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With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements. [ABSTRACT FROM AUTHOR]

Accurate measurement of hand forces in motorbike riding is highly desirable for studies of safe riding. In this paper, we implement a force-sensing glove system for measuring real-time hand forces during motorbike riding with the aim of giving feedback to the riders. It consists of a pair of gloves with tactile sensors suitably mounted and configured for data acquisition via a wireless smartphone. A novel calibration method is developed for dynamic calibration considering force measurement in natural operation and environments. Consequently, a series of data classification algorithms ensure accurate hand performance feedbacks for motorbike riders. The feedback data could potentially alert the riders to predict and prevent accidents. Validation tests demonstrate that this force-sensing glove system has a strong potential as a tool for hand performance monitoring in real environment. [ABSTRACT FROM AUTHOR]

Balanced energy consumption in WSNs is the key to maximize network lifetime. After analyzing laws of energy consumption of WSNs, a heterogeneous node deployment model based on area topology control is proposed in this paper. It utilizes predetermined heterogeneous node deployment to balance area energy consumption ratio and the topology control algorithm based on area energy consumption ratio to maintain dynamic equilibrium energy consumption. Simulation results show that our model takes a superior performance on maintaining network coverage, balancing energy consumption, and maximizing network lifetime. Compared with similar WSNs deployment models, our model can be more efficient to prolong network lifetime and has advantages in reducing dependence on deployment accuracy, balancing dynamic energy consumption, and improving lifetime in poor network environment. [ABSTRACT FROM AUTHOR]

The peer-to-peer-based video resource dissemination is important for handling extreme conditions such as flash crowds which severely break the balance between supply and demand of video content and bring negative effects for quality of service (QoS). In this paper, we propose a novel interest detection-based video dissemination algorithm under flash crowd in mobile ad hoc networks (IDVD). IDVD classifies the user behavior of requesting video content in terms of the popularities and playback time of video and considers the demand of popular video as the cause of flash crowd. IDVD makes use of the prediction of necessary bandwidth and period time of intensive request to be aware of period time and scale of flash crowd. IDVD employs a resource dissemination algorithm which can fast discover interested nodes and achieve the on-demand dissemination of video resources in neighboring geographical area, in order to handle imbalance between supply and demand. IDVD uses the epidemic model to describe the state transition of nodes and define the convergence condition of resource dissemination algorithm. Extensive tests show how IDVD achieves much better performance results in comparison with other state of the art solutions. [ABSTRACT FROM AUTHOR]