Data Transmission Tasks Research Articles

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform.

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

A Novel Optical Wireless Modulation Exploiting Time, Frequency, and Amplitude Divisions Enabling Link and Illumination Reliability

The simultaneous task of data transmission and illumination achievable with Visible Light Communication is continuing to raise the interest of research and industry community. The joint use of the same infrastructure for illumination and communication may enable new location-based services, by implementing sensing functionalities. In this paper, we propose a new multi-dimensional modulation scheme combining different techniques, in order to increase the spectral and energy efficiency, while meeting the optical power emission constraints. Specifically, Pulse Position Modulation Pulse Amplitude Modulation and Frequency Shift Keying are effectively combined in a joint fashion, in order to exploit time, amplitude and frequency information. The innovation of using a smart combination of those modulation formats is based on the exploitation of specific features of the different approaches. In particular, we combine time, amplitude and frequency features to enhance the robustness of the system, without sacrificing the data rate performance as for coded systems. The major implication of such approach is twofold. In this way, we grant a constant illumination level per symbol and, moreover, we outperform in terms of reliability coded modulations for the same level of spectral efficiency. Theoretical results, validated through experimental evaluations, demonstrate that the combined approach achieves very good performance.

Adaptive transmission construction based on polar codes

In actual communication scenarios, wireless transmission channels have the nature of heterogeneity. In order to ensure that data can be transmitted with high reliability and low delay, this paper designs a quasi-optimal polar code that can adapt to various channel environments, which called Q-polar code. Based on the polarization theory of polar codes and the channel state information (CSI), the index set of polar codes is designed and constructed to perform data transmission tasks. The simulation results show that the bit error rate (BER) performance of proposed scheme can be improved compared with traditional polar code, especially when the code length N is small and the code rate is large.

BMS: Bandwidth-aware Multi-interface Scheduling for energy-efficient and delay-constrained gateway-to-device communications in IoT

With the rapid growth of Internet of Things (IoT), gateways are being widely deployed and constantly deliver data traffic to their surrounding IoT devices, referred to as gateway-to-device or G2D communications. As the most commonly-accepted wireless technology, WiFi has been recommended for G2D communications due to its high data rate, high reliability, native IP compatibility, and good reusability of existing infrastructures. However, WiFi is inherently energy-hungry and thus could potentially shorten the lifetime of battery-powered IoT devices. Although numerous schemes have been designed to reduce the energy consumption of WiFi devices, they often suffer from long latency due to untimely wake-ups of the WiFi interface, especially under dynamic traffic. To address this, multi-interface protocols have been proposed to leverage the coexisting low-power ZigBee to enable timely wake-ups of the high-power WiFi interface. Despite high energy efficiency and low delay, these protocols have the silent assumption of unlimited ZigBee bandwidth, thus overlooking the optimization of ZigBee bandwidth efficiency. Hence, their performance is limited, when device density is high and/or ZigBee concurrently performs some other data transmission tasks. In this paper, we propose a Bandwidth-aware Multi-interface Scheduling (BMS) scheme, aiming to make efficient use of the limited ZigBee bandwidth to minimize IoT device’s WiFi energy consumption with constrained transmission delay in G2D communications. With BMS, the gateway utilizes the limited ZigBee bandwidth to dynamically alternate each device between two WiFi transmission modes for minimized energy consumption and bounded delay. A prototype of the proposed system is implemented and evaluated, and the testbed results show that under moderate traffic and delay bound, the energy consumption of BMS is 95.1% and 44.8% lower than those of the standard 802.11 power saving management and a state-of-the-art multi-interface scheme, respectively.

Recursive state estimation for a class of quantized coupled complex networks subject to missing measurements and amplify-and-forward relay

This paper investigates the algorithm design problem of recursive state estimation (RSE) for a class of complex networks (CNs) subject to quantized coupled parameter, missing measurements (MMs) and amplify-and-forward (AF) relay. In the node-to-node network channels, the signals before entering into the communication networks are quantized. In addition, a series of Bernoulli random variables is employed to model the phenomenon of MMs and an AF relay is deployed in the sensor-to-estimator network channels with the purpose of achieving the task of remote data transmission. A recursive state estimator is constructed such that, for all quantized coupled signal, MMs and AF relay, a state estimation error covariance (SEEC) upper bound (SEECUB) is presented and then the estimator gain (EG) is parameterized by optimizing the trace of SEECUB. Subsequently, a rigorous theoretical analysis is given to establish the monotonicity relationship between the trace of the minimized SEECUB and the probabilities of MMs. Finally, a simulation study is carried out for the proposed RSE approach to demonstrate the feasibility and validity of such state estimation strategy.

An Unequal Clustering Method Based on Particle Swarm Optimization in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) currently provide an important technical means of underwater communication, but there are difficulties in power updating or power replenishment because the sensor nodes work in an underwater environment. Therefore, energy consumption optimization has become the focus of research on UASNs. Node clustering is widely considered to be able to optimize network energy consumption. Although the current clustering-based routing method prolongs the network life cycle to a certain extent, some nodes may fail due to excessive energy consumption caused by excessive data transmission tasks, and the problems of high and uneven energy consumption still exist. To extend the life cycle of UASNs, this article proposes an unequal clustering method based on particle swarm optimization. Our method uses iterative updates to dynamically adjust the cluster size based on the remaining energy of the cluster head, the distance from the cluster head to the Sink node, and the number of times the cluster head forwards data between clusters to balance the cluster head load. The energy consumption of the whole path from the cluster head to the Sink node and the number of hops required are considered in the intercluster transmission phase. Simulation results show that this method can effectively reduce the network energy consumption.

Reliable task offloading mechanism based on trusted roadside unit service for internet of vehicles

With the introduction of the internet of vehicles (IoV) paradigm, the task transmission and processing capabilities of vehicular ad hoc networks (VANET) have been greatly improved. However, with the continuous increase of information processing quantity, vehicles need mobile edge computing technology to offload vehicle task data to the network edge to improve their limited task processing capabilities. Existing vehicle networking environments face various types of security issues. Roadside infrastructure undertakes the dual tasks of data processing and transmission. Therefore, its security problems have a more significant impact on the operation of the IoV system. Under malicious attacks, facing the vehicle task processing requirements, how to securely, reliably, and quickly allocate vehicle tasks to roadside units (RSU) to assist in processing is another challenge. This paper proposes a task offloading mechanism for IoV based on a trusted RSU service to deal with the above problems. Firstly, we construct a novel infrastructure trust management model and introduce social factors to strengthen RSU's security management. Secondly, we model the vehicle task offloading in conjunction with the reliability of RSU. This can improve the ability of the system to handle the task offloading when the system is under attack. Finally, we design extensive experiments to validate the vehicle task offloading algorithm to achieve reliable vehicle task processing, ensuring the achievement of system model goals. The simulation results show that the proposed scheme can still maintain a low delay and a high task success rate when the system is under attack.

Scheduling single-satellite observation and transmission tasks by using hybrid Actor-Critic reinforcement learning

Earth observation satellites(EOS) generate a large amount of observation data in intensive observation scenarios, while the ability of data storage on the EOS is limited. It makes integrating satellite observation and data transmission tasks for EOS imperative. This paper establishes a time-continuous model for the single EOS integrated scheduling problem, which considers data transmission and observation simultaneously. A hybrid Actor-Critic reinforcement learning method is adopted to solve the EOS integrated scheduling problem for a more efficient solution in intensive observation scenarios. Furthermore, the algorithm can flexibly determine the start and end time of the data transmission task. Experimental results show that the hybrid Actor-Critic reinforcement learning method deals with a large scale of problems with high efficiency and good results.

Development of Web-Based Flood Detection System and Flood Control Using Nodemcu

Internet of Things is a concept and method for remote control, monitoring, data transmission, and various other tasks. For some areas in Indonesia, flooding may be unavoidable, but this can be done to minimize the number of disaster losses by providing early warnings if there are indications that a flood will occur. This study will create an internet of things system to monitor the river’s water level and the sluice gate control system. The manufacture of this system will be made in the form of a prototype, to detect the water level will use ultrasonic sensors, and to drive the floodgates in this system will use a stepper motor. In this system, to determine the speed of movement of the openings of the sluice gate is determined based on the water level obtained as input data and calculated using fuzzy logic algorithms. The fuzzy logic algorithm here is one of the methods used in the field of control system studies. Testing on the fuzzy algorithm the author uses the fuzzy logic toolbox in the MATLAB application as a comparison to the fuzzy algorithm made on the prototype, from the results of testing the flood monitoring system runs perfectly by providing the appropriate output results and the average error of controlling the floodgates is 0.58%.

Local data commons: the sleeping beauty in the community of data commons

BackgroundPublic Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC.ResultsThis article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples.ConclusionsAlthough LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.

Dynamic SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis

Developing and fine-tuning software programs for heterogeneous hardware such as CPU/GPU processing platforms comprise a highly complex endeavor that demands considerable time and effort of software engineers and requires evaluating various fundamental components and features of both the design and of the platform to maximize the overall performance. The dataflow programming approach has proven to be an appropriate methodology for reaching such a difficult and complex goal for the intrinsic portability and the possibility of easily decomposing a network of actors on different processing units of the heterogeneous hardware. Nonetheless, such a design method might not be enough on its own to achieve the desired performance goals, and supporting tools are useful to be able to efficiently explore the design space so as to optimize the desired performance objectives. This article presents a methodology composed of several stages for enhancing the performance of dataflow software developed in RVC-CAL and generating low-level implementations to be executed on GPU/CPU heterogeneous hardware platforms. The stages are composed of a method for the efficient scheduling of parallel CUDA partitions, an optimization of the performance of the data transmission tasks across computing kernels, and the exploitation of dynamic programming for introducing SIMD-capable graphics processing unit systems. The methodology is validated on both the quantitative and qualitative side by means of dataflow software application examples running on platforms according to various different mapping configurations.

Design and Implementation of Low-Cost Distributed Tabletop Magnetic Particle Imaging System

Magnetic particle imaging (MPI) is a novel tomography model that mainly benefits from the nonlinear magnetization response of magnetic nanoparticles under a dramatically changing magnetic field. Low-cost and compact system construction is beneficial to the application of education, biomedical research, preclinical and clinical medicine. Conversely, many aspects of the production cost, infrastructure requirements, and distributed measurements can be prohibitively expensive for them. In this article, a low-cost, compact tabletop MPI scanner based on a field-programmable gate array (FPGA) framework with an Advanced RISC Machine (ARM) cores was proposed to address these issues. First, the proposed low-cost MPI scanner was realized by substituting discrete elements, which is affordable to cover the most economically underdeveloped regions. Besides, the ARM cores embedded in the FPGA framework which has the inherent advantages of low-power operation, are exploited to execute high-power real-time data transmission tasks instead of conventional X86. And a customized tabletop MPI system was constructed via the proposed architecture for advanced distributed MPI research. Simulative vessel experiments with commercial superparamagnetic nanoparticles (SPIONs) have been performed in the proposed system by vessel phantoms. The experimental images showed that the proposed system could achieved an image resolution of approximately 2 mm, which can distinguish spatial locations of brain veins (2–4 mm in diameter). Moreover, the experiment of distributed transmission showed that the proposed system could perform the collection and transmission of distributed particle signals based on the inherent user datagram protocol (UDP) network transmission protocol with an average round trip time (RTT) of 28.044 ms. Hence, the proposed system has the potential to apply the network cloud to get distributed biomedical information for remote education or diagnosis.

A Cloud-Computing-Based Portable Networked Ground Station System for Microsatellites.

Microsatellites have attracted a large number of scholars and engineers because of their portability and distribution characteristics. The ground station suitable for microsatellite service has become an important research topic. In this paper, we propose a networked ground station and verify it on our own microsatellite. The specific networked ground station system consists of multiple ground nodes. They can work together to complete data transmission tasks with higher efficiency. After describing our microsatellite project, a reasonable distribution of ground nodes is given. A cloud computing model is used to realize the coordination of multiple ground nodes. An adaptive communication system between satellites and ground stations is used to increase link efficiency. Extensive on-orbit experiments were used to validate our design. The experimental results show that our networked ground station has excellent performance in data transmission capability. Finally, the specific cloud-computing-based ground station network successfully completes our satellite mission.

Event-Triggered Extended Kalman Filtering Analysis for Networked Systems

In this paper, the filtering problem of nonlinear networked systems with event-triggered data transmission tasks is studied. To reduce the transmission of excessive measurement data in the bandwidth-limited network, a data transmission mechanism with event trigger is introduced to analyze the error behavior of the extended Kalman filter. We prove that the real estimation error and error covariance matrices can be determined by restricting the initial conditions appropriately. Finally, the effectiveness of the filtering algorithm is verified by simulation.

Joint Optimization of AoI, SINR, Completeness, and Energy in UAV-Aided SDCNs: Coalition Formation Game and Cooperative Order

As one of the most important representatives for the edge intelligence, Unmanned Aerial Vehicle (UAV) can be used to provide the intelligent edge service, such as performing data collection, computation and transmission tasks in the air-to-ground networks. In the considered Sensed Data Collection Networks (SDCNs), the UAV is in charge of collecting and computing the sensed data from mobile ground nodes, and multiple performance metrics need to be jointly optimized, including the collected data’s Age of Information (AoI) and the Signal to Interference plus Noise Ratio (SINR) at the UAV from mobile sensor nodes, the data collection areas’ completeness, and the mobile sensor nodes’ energy consumption. Due to the absence of such a joint optimization framework, this paper formulates the mobile sensing nodes deployment problem in the UAV-aided SDCNs. Afterwards, the formulated problem is transformed into a utility maximization problem from the perspective of Coalition Formation Game (CFG), where the orders, actions, rule, and stability in the coalitions are analyzed and proved. Then, two algorithms are put forward to reach the NE (Nash Equilibrium) of the CFG by determining the mobile sensor nodes’ coalitions and positions. Finally, extensive simulations are conducted with different parameters settings to evaluate the correctness and effectiveness of the proposed algorithms. Results have shown that the mobile sensor nodes, which adopt the proposed cooperative order to formulate the coalitions, could reach lower AoI, larger SINR, higher data completeness, and less energy consumption than the typical Pareto order.

A novel demodulation method for transmission using nitrogen–vacancy-based solid-state quantum sensor

Diamond based quantum sensing is a fast-emerging field with both scientific and technological significance. The nitrogen–vacancy (NV) center, a crystal defect in diamond, has become a unique object for microwave sensing applications due to its excellent stability, long spin coherence time, and optical properties at ambient condition. In this work, we use diamond NV center as atomic receiver to demodulate on–off keying (OOK) signal transmitted in broad frequency range (2 GHz–14 GHz in a portable benchtop setup). We proposed a unique algorithm of voltage discrimination and demonstrated audio signal transceiving with fidelity above 99%. This diamond receiver is attached to the end of a tapered fiber, having all optic nature, which will find important applications in data transmission tasks under extreme conditions such as strong electromagnetic interference, high temperatures, and high corrosion.

Safe navigation for UAV-enabled data dissemination by deep reinforcement learning in unknown environments

Unmanned aerial vehicles (UAVs) are increasingly considered in safe autonomous navigation systems to explore unknown environments where UAVs are equipped with multiple sensors to perceive the surroundings. However, how to achieve UAV-enabled data dissemination and also ensure safe navigation synchronously is a new challenge. In this paper, our goal is minimizing the whole weighted sum of the UAV's task completion time while satisfying the data transmission task requirement and the UAV's feasible flight region constraints. However, it is unable to be solved via standard optimization methods mainly on account of lacking a tractable and accurate system model in practice. To overcome this tough issue, we propose a new solution approach by utilizing the most advanced dueling double deep Q network (dueling DDQN) with multi-step learning. Specifically, to improve the algorithm, the extra labels are added to the primitive states. Simulation results indicate the validity and performance superiority of the proposed algorithm under different data thresholds compared with two other benchmarks.

Energy-Efficient UAV Assisted Secure Relay Transmission via Cooperative Computation Offloading

With the continuous development of B5G/6G cellular networks, the explosive growth of mobile Internet services and the resulting tremendous traffic demand have led to a significantly heavy pressure on how to efficiently satisfy the users’ traffic demands with guaranteed quality of services, especially the coverage at the cell-edge. Thanks to the advantage in flexible deployment, the unmanned aerial vehicle (UAV) has been considered as a promising approach for providing efficient data access for cell-edge users (CEUs), which usually suffer from a poor connection to the cellular base stations (BSs). In this paper, we consider a scenario of a group of CEUs with each CEU having both the task of delivering data to the cellular BS (i.e., the data-transmission task) and the task of completing a given computation workload (i.e., the computation-task). Meanwhile, we consider that the UAV serves as a dual-role (DUAV), i.e., the relay for forwarding CEUs’ data to the cellular BS, and the edge-server for processing the CEUs’ offloaded computation workloads. Taking into account that a malicious node which overhears the DUAV’s relay transmission, we investigate the secrecy driven UAV assisted relay transmission via cooperative computation offloading, in which we exploit the CEUs’ computation-offloading transmission to provide cooperative jamming to the malicious node who overhears the DUAV’s relay transmission. Specifically, we formulate a joint optimization of the DUAV’s hovering position, transmit-power and computation-rate allocation as well as the CEUs’ transmission duration and computation offloading, with the objective of minimizing the total energy consumption of the DUAV and all CEUs. Despite the non-convexity of the formulated joint optimization problem, we propose an efficient algorithm for finding the optimal solution. With the optimal offloading and transmission solutions provided by the polyblock approximation based algorithm (PA-Algorithm) under the given hovering point as a subroutine, we further optimize the DUAV’s hovering position by proposing a precoding-based Cross-Entropy algorithm (PBCE-Algorithm) for finding the DUAV’s optimal hovering position. In particular, by properly precoding the feasible region for hovering, our PBCE-Algorithm can outperform some existing algorithms in terms of accuracy and efficiency. To further reduce the computational complexity, we also propose a low-complexity algorithm for solving the joint optimization problem. Numerical results demonstrate the efficiency of our proposed algorithms and the performance advantage of our proposed secrecy driven UAV assisted relay transmission via cooperative computation offloading.