Data Broadcast Research Articles

Survey on Modest Sensory Network Utility Device Watcher Using Android Database System

Abstract: This report introduces the android application using a modest sensor device. The objective of this project is to develop a system to collect data from sensors, and to send this data broadcast through a network which passes the data to an application. Wireless transmission methods have been detected, and it was decided that a HTTP GET method would be used. This report also saysthe progress made to date on the project, and proposes a timeline for the completion of the project. The aim of this project was to design and build a wireless sensor network. These are the exploration of personal area networks and mesh networking, this system was implemented to detect physical intrusion. This network communicates with the generic infrastructure, resilient to future wireless sensor projects, which stored sensor data in a database. Also it was a user interface to monitor the status of the entire application

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

An optimized and efficient multiuser data sharing using the selection scheme design secure approach and federated learning in cloud environment

Real-Time Precise DGNSS/INS Integrated Relative Positioning with High Output Rate and Low Broadcast Rate for Kinematic-to-Kinematic Applications

High-output-rate relative positions are required for high-speed safety-critical kinematic-to-kinematic applications such as pre-crash sensing and shipboard landing. We propose a real-time, high-output-rate relative positioning method based on the integration of a real time kinematic (RTK) differential global navigation satellite systems (DGNSS) relative positioning algorithm, carrier-phase-based tightly coupled GNSS/Inertial navigation system (TC-GNSS/INS) integration algorithm and polynomial prediction algorithm for position increment. We focus on the rarely studied issue that data broadcast rates and sampling rates have effects on the integrated relative positioning accuracy under different motion states of a moving base. A vehicle-to-vehicle field test with a frequently turning base demonstrates the advantages of the proposed method, such as low bit rate of broadcast data, high output rate of position solutions and excellent real-time tolerance of latency. The results show that compared with the 10-Hz output of sole RTK DGNSS relative positioning, the proposed method can provide centimeter-level-accuracy relative positions at an output rate of 125 Hz with a sampling rate of 1 Hz, and the bit rate can be reduced by 83.12%. A UAV-to-boat field test with straight-line-motion moving base is then carried out to validate the applicability of the proposed system for aircraft applications. The results show that the broadcast rate of position increments of the moving base can be further reduced.

Portable IoT Body Temperature Screening System to Combat the Adverse Effects of COVID-19

In managing the COVID-19 pandemic, the Malaysian government enforced mandatory body temperature screening as a rudimentary form of infection detection at the entry points of establishments and public transportation. However, previous iterations of IoT body temperature screening systems were bulky, fragile, expensive, and designed for personal use instead of the screening of many people. Therefore, a standalone, portable, and rugged IoT-enabled body temperature screening system for detecting elevated temperatures was developed in this research work. This system uses a proximity sensor to detect subjects and determine their body temperature using a non-contact temperature sensor. Body temperature data is displayed on the device and uploaded over a Wi-Fi network to a cloud server for data storage and analysis. From the cloud server, body temperature information is retrieved and displayed on the Blynk IoT client dashboard for remote monitoring. The device also provides alerts for body temperatures above 37.5 °C. The prototype system performed impressively during the assessment. Body temperature readings were impressively accurate compared to a medical-grade non-contact thermometer, with an average variance of less than 1%. Additionally, the system was highly reliable, with a 100% IoT data broadcast success rate.

A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices.

The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

Evaluating factors contributing to the failure of information system in the banking industry

The increasing use of Information Technology (IT) has led to many security and other related failures in the banks and other financial institutions in Bangladesh. In this paper, we investigated the factors contributing to the failurein the IT system of the banking industry in Bangladesh. Based on the experts' opinions and weight on the specified evaluating criteria, an empirical test was conducted using a rough set theory to produce a framework for the IT system failure factors. In this study, an extended approach involving the integration of rough set theory based flexible Failure Mode and Effect Analysis (FMEA) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has beenapplied to help the managers of the corresponding field to identify the factors responsible for the failure of the IT system in the banking industries and then prioritize them accordingly, for the ease of decision-making.In this research, eleven such failure factors were identified, which were then quantitatively analyzed to facilitate managers in crucial decision-making. It was observed that cyber-attack, database hack risks, server failure, network interruption, broadcast data error, and virus effect were the most significant factors for the failure of the IT system. The framework developed in this research can be utilized to assist in efficient decision-makingin other serviceindustries where IT systems play a key role. To the best of the knowledge, this is the first study thatempirically tested key failure factors of the IT system for the banking sector using an integrated method.

Research on the marketing strategy of Tik Tok live broadcast with goods

Through literature analysis, this paper explores the issue of the marketing strategy of Tik Tok live broadcast with goods, and at the same time, analyzes the problems arising in its development process and puts forward some optimization suggestions. This paper analyzes the existing live broadcast with goods model of Tik Tok. Tik Tok mainly has two types of live broadcast with goods models: internet celebrity bloggers themselves live broadcast with goods and pop stars+live broadcast with goods, and this paper points out the problems of homogenization of live broadcast content, serious fraud of live broadcast data, and product quality and after-sale service cannot be guaranteed according to the analysis. Then, according to the above problems, this paper puts forward four optimization suggestions: emphasis on professional training of anchors, linking online and offline shopping festivals, checking the live data to ensure authenticity, and strict control of the selection process to ensure product quality.

ALI: Anonymous Lightweight Inter-Vehicle Broadcast Authentication with Encryption

Wireless broadcast transmission enables Inter-vehicle or Vehicle-to-Vehicle (V2V) communication among nearby vehicles. This communication supports latency-critical applications for improved safety and maybe optimized traffic. However, V2V communication is vulnerable to cyber attacks involving message manipulation. Mechanisms are required to ensure both authenticity and integrity of broadcast data, while maintaining drivers privacy against surveillance. Considering the limited computational resources of vehicles and the possibility of high traffic density scenarios, authentication processes should have low computational overhead. Prior research has produced multiple authentication protocol proposals based on digital signatures, hash functions, or Message Authentication Codes (MACs). To date, there is no computationally efficient secure broadcast authentication scheme tolerable by the vehicles resource-constrained On-Board Units (OBUs) for latency-critical applications in heavy traffic conditions. This paper provides a new secure, efficient, and privacy-preserving scheme proposing Anonymous Lightweight Inter-vehicle (ALI) broadcast authentication with encryption. ALI provides a high level of anonymity by combining a message authentication scheme with beacon encryption. The cryptographic overhead for V2V communication in the ALI scheme is only 149 bytes, and can handle authentication of approximately 700 broadcast messages every 100 milliseconds. This demonstrates the suitability of the ALI scheme in heavy traffic scenarios. We show the security and efficiency of our proposal by conducting security proof and performance analysis.

ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network

The Internet of Things (IoT) interconnects massive cyber-physical devices (CPD) to provide various applications, such as smart home and smart building. Bluetooth Mesh is an emerging networking technology, which can be used to organize a massive network with Bluetooth Low Energy (BLE) devices. Managed-flooding protocol is used in Bluetooth Mesh to route the data packets. Although it is a highly desirable option when data transmission is urgent, it is inefficient in a larger and denser mesh network due to the collisions of broadcast data packets. In this paper, we introduce ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. ACE relies on the existing Bluetooth Mesh messages to distribute routes without additional traffic overhead and conducts a beacon-aware routing update adaptively as the topology evolves. In ACE, BLE channel resources can be efficiently utilized by a channel scheduling scheme for each node locally and autonomously without any neighborly negotiation. We implement ACE on the nRF52840 from Nordic Semiconductor and evaluate its effectiveness on our testbed. Compared to the Bluetooth Mesh, our experiments proved that ACE could reduce the end-to-end latency by 16%, alleviate packets collisions issues, and increase the packet delivery ratio (PDR) by 30% under heavy traffic. Moreover, simulation results verified that ACE has better scalability when the size and density of networks become larger and denser.

Event-triggered control optimal tuning through bio-inspired optimization in robotic manipulators

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are obtained by using fourteen different bio-inspired optimization algorithms. The statistics results reveal that, among the tested bio-inspired optimization algorithms, the most reliable algorithm in the proposed tuning problem is the differential evolution variant DE/Best/1/Exp. The obtained result is validated both in numerical simulation as well as using a laboratory prototype. The simulation results indicate that the obtained control parameters can also deal with disturbances and reference changes not considered in the ETCTA’s optimization problem formulation without significantly worsening the control design objective. Experimental results disclose that the proposed event-triggered control tuning approach provides the best trade-off between the number of control signal updates and the position error among other tuning approaches, decreasing the data broadcasting of the control update by around 86.33% with a non-significant increase in the stabilization error of around 26.53%.

Jointly Adaptive Distributed Beamforming and Resource Allocation for Buffer-Aided Multiple-Relay NOMA Networks

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

The Domain Mismatch Problem in the Broadcast Speaker Attribution Task

The demand of high-quality metadata for the available multimedia content requires the development of new techniques able to correctly identify more and more information, including the speaker information. The task known as speaker attribution aims at identifying all or part of the speakers in the audio under analysis. In this work, we carry out a study of the speaker attribution problem in the broadcast domain. Through our experiments, we illustrate the positive impact of diarization on the final performance. Additionally, we show the influence of the variability present in broadcast data, depicting the broadcast domain as a collection of subdomains with particular characteristics. Taking these two factors into account, we also propose alternative approximations robust against domain mismatch. These approximations include a semisupervised alternative as well as a totally unsupervised new hybrid solution fusing diarization and speaker assignment. Thanks to these two approximations, our performance is boosted around a relative 50%. The analysis has been carried out using the corpus for the Albayzín 2020 challenge, a diarization and speaker attribution evaluation working with broadcast data. These data, provided by Radio Televisión Española (RTVE), the Spanish public Radio and TV Corporation, include multiple shows and genres to analyze the impact of new speech technologies in real-world scenarios.

Audiovisual speaker indexing for Web-TV automations

The current paper introduces a multimodal framework to provide Web-TV automations for live broadcasting and overall big streaming data management. The term indexing refers to the spatiotemporal localization of speakers participating in a discussion panel. Multiple modalities acting in parallel form the data-driven decision-making pipeline. The automated workflow includes the tasks of active speaker detection and localization, frame selection, and creation of a semantically annotated database. For improved performance and robustness, an information fusion model is proposed, which makes use of different audio and visual modalities. Audio-driven Voice Activity Detection follows the Enhanced Temporal Integration methodology applied on a standard audio feature set. For the localization of the dominant audio source, the argument that maximizes the General Cross-Correlation method is calculated. The visual modalities include face and mouth detection and Visual Voice Activity Detection. A Long Short Term Memory network is trained with mouth image sequences to determine voice activity. The values of the audio and visual Voice Activity Detection modules, as well as the General Cross-Correlation result, are used to train an Adaptive Neuro-Fuzzy model, which is responsible for the final decision. Experimental results prove the superiority of the information fusion approach compared to unimodal audio or visual models.

Дистанционное наблюдение за пациентами с хронической сердечной недостаточностью с применением телемониторинга АД и ЭКГ

Introduction. Remote monitoring of patients with chronic heart failure seems to be very promising in connection with a possible decrease in mortality as a result of the use of telemedicine technologies in the treatment of chronic heart failure (CHF). Materials and methods. Remote monitoring of the health of 997 patients with confirmed diagnoses of circulatory diseases complicated by CHF, aged 18 to 74, inclusive, living in rural areas, mainly in remote and hard-to-reach settlements of Uvatsky, Vagaysky, Nizhnetavdinsky, Tobolsky and Yarkovsky, was carried out. districts of the Tyumen region. Results. We studied the results of remote monitoring of blood pressure and ECG in 997 patients with chronic heart failure in rural areas for 24 months using various models of telemedicine support. In the «home» monitoring group (n = 316), patients independently recorded and broadcast blood pressure and ECG data; in the «office» monitoring group (n = 681), the same studies were performed by medical workers. Additionally, the presence or absence of patient complaints about the state of health at the time of the research was recorded. Conclusions. The organization and conduct of remote monitoring of the health status of patients with CHF using teleAP and teleECG in the «home» self-registration mode has an advantage over the implementation of such monitoring in a medical organization, which is reflected in a statistically significant decrease in the need for hospitalizations.

A distributed optimisation framework combining natural gradient with Hessian-free for discriminative sequence training

This paper presents a novel natural gradient and Hessian-free (NGHF) optimisation framework for neural network training that can operate efficiently in a distributed manner. It relies on the linear conjugate gradient (CG) algorithm to combine the natural gradient (NG) method with local curvature information from Hessian-free (HF). A solution to a numerical issue in CG allows effective parameter updates to be generated with far fewer CG iterations than usually used (e.g. 5-8 instead of 200). This work also presents a novel preconditioning approach to improve the progress made by individual CG iterations for models with shared parameters. Although applicable to other training losses and model structures, NGHF is investigated in this paper for lattice-based discriminative sequence training for hybrid hidden Markov model acoustic models using a standard recurrent neural network, long short-term memory, and time delay neural network models for output probability calculation. Automatic speech recognition experiments are reported on the multi-genre broadcast data set for a range of different acoustic model types. These experiments show that NGHF achieves larger word error rate reductions than standard stochastic gradient descent or Adam, while requiring orders of magnitude fewer parameter updates.

Is it a fallacy to believe in the hot hand in the NBA three-point contest?

The NBA Three-Point Contest has been considered an ideal setting to study the hot hand, as it showcases the elite professional shooters that hot hand beliefs are typically directed towards, but in an environment that eliminates many of the confounds present in game action. We collect 34 years of NBA Three-Point Contest television broadcast data (1986–2020), apply a statistical approach that improves on those of previous studies, and find considerable evidence of hot hand shooting in and across individuals. Our results support fans’ and experts’ widely held belief in the hot hand among NBA shooters.

NN Search based on Non-uniform Space Partition on Air

It is one of most important challenges in information services based on location to support a huge number of clients to process the NN query for a given query point. A scheme of NN query processing based on wireless data broadcasting is an effective way to overcome the challenge. In this paper, we propose an indexing scheme NSPI (Non-uniform Space Partition Index) for quick NN search over wireless data broadcasting. For implementing the aim, we use a non-uniform spatial partition and provide an index based on the partition for equidistance between indexes on the wireless channel. The index scheme enables the clients to process NN quickly by lessening the time for the clients to meet the index on the channel. To show the effectiveness of the proposed scheme, we compare the access time and tuning time of the clients with existing indexing schemes by simulation studies. The proposed NSPI shows shorter access time than the other existing scheme. Also, NSPI outperforms in the aspect of tuning time than the others.

Analysis of Forwarding Schemes to Mitigate Data Broadcast Storm in Connected Vehicles over VNDN

Analysis of Forwarding Schemes to Mitigate Data Broadcast Storm in Connected Vehicles over VNDN

STUDY OF THE INFLUENCE OF FACTORS ON THE CONTENT OF RUSSIAN FEDERAL CHANNELS IN THE CONTEXT OF DIGITALIZATION

The authors carry out a study of the influence of various factors on the content of Russian federal channels. Using content analysis of the broadcast data were obtained to identify the influence of factors, characteristic for the stage of formation of a consumer society in Russia on the content of television channels and the analysis of conformity of telecasts the authors developed, criteria that have been formed and considered together with current trends in society related to the digitalization processes, and preferences of Russians. Television is an important social institution that should not only reflect the current situation in society, but also broadcast certain behaviors, culture and values. Television content requires a lot of attention, because now there are significant changes in the individual in the new conditions of digital communication. Modern television content should move away from the simple model of mass culture, which is based on a standardized approach based on entertainment content. Content should take into account modern consumer values.

An Accelerating Approach for Blockchain Information Transmission Based on NDN

Blockchain is becoming more and more popular in various fields. Since the information transmission mode of the blockchain is data broadcasting, the traditional TCP/IP network cannot support the blockchain system well, but the Named-Data Networking (NDN) could be a good choice because of its multi-path forwarding and intra-network caching functions. In this article, we propose a new blockchain information transmission acceleration strategy (AITS) combining with graph theory and probability theory based on the NDN architecture. We select some more important nodes in the network as “secondary nodes”, and give them more bandwidth and cache space to assist the NDN network in data transmission. In order to select the correct node as the secondary node, we present a method to calculate the number of secondary nodes, and give the function to calculate the importance of each node. The simulation results show that in complex networks, the proposed method has superior performance in accelerating information transmission and reducing data overhead.