicles have different delay constraints, minimize energy consumption of RSUs under task dependence and delay constraints is challenging. This paper defines the task priority queuing criterion for the task priority division problem, proposes a task scheduling strategy for energy‐packet queue length tradeoff (TSET) in CVIS under RSUs distributed task scheduling problem and establishes the vehicle speed state model, task model, data queue model, task computing model and energy consumption model. After Lyapunov optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verify that TSET reduces the average energy consumption of roadside units and ensures the stability of the data queue under task dependence and deadline conditions.

Resource management for computational offload in MEC networks with energy harvesting and relay assistance

In mobile edge computing (MEC) networks, the integrated application of energy harvesting (EH) and relay cooperation is a promising technology, which ensures the real-time performance of the system and computing power supply. Relay and EH cooperation also expand communication range and extend the battery life of energy-constrained devices. However, in wireless powered and relay-assisted MEC networks, it is challenging to jointly dispatch energy, and computing resources to coordinate heterogeneous performance requirements. To fill this gap, this paper examines the fundamental compromise between utility energy efficiency (UEE) and stable queue length in wireless powered and relay-assisted MEC network systems, and the amount of data transferred for each unit of energy is called UEE. The data queue and energy queue models are introduced and the constraints of stability of two queues in long term are included in the formulated problem. To tackle the fractional and nonconvex optimization problems, the Dinkelsbach method is utilized. Since optimization problems change from time to time and the long-term averaging queue stability is considered, the Lyapunov optimization theory is introduced to convert the non-convex problems into the solvable one. An online resource offloading allocation algorithm is proposed to determine the solution. In addition, the algorithm implements the control parameter V to achieve the compromise between UEE and stable queue length, while the impact of various parameters are also revealed on system performance.

Real-time processing and optimization strategies for IoT data streams

Abstract With the development of industrial IoT and the arrival of smart manufacturing, the field of edge computing has gained more and more attention. However, traditional industrial computing scenarios relying on industrial clouds make data latency a greater challenge. In this paper, for the contradiction between edge devices and task resource allocation encountered in edge computing scenarios in smart manufacturing, we propose an industrial internet task scheduling model for smart manufacturing and introduce a scheduling node state matrix to realize the state management of each scheduling subtask. Aiming at the problem of multiple tasks seizing resources in a complex, intelligent manufacturing environment, the study combines the caching mechanism to realize the task offloading computational processing of order scheduling, in which the caching mechanism is used to solve the problem of computational resource limitations at the edge. It is found through simulation that when the computational task factor ξk =2 is larger, more offloading power is allowed to be transmitted to the edge ser ver for computation. For computational tasks with smaller task factor ξk , the device tends to allocate more computational rate to that computational task. Eventually the data queue length will be continuously reduced and the data queue is concentrated in the interval of very small values, this result verifies that the task scheduling algorithm is able to perform task scheduling efficiently and reduce the latency.

Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection

Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection

Energy-Efficient and Low-Complexity Transmission Control With SWIPT-NOMA for Green Cellular Networks

In this study, we consider an energy-efficient and low-complexity transmission control in a SWIPT-NOMA-based green cellular network (GCN) that consists of a green base station (GBS) and green users (GUEs). First, we formulate a non-convex problem that minimizes transmit power consumption while supporting minimum downlink user service rate, downlink data queue stability, and user battery availability. Then, we transform the problem into a Lyapunov-drift-penalty minimization problem, which can determine a new resource allocation scheme that balances transmit power consumption and queue stability. Second, the Lyapunov-drift-penalty problem is decomposed into subchannel assignment, power allocation, and power splitting (PS) ratio control problems. The subchannel assignment problem is solved using a matching theory-based low-complexity algorithm. The power allocation and PS ratio control problems are solved using the alternating optimization (AO) approach and bisection method. This decomposed subproblem-based control also enables distributed control between the GBS and GUEs. Third, we prove the convergence, optimality, and polynomial computation complexity of the proposed algorithm. Lastly, we demonstrate that the proposed control outperforms the benchmark controls regarding transmit power consumption and the achievable rate. Owing to the optimality and low complexity, the proposed control can be efficiently applied to large-scale and distributed GCNs in sixth-generation environments.

Noninvasive Radiation Markers Based on Lung Cancer CT Images and Dosimetry Features to Predict the Immune Response to Radiotherapy in Tumor Microenvironment

For lung cancer patients with complex conditions, doctors need to determine treatment plan in combination with changes of immune response during radiotherapy. It was urgent to find a strategy to predict immune response to intervene the treatment at an early stage. The radiotherapy process and tumor microenvironment status of 105 lung cancer patients were tracked and recorded in our hospital from 2019 to 2022, public database TCGA-LUAD/LUSC queue of TCIA image data was downloaded as external verification set. Data was analyzed according to the following methods: First, the 3D slicer software was used to process the CT imageomics data, and the doseomics information was calculated by DVHmetrics package from R software. Then Python was brought in to develop a non-invasive radiation scoring system through machine learning methods (T-test, LASSO, random forest and Xgboost). Finally, combined with the clinical information related to the patient's immune response, a nomogram was made to study the correlation between the score markers and the ratio of CD4 cells/CD8 cells in the tumor microenvironment, the prognosis of lung cancer, as well as the incidence of radiation pneumonia. The radiation markers score formula was obtained with CT imageomics and DVH dosimics features, which achieved an AUC of 0.67-0.75 in predicting CD4/CD8 condition in tumor microenvironment after radiotherapy. It was worth noticing that in predicting the prognosis of lung cancer, it was difficult to distinguish between the radiation score and the patient's survival status (P>0.05). However, we found that the radiation pneumonitis of lung cancer patients in the low score group (42.6%) was significantly higher than that in the high score group (13.4%). Radiation markers score was a non-invasive method to evaluate the immune response of patients with lung cancer, which can be used to predict the incidence of radiation pneumonitis.

Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks

In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all GUs' data, while the UAVs' network formation optimizes the multi-hop UAV network topology to minimize the energy consumption and transmission delay. The joint network formation and trajectory optimization is solved by a two-step iterative approach. Firstly, we devise the adaptive network formation scheme by using a heuristic algorithm to balance the UAVs' energy consumption and data queue size. Then, with the fixed network formation, the UAVs' trajectories are further optimized by using multi-agent deep reinforcement learning without knowing the GUs' traffic demands and spatial distribution. To improve the learning efficiency, we further employ Bayesian optimization to estimate the UAVs' flying decisions based on historical trajectory points. This helps avoid inefficient action explorations and improves the convergence rate in the model training. The simulation results reveal close spatial-temporal couplings between the UAVs' trajectory planning and network formation. Compared with several baselines, our solution can better exploit the UAVs' cooperation in data offloading, thus improving energy efficiency and delay performance.

ADCL: Adversarial Distilled Contrastive Learning on lightweight models for self-supervised image classification

With the development of modern sensors, numerous images are collected in edge application scenarios; however, their utilization is quite expensive because a massive effort is required to label them for further usage. Self-supervised learning, with no need for labeled data, shows great potential in this context; however, notable performance degradations can be observed when training lightweight networks which are essential in edge implementation. We propose an effective distillation method called Adversarial Distilled Contrastive Learning (ADCL) to mitigate this issue. Specifically, we introduced knowledge distillation into self-supervised learning to transfer underlying feature clustering relations from teacher models to shallow models. We adopted an online-updated rather than a pretrained teacher model to realize convenient implementation to specific data domains. An adversarial loss item was introduced to alleviate unstable optimization caused by an online trained teacher by forcing the teacher model to find feature relations beyond the recognition of the student model. Compared with other self-supervised knowledge distillation methods that maintain data queues consisting of positive and negative examples, the asymmetric contrastive learning method was employed to further relieve the memory bottleneck during training. The experimental results prove the effectiveness of our method. When ResNet-50 is used as a teacher to teach ResNet-18 on ImageNet, ADCL achieves top-1 accuracies of 60.3% , which surpasses other knowledge distillation methods with online teachers and is comparable to approaches using pretrained teachers and data queues.

Energy-efficient and delay-guaranteed routing algorithm for software-defined wireless sensor networks: A cooperative deep reinforcement learning approach

Wireless sensor networks (WSNs) have undergone rapid development but still suffer from energy shortage issues. Meanwhile, existing energy-efficient routing approaches lack a cross-layer cooperation mechanism and cannot offer intelligent decision-making capabilities for resource-constrained nodes. In this paper, we design an intelligent routing algorithm with adaptive duty cycling for application in software-defined WSNs (SDWSNs) to maximize the network lifetime and data transmission reliability subject to a long-term data queue stability constraint. In particular, we introduce Lyapunov optimization to decouple the multistage routing optimization problem into per-frame deterministic problems with delay guarantee. To optimize the routing algorithm and adaptively adjust the duty-cycling mechanism, we propose a cooperative deep reinforcement learning (CDRL) model. Moreover, we develop a centralized training and distributed execution framework to decouple the model training and inference processes, which provides the capability of intelligent routing decision-making for resource-constrained nodes with low computational complexity. In addition, we design a new hybrid routing metric and a unique feature construction and extraction scheme to capture fine-grained information from our hybrid routing metric. The proposed CDRL algorithm adopts a cooperative and iterative model optimization strategy to achieve highly reliable data transmission and optimal long-term network performance. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of network lifetime, packet delivery ratio, and statistical delay performance under various scenarios compared to the EBR-RL, EA-AGG, and QLRP algorithms.

Data Offloading Enabled by Heterogeneous UAVs for IoT Applications Under Uncertain Environments

With the continuous expansion of the Internet of Things (IoT) application scope, there are growing IoT scenarios that lack the coverage of wireless communication networks have the demand for data offloading. Efficient data transmission has been the main concern of these applications. Thus, data offloading within the limited communication environment has become a hotspot in both industry and academia. Since unmanned aerial vehicles (UAVs) can move across regions to make up for the communication gap caused by the loss of wireless communication networks, a lot of in-depth studies on UAV-enabled data offloading have been conducted. Nevertheless, few studies to date consider the uncertain user status and the heterogeneous UAV capabilities, which is however more practical and needs more attention. In this article, we propose an innovative framework to dynamically estimate user status information and determine the UAV scheduling strategy. On this basis, the heterogeneous UAV-enabled data offloading is modeled as a constrained multiobjective optimization problem, whose purpose is to lower the user data queue length while extending the working time of the UAV. Moreover, a differential evolution-based dynamic objective approximation method—RUDDER is proposed to solve the constrained multiobjective optimization problem. Through rigorous mathematical proof, we prove that RUDDER can consistently guide the population to approach the optimization solution with polynomial-level time complexity. To verify the effectiveness of the proposed RUDDER, extensive experiments are conducted to compare it with five comparison algorithms. The experimental results demonstrate the superiority of the RUDDER in terms of energy saving, time efficiency, and adaptability.

Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

The mobile and flexible unmanned aerial vehicle (UAV) with mobile edge computing (MEC) can effectively relieve the computing pressure of the massive data traffic in 5G Internet of Things. In this paper, we propose a novel online edge learning offloading (OELO) scheme for UAV-assisted MEC secure communications, which can improve the secure computation performance. Moreover, the problem of information security is further considered since the offloading information of terminal users (TUs) may be eavesdropped due to the light-of-sight characteristic of UAV transmission. In the OELO scheme, we maximize the secure computation efficiency by optimizing TUs' binary offloading decision and resource management while guaranteeing dynamic task data queue stability and minimum secure computing requirement. Since the optimization problem is fractionally structured, binary constrained and multi-variable coupled, we first utilize the Dinkelbach method to transform the fractionally structured problem into a tractable form. Then, OELO generates the offloading decision based on deep reinforcement learning (DRL) and optimizes the resource management in an iterative manner through successive convex approximation (SCA). Simulation results show that the proposed scheme achieves better computing performance and enhances the stability and security compared with benchmarks.

Dynamic Offloading and Trajectory Control for UAV-Enabled Mobile Edge Computing System With Energy Harvesting Devices

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computation service to distributed devices in the absence of terrestrial infrastructure. In this paper, we consider a UAV-enabled MEC system serving multiple energy harvesting (EH) devices, where the energy and task data arrive at the users stochastically. Without any future knowledge of task data and energy arrivals, our objective is to design an online algorithm to jointly optimize the UAV energy and task processing rate, meanwhile satisfying the long-term data queue stability. We formulate the problem as a multi-stage stochastic programming and propose an online algorithm, named PLOT, based on perturbed Lyapunov optimization technique. In particular, PLOT resolves the coupling effect of sequential control actions, and converts the stochastic problem into per-slot deterministic optimization problem. For each per-slot problem, we design a low-complexity algorithm to solve it. We show that the PLOT algorithm can derive a feasible solution to the original problem and achieve an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[O(1/V),O(V)]$ </tex-math></inline-formula> trade-off between the system cost and the data queue length. Simulation results justify our analysis and demonstrate that the PLOT algorithm achieves better performance in terms of system utility and maintains queue stability that is not achieved by other benchmark methods.

Optimizing Trajectory and Dynamic Data Offloading Using a UAV Access Platform

The use of unmanned aerial vehicles (UAV) as an integrated sensing and communication platform is emerging for surveillance and tracking applications, especially in large infrastructure-deficient environments. In this study, we develop a multi-UAV system to collect data dynamically in a resource-constrained context. The proposed approach consists of an access platform called Access UAV (A_UAV) that stochastically coordinates the data collection from the Inspection-UAVs (I_UAVs) equipped with a visual sensor to relay the same to the cloud. Our approach jointly considers the trajectory optimization of A_UAV and the stability of the data queues at each UAV. In particular, the Distance and Access Latency Aware Trajectory (DLAT) optimization for A_UAVs is developed, which generates a fair access schedule for I_UAVs. Moreover, a Lyapunov-based online optimization ensures the system stability of the average queue backlogs for dynamic data collection while minimizing total system energy. Coordination between I_UAV and A_UAV is achieved through a message-based mechanism. The simulation results validate the performance of our proposed approach against several baselines under different parameter settings.

Distributed Task Scheduling in Serverless Edge Computing Networks for the Internet of Things: A Learning Approach

By delegating the infrastructure management, such as provisioning or scaling to third-party providers, serverless edge computing has recently been widely adopted in several applications, especially Internet of Things (IoT) applications. Task scheduling is a critical issue in serverless edge computing as it significantly impacts the quality of user experience. In contrast to the centralized scheduling in the cloud center, serverless edge task scheduling is more challenging due to the heterogeneous and resource-constrained nature of edge resources. This article aims to study the distributed task scheduling for the IoT in serverless edge computing networks, in which heterogeneous serverless edge computing nodes are rational individuals with interests to optimize their own scheduling utility while the nodes only have access to local observations. The task scheduling competition process is formulated as a partially observable stochastic game (POSG) to enable serverless edge computing nodes to noncooperatively schedule tasks and allocate computing resources depending on their locally observed system state, which takes into account the associated task generation state, data queue state, communication channel state, and previous computing resource allocation state. To solve the proposed POSG and deal with the partial observability, a multiagent task scheduling algorithm based on the dueling double deep recurrent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (D3RQN) method is developed to approximate the optimal task scheduling and resource allocation solution. Finally, extensive simulation experiments are conducted to validate the effectiveness and superiority of the proposed scheme.

Queue-Aware Access Prioritization for Massive Machine-Type Communication

One of the pivotal services of the fifth generation (5G) of cellular technology is massive Machine-type Communications (mMTCs), which is intended to support connecting high density of machine-type devices (MTDs). Random access channel of long-term evolution (LTE)/LTE advanced needs to be modified in order to support the large simultaneous arrival of MTDs. 3GPP suggested access class barring (ACB) as a mechanism to inhibit network congestion in the mMTC or massive Internet of Things (IoT) scenario. Consequently, in devices that are repeatedly ignored by ACB, the queue of data packets keeps growing. In storage-constrained IoT nodes with limited buffer, this may lead to packet drop due to buffer overflow, causing a decline in the overall throughput of the system. To address this issue, a novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB. To study the queue build-up at each MTC device, a node-centric analysis of ACB in the buffer-constrained scenario is performed using a 2-D Markov chain. It is shown that the proposed QPAC scheme, with optimal model parameters obtained by maximizing overall system utility, offers up to 70% gain in throughput compared to the nearest competitive dynamic ACB scheme.

Online Cognitive Data Sensing and Processing Optimization in Energy-Harvesting Edge Computing Systems

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

Energy-Efficient Online Data Sensing and Processing in Wireless Powered Edge Computing Systems

Wireless powered multi-access edge computing (MEC) has emerged as a promising paradigm to enable high-performance computation of energy-constrained wireless devices (WDs) in internet of things (IoT) systems. However, to overcome the severe path loss of both energy transfer and data communications, wireless powered MEC suffers from high operating power consumption. To achieve sustainable and economic system operation, this paper focuses on developing energy-efficient online data processing strategy for wireless powered MEC systems under stochastic fading channels. In particular, we consider a hybrid access point (HAP) transmitting RF energy to and processing the sensing data offloaded from multiple WDs. Under an average power constraint of the HAP, we target at maximizing the long-term average data sensing rate of the WDs while maintaining task data queue stability. To this end, we formulate a multi-stage stochastic optimization problem to control the energy transfer and task data processing in sequential time slots. Without the knowledge of future channel fading, it is very challenging to determine the sequential control actions that are tightly coupled by the battery and data buffer dynamics. To solve the problem, we propose a Lyapunov optimization-based online algorithm named LEESE, which decomposes the multi-stage stochastic problem into per-slot deterministic optimization problems. We show that each per-slot problem can be equivalently transformed into a convex optimization problem. To facilitate online implementation in large-scale MEC systems, instead of solving the per-slot problem with off-the-shelf convex algorithms, we propose a block coordinate descent (BCD)-based method that produces a close-to-optimal solution in less than 0.04% of the computation delay. Simulation results demonstrate that the proposed LEESE algorithm can provide 18% higher data sensing rate than the representative benchmark methods considered, while incurring sub-millisecond computation delay suitable for real-time control under fading channel.

Optimization on information freshness for multi‐access users with energy harvesting cognitive radio networks

AbstractFor the multi‐access channel wireless sensor network scenario with limited channel resources, the transmission of real‐time information needs to consider the channel environment, spectrum utilization and the freshness of information. First, we present a cognitive network model where secondary user (SU) shares the primary user (PU) spectrum through the underlay scheme. The sensor nodes with energy harvesting (EH) capabilities dynamically monitor changes in the surrounding environment and transmit status updates to the common secondary destination node. Considering the stability of the data queue in the SU as well as the working state and stable service interference constraints of PU, an optimization problem of minimizing the average AoI and average peak AoI (PAoI) of the EH node is proposed. Second, two policies are used to solve the above problems, including the decentralized probabilistic‐random‐access (PRA) policy and the drift plus penalty (DPP) policyin. In PRA policy, two SU nodes make independent transmission decisions based on corresponding probability distributions, and in DPP policy, the Lyapunov optimization framework is used to optimize scheduling decisions in the timeslots for optimizing the age of data packets. The simulation results show that the optimization of AoI under the DPP policy is more significant than the PRA policy, which can effectively improve the timeliness and freshness of data packets.

Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.