To see the other types of publications on this topic, follow the link: Wide area networks (Computer networks) – Evaluation.
Journal articles on the topic 'Wide area networks (Computer networks) – Evaluation'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Wide area networks (Computer networks) – Evaluation.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
NABESHIMA, M. "Performance Evaluation of MulTCP in High-Speed Wide Area Networks." IEICE Transactions on Communications E88-B, no. 1 (January 1, 2005): 392–96. http://dx.doi.org/10.1093/ietcom/e88-b.1.392.
Full text
2
Clarke, RT, SJ Nichols, and P. Mars. "Transputer-based simulation tool for performance evaluation of wide area telecommunications networks." Microprocessors and Microsystems 13, no. 3 (April 1989): 173–78. http://dx.doi.org/10.1016/0141-9331(89)90123-3.
Full text
3
Akella, Aditya, Srinivasan Seshan, and Anees Shaikh. "An empirical evaluation of wide-area internet bottlenecks." ACM SIGMETRICS Performance Evaluation Review 31, no. 1 (June 10, 2003): 316–17. http://dx.doi.org/10.1145/885651.781075.
Full text
4
Nekovee, Maziar, Marinho P. Barcellos, and Michael Daw. "Reliable multicast for the Grid: a case study in experimental computer science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (July 18, 2005): 1775–91. http://dx.doi.org/10.1098/rsta.2005.1606.
Full textAbstract:
In its simplest form, multicast communication is the process of sending data packets from a source to multiple destinations in the same logical multicast group. IP multicast allows the efficient transport of data through wide-area networks, and its potentially great value for the Grid has been highlighted recently by a number of research groups. In this paper, we focus on the use of IP multicast in Grid applications, which require high-throughput reliable multicast. These include Grid-enabled computational steering and collaborative visualization applications, and wide-area distributed computing. We describe the results of our extensive evaluation studies of state-of-the-art reliable-multicast protocols, which were performed on the UK's high-speed academic networks. Based on these studies, we examine the ability of current reliable multicast technology to meet the Grid's requirements and discuss future directions.
5
Ojo, Mike Oluwatayo, Davide Adami, and Stefano Giordano. "Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area." Future Internet 13, no. 5 (April 29, 2021): 115. http://dx.doi.org/10.3390/fi13050115.
Full textAbstract:
Smart agriculture and wildlife monitoring are one of the recent trends of Internet of Things (IoT) applications, which are evolving in providing sustainable solutions from producers. This article details the design, development and assessment of a wildlife monitoring application for IoT animal repelling devices that is able to cover large areas, thanks to the low power wide area networks (LPWAN), which bridge the gap between cellular technologies and short range wireless technologies. LoRa, the global de-facto LPWAN, continues to attract attention given its open specification and ready availability of off-the-shelf hardware, with claims of several kilometers of range in harsh challenging environments. At first, this article presents a survey of the LPWAN for smart agriculture applications. We proceed to evaluate the performance of LoRa transmission technology operating in the 433 MHz and 868 MHz bands, aimed at wildlife monitoring in a forest vegetation area. To characterize the communication link, we mainly use the signal-to-noise ratio (SNR), received signal strength indicator (RSSI) and packet delivery ratio (PDR). Findings from this study show that achievable performance can greatly vary between the 433 MHz and 868 MHz bands, and prompt caution is required when taking numbers at face value, as this can have implications for IoT applications. In addition, our results show that the link reaches up to 860 m in the highly dense forest vegetation environment, while in the not so dense forest vegetation environment, it reaches up to 2050 m.
6
Lakshmi Narayana, Chavala, Rajesh Singh, and Anita Gehlot. "Performance evaluation of LoRa based sensor node and gateway architecture for oil pipeline management." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 974. http://dx.doi.org/10.11591/ijece.v12i1.pp974-982.
Full textAbstract:
These days, the oil industrial industry is leaning toward employing smart field improvements to streamline various activities in the midstream area. Oil transportation over large distances via pipelines has a cheap cost and high efficiency in this sector. If pipelines are not properly maintained, they may fail, potentially causing catastrophic, long-term, and irreversible consequences on both natural and human conditions. Low power wide area networks (LPWANs) are without a doubt one of the domains that cause the most from industrial fields when it comes to realizing the vision of the internet of things (IoT). Long-range (LoRa) is an emerging LPWAN technology that is particularly useful for transmitting data over long distances. The goal of this work is to offer a methodology for managing oil pipelines over long distances utilizing the LoRa communication protocol and the installation of sensor nodes and LoRa gateways along the pipeline. We also used the optimized network engineering tools (OPNET) simulator to examine various simulation findings of LoRa performance.
7
Oukessou, Yassine, Mohamed Baslam, and Mohamed Oukessou. "Improved uplink throughput and energy efficiency of LoRaWAN using 2-hop LEACH protocol." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (December 1, 2021): 1557. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1557-1563.
Full textAbstract:
The low power wide area networks (LPWAN) is the new connectivity technology that is geared towards energy constrained internet of things (IoT) devices, is starting to become one of the drivers of the re-accelerating IoT market and has one goal: ensure the wide range distance while reducing the battery energy consumption. We focus in this paper on the evaluation of the uplink throughput of the long-range wide area networks (LoRaWAN) then we attempt optimize the throughput and power dissipation using low energy adaptive clustering hierarchy (LEACH) protocol. Therefore, we exploit a novel module developed in NS-3 simulator for obtaining the first measurements scenario, then the LEACH algorithm for the second optimization case. As result, the simulation analysis will help us to add a new LoRaWAN routing protocol feature.
8
Lavric, Alexandru, and Valentin Popa. "Performance Evaluation of LoRaWAN Communication Scalability in Large-Scale Wireless Sensor Networks." Wireless Communications and Mobile Computing 2018 (June 28, 2018): 1–9. http://dx.doi.org/10.1155/2018/6730719.
Full textAbstract:
The LoRaWAN communication protocol can be used for the implementation of the IoT (Internet of Things) concept. Currently, most of the information regarding the scalability of the LoRa technology is commercial and deals with the best-case scenario. Thus, we need realistic models, enabling the proper assessment of the performance level. Most of the time, the IoT concept entails a large number of nodes distributed over a wide geographical area, therefore forming a high density, large-scale architecture. It is important to determine the number of collisions so that we can assess the network performance. The present paper aims at assessing the performance level of the LoRaWAN technology by analyzing the number of packet collisions that can occur. Thus, this paper determines the maximum number of LoRa nodes that can communicate with a Gateway considering the LoRaWAN protocol specifications. Furthermore, we have proposed a series of solutions for reducing the number of collisions and increasing the capacity of the communication channel.
9
Runge, Jason, and Radu Zmeureanu. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review." Energies 12, no. 17 (August 23, 2019): 3254. http://dx.doi.org/10.3390/en12173254.
Full textAbstract:
During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.
10
Milani, Stefano, and Ioannis Chatzigiannakis. "Design, Analysis, and Experimental Evaluation of a New Secure Rejoin Mechanism for LoRaWAN Using Elliptic-Curve Cryptography." Journal of Sensor and Actuator Networks 10, no. 2 (June 18, 2021): 36. http://dx.doi.org/10.3390/jsan10020036.
Full textAbstract:
LoRaWAN (Long Range Wide Area Network) is a Low-Power Wide Area Networks (LPWAN) technology with very rapid uptake during the previous years, developed by the LoRa (Long Range) Alliance as an open standard operating over the unlicensed band. Current LoRaWAN architecture foresees specific techniques for bootstrapping end-to-end encryption during network initialization. In particular, this work focuses on the Over-The-Air Activation (OTAA) method, which uses two keys (Network key (NwkKey) and Application key (AppKey)) that are hard-coded into the device and do not change throughout the entire lifetime of the deployment. The inability to refresh these two keys is as a weak point in terms of the overall security of the network especially when considering deployments that are expected to operate for at least 10–15 years. In this paper, the security issues of OTAA are presented in detail highlighting the vulnerabilities against the specific type of attacks. A new scheme for network activation is proposed that builds upon the current LoRaWAN architecture in a way that maintains backwards compatibility while resolving certain vulnerabilities. Under the new mechanism, the devices periodically negotiate new keys securely based on elliptic-curve cryptography. The security properties of the proposed mechanism are analyzed against a specific type of attacks. The analysis indicates that the new secure rejoin mechanism guarantees (i) computational key secrecy, (ii) decisional key secrecy, and (iii) key independence, forward and backward, for both root keys thus properly addressing the considered security vulnerabilities of LoRaWAN. Moreover, the method is implemented in software using the RIOT-OS, a hardware-independent operating system that supports many different architectures for 8 bit, 16 bit, 32 bit and 64 bit processors. The resulting software is evaluated on the FIT IoT-Lab real-world experimentation facility under a diverse set of ARM Cortex-M* devices targeting a broad range of IoT applications, ranging from advanced wearable devices to interactive entertainment devices, home automation and industrial cyber-physical systems. The experiments indicate that the overall overhead incurred in terms of energy and time by the proposed rejoin mechanism is acceptable given the low frequency of execution and the improvements to the overall security of the LoRaWAN1.1 OTAA method.
11
Alansari, Zainab, Nor Badrul Anuar, Amirrudin Kamsin, and Mohammad Riyaz Belgaum. "A systematic review of routing attacks detection in wireless sensor networks." PeerJ Computer Science 8 (October 21, 2022): e1135. http://dx.doi.org/10.7717/peerj-cs.1135.
Full textAbstract:
Wireless sensor networks (WSNs) consist of hundreds, or thousands of sensor nodes distributed over a wide area and used as the Internet of Things (IoT) devices to benefit many home users and autonomous systems industries. With many users adopting WSN-based IoT technology, ensuring that the sensor’s information is protected from attacks is essential. Many attacks interrupt WSNs, such as Quality of Service (QoS) attacks, malicious nodes, and routing attacks. To combat these attacks, especially on the routing attacks, we need to detect the attacker nodes and prevent them from any access to WSN. Although some survey studies on routing attacks have been published, a lack of systematic studies on detecting WSN routing attacks can be seen in the literature. This study enhances the topic with a taxonomy of current and emerging detection techniques for routing attacks in wireless sensor networks to improve QoS. This article uses a PRISMA flow diagram for a systematic review of 87 articles from 2016 to 2022 based on eight routing attacks: wormhole, sybil, Grayhole/selective forwarding, blackhole, sinkhole, replay, spoofing, and hello flood attacks. The review also includes an evaluation of the metrics and criteria used to evaluate performance. Researchers can use this article to fill in any information gaps within the WSN routing attack detection domain.
12
He, Wu, and Silvana Watson. "Designing a field experience tracking system in the area of special education." Campus-Wide Information Systems 31, no. 5 (October 28, 2014): 290–303. http://dx.doi.org/10.1108/cwis-07-2013-0028.
Full textAbstract:
Purpose – To improve the quality of field experience, support field experience cooperation and streamline field experience management, the purpose of this paper is to describe the experience in using Activity Theory to design and develop a web-based field experience tracking system for a special education program. Design/methodology/approach – The authors used Activity Theory to design and develop a web-based field experience tracking system for a special education program. An in-depth evaluation of the developed web-based system including usability testing and actual use of the system was conducted. Findings – The evaluation and data analysis results demonstrate the value of Activity Theory and show that a web-based tracking system is a valuable tool to support the management of pre-service teachers’ field experiences. Originality/value – This is the first paper to discuss the design and development of field experience tracking system using Activity Theory. This paper can be used to motivate other developers to use Activity theory to design campus-wide information system. The system and methodology the authors used in this project has wider applicability and generalizability, and can be applied to the management of other competency and field based professional training in areas such as nursing, social work and medicine.
13
Wu, Daniel, Dhruv Gupta, and Prasant Mohapatra. "QuRiNet: A wide-area wireless mesh testbed for research and experimental evaluations." Ad Hoc Networks 9, no. 7 (September 2011): 1221–37. http://dx.doi.org/10.1016/j.adhoc.2011.02.001.
Full text
14
Chandrasekaran, Radhika, and Senthil Kumar Paramasivan. "A State-of-the-Art Review of Time Series Forecasting Using Deep Learning Approaches." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 12 (December 31, 2022): 92–105. http://dx.doi.org/10.17762/ijritcc.v10i12.5890.
Full textAbstract:
Time series forecasting has recently emerged as a crucial study area with a wide spectrum of real-world applications. The complexity of data processing originates from the amount of data processed in the digital world. Despite a long history of successful time-series research using classic statistical methodologies, there are some limits in dealing with an enormous amount of data and non-linearity. Deep learning techniques effectually handle the complicated nature of time series data. The effective analysis of deep learning approaches like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Autoencoders, and other techniques like attention mechanism, transfer learning, and dimensionality reduction are discussed with their merits and limitations. The performance evaluation metrics used to validate the model's accuracy are discussed. This paper reviews various time series applications using deep learning approaches with their benefits, challenges, and opportunities.
15
DUCATELLE, FREDERICK, GIANNI DI CARO, and LUCA MARIA GAMBARDELLA. "USING ANT AGENTS TO COMBINE REACTIVE AND PROACTIVE STRATEGIES FOR ROUTING IN MOBILE AD-HOC NETWORKS." International Journal of Computational Intelligence and Applications 05, no. 02 (June 2005): 169–84. http://dx.doi.org/10.1142/s1469026805001556.
Full textAbstract:
This paper describes AntHocNet, an algorithm for routing in mobile ad-hoc networks based on ideas from the ant colony optimisation framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behaviour is supported by a lightweight information bootstrapping process. Paths are represented in the form of distance-vector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbour to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.
16
Nashiruddin, Muhammad Imam, Maruli Tua Baja Sihotang, and Muhammad Ary Murti. "Comparative study of low power wide area network based on internet of things for smart city deployment in Bandung city." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 1 (January 1, 2022): 425. http://dx.doi.org/10.11591/ijeecs.v25.i1.pp425-439.
Full textAbstract:
Smart city implementation, such as smart energy and utilities, smart mobility & transportation, smart environment, and smart living in urban areas is expanding rapidly worldwide. However, one of the biggest challenges that need to be solved is the selection of the appropriate internet of things (IoT) connectivity technologies. This research will seek for the best candidate low power wide area network (LPWAN) technologies such as long-range wide area network (LoRaWAN), narrow-band internet of things (NB-IoT), and random phase multiple access (RPMA) for IoT smart city deployment in Bandung city is based on IoT network connectivity between with six technical evaluation criteria: gateway requirements, traffic/data projection, the best signal level area distribution, and overlapping zones. Bass model is carried out to determine the capacity forecast. While in coverage prediction, LoRaWAN and NB-IoT use the Okumura-Hata propagation, and Erceg-Greenstein (SUI) model is used for RPMA. Based on the simulation and performance evaluation results, RPMA outperforms LoRaWAN and NB-IoT. It required the least gateway number to cover Bandung city with the best signal levels and overlapping zones.
17
Dong, Mo, Zhipeng Sun, Haiye Yu, Lei Zhang, Yuanyuan Sui, and Ruohan Zhao. "Performance Evaluation of the Transmission Quality of the Internet of Things in Farmland Environmental Monitoring Based on ns-3." Mobile Information Systems 2022 (May 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/3148348.
Full textAbstract:
The Internet of Things (IoT) technology focuses on the application of information-sensing equipment, transmission network, and intelligent information processing technology. Through scientific management, it can achieve the rational use of agricultural resources, thus improving the ecological environment, reducing production costs, and improving the yield and quality of agricultural products. At present, the application of agricultural IoT technology faces problems including small network coverage, limited monitoring projects, high transmission cost, and short power supply time, which limit the popularity and ability of farmland environmental monitoring. In this paper, ns-3 is used to simulate the IoT for farmland environmental monitoring based on the long-range wide-area network (LoRaWAN) protocol. The simulation results show the transmission distance with good transmission quality under the LoRaWAN protocol. Furthermore, we analyze the comparison of the combination range of network transmission quality parameters under the two acknowledgment (ACK) mechanisms. Finally, we present the parameter combination range and suggestions to improve the network transmission quality, which provide a new basis for guiding the farmland environmental monitoring system.
18
Palmer, Diane, Elena Koumpli, Ian Cole, Ralph Gottschalg, and Thomas Betts. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry." Energies 11, no. 12 (December 15, 2018): 3506. http://dx.doi.org/10.3390/en11123506.
Full textAbstract:
Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof’s suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses.
19
Kassandra Pereira Belfort, Ilka, Isaura Danielli Borges de Sousa, Tatyanne Silva Rodrigues, Ana Paula Cunha, Vanessa Edilene Duarte Martins, Sally Cristina Moutinho Monteiro, and Allan Kardec Duailibe Barros. "COMPUTER MODELS FOR DISEASE PREDICTION." International Journal for Innovation Education and Research 8, no. 1 (January 31, 2020): 275–84. http://dx.doi.org/10.31686/ijier.vol8.iss1.2158.
Full textAbstract:
With the increased computational power and ease of gathering medical information, Artificial Intelligence has helped all areas of health in developing algorithms and techniques for disease diagnosis and staging. The technology has been applied in several areas, due to its wide range of features, some activities become simpler with your help. Thus, this study aimed to identify the main computational models for disease prediction. Data collection was performed in the virtual databases present in the Health Library Research Portal (VHL): LILACS: Latin American and Caribbean Health Sciences Literature, Scielo - ScientificElectronic Library Online and Literature Analysis and Retrieval System Medical Online (MEDLINE). We found 52 articles and 10 of these in the review. From the reading and evaluation of the included articles, which can be aided by computer vision techniques, machine learning through neural networks and pattern recognition can be developed algorithms capable of identifying diseases. Thus, from this diagnosis provided by the algorithm, the health professional will have conditions for early prevention, diagnosis and treatment of diseases.
20
Abu Bakar, Kamalrulnizam, and B. S. Doherty. "Evaluation of the Recorded State Mechanism for Protecting Agent Integrity Against Malicious Hosts." International Journal of Computers Communications & Control 3, no. 1 (March 1, 2008): 60. http://dx.doi.org/10.15837/ijccc.2008.1.2375.
Full textAbstract:
As agent technology is expected to become a possible base platform for an electronic services framework, especially in the area of Electronic Commerce, reliable security protection is a crucial aspect, since some transactions in this area might involve confidential information, such as credit card number, bank account information or some form of digital cash, that has value and might therefore be attacked. In addition, without proper and reliable security protection, the wide spread use of agent technology in real world applications could be impeded. In this paper, evaluation of the Recorded State Mechanism (RSM) previously proposed by the authors is presented. The evaluation examines the RSM security protection and implementation overhead, in order to analyse the RSM security strength and implementation feasibility in real world application.
21
Gao, Xin. "Performance evaluation of automatic object detection with post-processing schemes under enhanced measures in wide-area aerial imagery." Multimedia Tools and Applications 79, no. 41-42 (August 15, 2020): 30357–86. http://dx.doi.org/10.1007/s11042-020-09201-0.
Full text
22
Kirihara, Soshu. "Stereolithographic Additive Manufacturing of Bulky Ceramic Components with Functionally Geometric Micropattern." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2016, CICMT (May 1, 2016): 000001–5. http://dx.doi.org/10.4071/2016cicmt-ta11.
Full textAbstract:
Abstract In a stereolithographic additive manufacturing (AM), two dimensional (2D) cross sectional patterns were created through photo polymerization by ultraviolet laser drawing on spread resin paste including ceramic nanoparticles, and three dimensional (3D) composite models were sterically printed by layer lamination through chemical bonding. An automatic collimeter was equipped with the laser scanner to adjust beam diameter. Fine or coarse beams could realize high resolution or wide area drawings, respectively. Metal and ceramic bulky components including dendritic networks were geometrically built by using stereolithographic AM. Geometric patterns with periodic, self-similar, graded and fluctuated arrangements were created by computer aided design, manufacture and evaluation (CAD/CAM/CAE) for effective modulations of energy and material flows through dielectric lattices in photonic crystals, porous electrodes in fuel cells and biological scaffolds in artificial bones.
23
Gao, Xin. "Correction to: Performance evaluation of automatic object detection with post-processing schemes under enhanced measures in wide-area aerial imagery." Multimedia Tools and Applications 80, no. 8 (January 12, 2021): 12905. http://dx.doi.org/10.1007/s11042-020-10464-w.
Full text
24
Abu Al-Haija, Qasem, and Saleh Zein-Sabatto. "An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks." Electronics 9, no. 12 (December 15, 2020): 2152. http://dx.doi.org/10.3390/electronics9122152.
Full textAbstract:
With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, the Internet of Things (IoT) has earned wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack that tends to be treated as normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with the cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide the comprehensive development of a new intelligent and autonomous deep-learning-based detection and classification system for cyber-attacks in IoT communication networks that leverage the power of convolutional neural networks, abbreviated as IoT-IDCS-CNN (IoT based Intrusion Detection and Classification System using Convolutional Neural Network). The proposed IoT-IDCS-CNN makes use of high-performance computing that employs the robust Compute Unified Device Architectures (CUDA) based Nvidia GPUs (Graphical Processing Units) and parallel processing that employs high-speed I9-core-based Intel CPUs. In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. All subsystems were developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the Network Security Laboratory-Knowledge Discovery Databases (NSL-KDD) dataset, which includes all the key attacks in IoT computing. The simulation results demonstrated a greater than 99.3% and 98.2% cyber-attack classification accuracy for the binary-class classifier (normal vs. anomaly) and the multiclass classifier (five categories), respectively. The proposed system was validated using a K-fold cross-validation method and was evaluated using the confusion matrix parameters (i.e., true negative (TN), true positive (TP), false negative (FN), false positive (FP)), along with other classification performance metrics, including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning-based IDCS systems in the same area of study.
25
Luvisotto, Michele, Federico Tramarin, Lorenzo Vangelista, and Stefano Vitturi. "On the Use of LoRaWAN for Indoor Industrial IoT Applications." Wireless Communications and Mobile Computing 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3982646.
Full textAbstract:
Low-Power Wide-Area Networks (LPWANs) have recently emerged as appealing communication systems in the context of the Internet of Things (IoT). Particularly, they proved effective in typical IoT applications such as environmental monitoring and smart metering. Such networks, however, have a great potential also in the industrial scenario and, hence, in the context of the Industrial Internet of Things (IIoT), which represents a dramatically growing field of application. In this paper we focus on a specific LPWAN, namely, LoRaWAN, and provide an assessment of its performance for typical IIoT employments such as those represented by indoor industrial monitoring applications. In detail, after a general description of LoRaWAN, we discuss how to set some of its parameters in order to achieve the best performance in the considered industrial scenario. Subsequently we present the outcomes of a performance assessment, based on realistic simulations, aimed at evaluating the behavior of LoRaWAN for industrial monitoring applications. Moreover, the paper proposes a comparison with the IEEE 802.15.4 network protocol, which is often adopted in similar application contexts. The obtained results confirm that LoRaWAN can be considered as a strongly viable opportunity, since it is able to provide high reliability and timeliness, while ensuring very low energy consumption.
26
Sheng-Tao Chen, Sheng-Tao Chen, Chien-Wu Lan Sheng-Tao Chen, Shih-Sung Lin Chien-Wu Lan, and 廖家德 Shih-Sung Lin. "An Evaluation of Self-Built Low-Power Wide-Area Network Based on LoRa." 電腦學刊 33, no. 5 (October 2022): 073–82. http://dx.doi.org/10.53106/199115992022103305007.
Full textAbstract:
<p>With the rapid development of applications in Internet of Things (IoT). Low power consumption and wide area is one of the solutions for the development of information transmission. Therefore, Low-Power Wide-Area Network (LPWAN) technology had led to extensive applications and discussions. The Long Range (LoRa) has the characteristics of self-built network and programmable control of communication parameters besides the above features of LPWAN technologies. The LoRa has more flexible application capabilities compared with the LPWAN technology that requires infrastructure provided by Internet Service Providers (ISP). Therefore, this paper describes the evaluation method from the LoRa parameters and the experiment of long-distance transmission. According to the experimental results, the actual effective transmission distance of the lightweight LoRa module from the packet loss rate is 621 meters. Moreover, the phenomena that different types of antennas connected to the transmitter affect the direction of the device are discussed.</p> <p> </p>
27
Martinez-Caro, J. M., and M. D. Cano. "IoT System Integrating Unmanned Aerial Vehicles and LoRa Technology: A Performance Evaluation Study." Wireless Communications and Mobile Computing 2019 (November 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/4307925.
Full textAbstract:
Nowadays, the popularity of the unmanned aerial vehicles (UAVs) is high, and it is expected that, in the next years, the implementation of UAVs in day-to-day service will be even greater. These new implementations make use of novel technologies encompassed under the term Internet of Things (IoT). One example of these technologies is Long-Range (LoRa), classified as a Low-Power Wide-Area Network (LPWAN) with low-cost, low-power consumption, large coverage area, and the possibility of a high number of connected devices. One fundamental part of a proper UAV-based IoT service deployment is performance evaluation. However, there is no standardized methodology for assessing the performance in these scenarios. This article presents a case study of an integrated UAV-LoRa system employed for air-quality monitoring. Each UAV is equipped with a set of sensors to measure several indicators of air pollution. In addition, each UAV also incorporates an embedded LoRa node for communication purposes. Given that mobility is key when evaluating the performance of these types of systems, we study eight different mobility models, focusing on the effect that the number of UAVs and their flying speed have on system performance. Through extensive simulations, performance is evaluated via multiple quality dimensions, encompassing the whole process from data acquisition to user experience. Results show that our performance evaluation methodology allows a complete understanding of the operation, and for this specific case study, the mobility model with the best performance is Pathway because the LoRa nodes are distributed and move orderly throughout the coverage area.
28
Zhang, Duo, and Dongmei Feng. "Mine Geological Disaster Risk Assessment and Management Based on Multisensor Information Fusion." Mobile Information Systems 2022 (July 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/1757026.
Full textAbstract:
Mining excavation is often the main cause of geological disasters in people’s construction activities. The geological disasters have the characteristics of large destruction, wide range of harm, and large loss. In particular, the collapse and slide geological disasters caused by underground mining are particularly prominent, and they have triggered a number of major natural disaster events. Therefore, it is particularly important to assess the exposure to geological hazards in mines. The purpose of this article is to study and analyze the assessment and management of the risk of geological hazards in mines based on multisensor data integration. This paper first introduces the process of multisource information fusion, and in the process of information fusion, the sensor needs to collect signals first, then preprocesses the signals provided by the sensor, and then analyzes the fusion process of D-S evidence theory algorithm and BP neural network algorithm in multisensor. Finally, the deformers in the study area are investigated by multisensor data integration techniques, the deformation and damage features of the deformers in the study area are evaluated, and the risk assessment and vulnerability evaluation of the key slopes are carried out. The experimental results of this paper show that according to the statistics of the distribution of slope disaster points, the geological disasters are mainly concentrated in 10–25°, a total of 361, accounting for 58.1% of the total disaster points. From the point density distribution, geological disasters are most concentrated at 20–30°, and the point density is 35 places/100 km2. The results show that in areas with large slope and height difference, it is easy to form air surface, deformation, and damage, resulting in geological disasters.
29
Paajala, Iikka, Jesse Nyyssölä, Juho Mattila, and Pasi Karppinen. "Users’ Perceptions of Key Blockchain Features in Games." Future Internet 14, no. 11 (November 4, 2022): 321. http://dx.doi.org/10.3390/fi14110321.
Full textAbstract:
The blockchain is an emerging technology that has the potential to revolutionize the gaming industry among a wide range of different business fields. So far, only a few studies have been conducted about blockchain gaming. This study introduces a mobile game utilizing blockchain asset tokens and smart contracts. It was developed for research purposes and used to demonstrate blockchain-based games using semi-structured interviews. This study follows the exploratory research paradigm, which aims to map research of little-known areas. This study focuses on how participants perceived blockchain attributes such as trust, transparency, and user-generated content and how this affected engagement and their willingness to play the game again. Based on our evaluation, generating blockchain assets positively impacted player retention. According to the results, providing genuine asset ownership through the blockchain contributes to environmental engagement and self-engagement, as well as player retention. Another positive blockchain feature discovered from the interview data is user-generated content implementation into games.
30
Deshpande, Pallavi, N. Suganthi, G. Nirmala, Anand Pandey, Patteti Krishna, Meenakshi Anurag Thalor, and Evans Asenso. "Metaheuristic Deep Learning-Driven Wireless Communication Security Adaptation Using Multivariate Analysis of Variance (MANOVA)." Security and Communication Networks 2022 (May 6, 2022): 1–7. http://dx.doi.org/10.1155/2022/8426997.
Full textAbstract:
The implementation of accurate models to improve access technologies, communication transmission, and network slicing is anticipated to play a big part in the edge computing approach, as the demands and needs of individuals are quickly evolving. Deep learning models have tended to deliver more benefits in a wide range of applications; it also assists data providers in demonstrating considerable improvements in tackling complex real-world challenges. While the integration of connected wireless networks and deep learning is still in its infancy, wireless communication networks are increasingly focused on sophisticated technologies to meet the current and future needs of end users. Based on the theoretical and practical aspect that ranges from the basic aspect to future applications of wireless communication, this study is intended in addressing the opportunities of adopting metaheuristic deep learning-driven wireless communication. The researchers intend to apply descriptive design to the study as this enables in understanding the critical aspects of the study in an elaborate manner. The authors use both a primary data sources and a secondary data sources for performing the analysis. The secondary data source is sourced to understand the application of deep learning in the wireless communication area, and primary research is used to gather the data from the respondents to test the hypothesis and provide conclusions based on the analyses. The scope of this work is to utilize a quantitative model to undertake an analysis on evaluating the key parameters in the application of deep learning in wireless communication, which will allow for critical analysis and interpretation based on the findings.
31
Zacarias-Morales, Noel, Pablo Pancardo, José Adán Hernández-Nolasco, and Matias Garcia-Constantino. "Attention-Inspired Artificial Neural Networks for Speech Processing: A Systematic Review." Symmetry 13, no. 2 (January 28, 2021): 214. http://dx.doi.org/10.3390/sym13020214.
Full textAbstract:
Artificial Neural Networks (ANNs) were created inspired by the neural networks in the human brain and have been widely applied in speech processing. The application areas of ANN include: Speech recognition, speech emotion recognition, language identification, speech enhancement, and speech separation, amongst others. Likewise, given that speech processing performed by humans involves complex cognitive processes known as auditory attention, there has been a growing amount of papers proposing ANNs supported by deep learning algorithms in conjunction with some mechanism to achieve symmetry with the human attention process. However, while these ANN approaches include attention, there is no categorization of attention integrated into the deep learning algorithms and their relation with human auditory attention. Therefore, we consider it necessary to have a review of the different ANN approaches inspired in attention to show both academic and industry experts the available models for a wide variety of applications. Based on the PRISMA methodology, we present a systematic review of the literature published since 2000, in which deep learning algorithms are applied to diverse problems related to speech processing. In this paper 133 research works are selected and the following aspects are described: (i) Most relevant features, (ii) ways in which attention has been implemented, (iii) their hypothetical relationship with human attention, and (iv) the evaluation metrics used. Additionally, the four publications most related with human attention were analyzed and their strengths and weaknesses were determined.
32
Rao, Shuyong, and Ye Yuan. "Online Monitoring of Power Grid Intelligent Voltage Stability considering Potential Factors." Mobile Information Systems 2022 (August 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/8760582.
Full textAbstract:
To improve the monitoring effect of voltage stability, an online monitoring method for intelligent voltage stability of the power grid is designed based on the consideration of potential factors. First, the converter is used as a voltage detecting device to collect the intelligent voltage data of the power grid, and then the characteristic quantity of power grid voltage is extracted by using the wavelet change method. Based on the practical criterion of voltage instability of smart grid, the quantitative evaluation factors that can reflect the severity of voltage instability are selected, and the voltage stability state is monitored online by wide-area measurement. The experimental results show that the proposed method has a high fitting degree between the voltage monitoring value and the actual voltage value, and the global maximum value of the transmission delay of the monitoring signal is only 90.57 ms, and the voltage identification rate can always remain above 95%, indicating that the proposed method has high effectiveness and timeliness in voltage stability monitoring. In addition, the Pearson coefficients of this method are in the range of 0.8∼1.0, indicating that the monitoring accuracy of voltage stability is higher, which proves that the design expectation is effectively realized.
33
Almuhaya, Mukarram A. M., Waheb A. Jabbar, Noorazliza Sulaiman, and Suliman Abdulmalek. "A Survey on LoRaWAN Technology: Recent Trends, Opportunities, Simulation Tools and Future Directions." Electronics 11, no. 1 (January 5, 2022): 164. http://dx.doi.org/10.3390/electronics11010164.
Full textAbstract:
Low-power wide-area network (LPWAN) technologies play a pivotal role in IoT applications, owing to their capability to meet the key IoT requirements (e.g., long range, low cost, small data volumes, massive device number, and low energy consumption). Between all obtainable LPWAN technologies, long-range wide-area network (LoRaWAN) technology has attracted much interest from both industry and academia due to networking autonomous architecture and an open standard specification. This paper presents a comparative review of five selected driving LPWAN technologies, including NB-IoT, SigFox, Telensa, Ingenu (RPMA), and LoRa/LoRaWAN. The comparison shows that LoRa/LoRaWAN and SigFox surpass other technologies in terms of device lifetime, network capacity, adaptive data rate, and cost. In contrast, NB-IoT technology excels in latency and quality of service. Furthermore, we present a technical overview of LoRa/LoRaWAN technology by considering its main features, opportunities, and open issues. We also compare the most important simulation tools for investigating and analyzing LoRa/LoRaWAN network performance that has been developed recently. Then, we introduce a comparative evaluation of LoRa simulators to highlight their features. Furthermore, we classify the recent efforts to improve LoRa/LoRaWAN performance in terms of energy consumption, pure data extraction rate, network scalability, network coverage, quality of service, and security. Finally, although we focus more on LoRa/LoRaWAN issues and solutions, we introduce guidance and directions for future research on LPWAN technologies.
34
Ding, Yi, Baoshen Guo, Lin Zheng, Mingming Lu, Desheng Zhang, Shuai Wang, Sang Hyuk Son, and Tian He. "A City-Wide Crowdsourcing Delivery System with Reinforcement Learning." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–22. http://dx.doi.org/10.1145/3478117.
Full textAbstract:
The revolution of online shopping in recent years demands corresponding evolution in delivery services in urban areas. To cater to this trend, delivery by the crowd has become an alternative to the traditional delivery services thanks to the advances in ubiquitous computing. Notably, some studies use public transportation for crowdsourcing delivery, given its low-cost delivery network with millions of passengers as potential couriers. However, multiple practical impact factors are not considered in existing public-transport-based crowdsourcing delivery studies due to a lack of data and limited ubiquitous computing infrastructures in the past. In this work, we design a crowdsourcing delivery system based on public transport, considering the practical factors of time constraints, multi-hop delivery, and profits. To incorporate the impact factors, we build a reinforcement learning model to learn the optimal order dispatching strategies from massive passenger data and package data. The order dispatching problem is formulated as a sequential decision making problem for the packages routing, i.e., select the next station for the package. A delivery time estimation module is designed to accelerate the training process and provide statistical delivery time guarantee. Three months of real-world public transportation data and one month of package delivery data from an on-demand delivery platform in Shenzhen are used in the evaluation. Compared with existing crowdsourcing delivery algorithms and widely used baselines, we achieve a 40% increase in profit rates and a 29% increase in delivery rates. Comparison with other reinforcement learning algorithms shows that we can improve the profit rate and the delivery rate by 9% and 8% by using time estimation in action filtering. We share the data used in the project to the community for other researchers to validate our results and conduct further research.1 [1].
35
Trihinas, Demetris, Michalis Agathocleous, Karlen Avogian, and Ioannis Katakis. "FlockAI: A Testing Suite for ML-Driven Drone Applications." Future Internet 13, no. 12 (December 16, 2021): 317. http://dx.doi.org/10.3390/fi13120317.
Full textAbstract:
Machine Learning (ML) is now becoming a key driver empowering the next generation of drone technology and extending its reach to applications never envisioned before. Examples include precision agriculture, crowd detection, and even aerial supply transportation. Testing drone projects before actual deployment is usually performed via robotic simulators. However, extending testing to include the assessment of on-board ML algorithms is a daunting task. ML practitioners are now required to dedicate vast amounts of time for the development and configuration of the benchmarking infrastructure through a mixture of use-cases coded over the simulator to evaluate various key performance indicators. These indicators extend well beyond the accuracy of the ML algorithm and must capture drone-relevant data including flight performance, resource utilization, communication overhead and energy consumption. As most ML practitioners are not accustomed with all these demanding requirements, the evaluation of ML-driven drone applications can lead to sub-optimal, costly, and error-prone deployments. In this article we introduce FlockAI, an open and modular by design framework supporting ML practitioners with the rapid deployment and repeatable testing of ML-driven drone applications over the Webots simulator. To show the wide applicability of rapid testing with FlockAI, we introduce a proof-of-concept use-case encompassing different scenarios, ML algorithms and KPIs for pinpointing crowded areas in an urban environment.
36
Ren, Ranci, John W. Castro, Silvia T. Acuña, and Juan de Lara. "Evaluation Techniques for Chatbot Usability: A Systematic Mapping Study." International Journal of Software Engineering and Knowledge Engineering 29, no. 11n12 (November 2019): 1673–702. http://dx.doi.org/10.1142/s0218194019400163.
Full textAbstract:
Background: The use of chatbots has increased considerably in recent years. These are used in different areas and by a wide variety of users. Due to this fact, it is essential to incorporate usability in their development. Aim: Our objective is to identify the state-of-the-art in chatbot usability and applied human–computer interaction techniques, to analyze how to evaluate chatbot usability. Method: We have conducted a systematic mapping study, by searching the main scientific databases. The search retrieved 170 references and 21 articles were retained as primary studies. Results: The works were categorized according to four criteria: usability techniques, usability characteristics, research methods and type of chatbots. Conclusions: Chatbot usability is still a very incipient field of research where the published studies are mainly surveys, usability tests, and rather informal experimental studies. Hence, it becomes necessary to perform more formal experiments to measure user experience, and exploit these results to provide usability-aware design guidelines.
37
Marahatta, Anup, Yaju Rajbhandari, Ashish Shrestha, Ajay Singh, Anup Thapa, Francisco Gonzalez-Longatt, Petr Korba, and Seokjoo Shin. "Evaluation of a LoRa Mesh Network for Smart Metering in Rural Locations." Electronics 10, no. 6 (March 22, 2021): 751. http://dx.doi.org/10.3390/electronics10060751.
Full textAbstract:
Accompanying the advancement on the Internet of Things (IoT), the concept of remote monitoring and control using IoT devices is becoming popular. Digital smart meters hold many advantages over traditional analog meters, and smart metering is one of application of IoT technology. It supports the conventional power system in adopting modern concepts like smart grids, block-chains, automation, etc. due to their remote load monitoring and control capabilities. However, in many applications, the traditional analog meters still are preferred over digital smart meters due to the high deployment and operating costs, and the unreliability of the smart meters. The primary reasons behind these issues are a lack of a reliable and affordable communication system, which can be addressed by the deployment of a dedicated network formed with a Low Power Wide Area (LPWA) platform like wireless radio standards (i.e., LoRa devices). This paper discusses LoRa technology and its implementation to solve the problems associated with smart metering, especially considering the rural energy system. A simulation-based study has been done to analyse the LoRa technology’s applicability in different architecture for smart metering purposes and to identify a cost-effective and reliable way to implement smart metering, especially in a rural microgrid (MG).
38
Adil Ali, Ali, Saadi Mohammed Saadi, Tameem Mohammed Mahmood, and Salama A. Mostafa. "A smart water grid network for water supply management systems." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1706–14. http://dx.doi.org/10.11591/eei.v11i3.3227.
Full textAbstract:
This paper proposes a smart water grids network (SWGN) architecture that combines the advantages of fog computing, internet of things (IoT), long range wide area network (LoRaWAN), and Software-defined networking (SDN). The main aims of the SWG architecture are to optimize data routing and monitor water supply and quality in real-time. SWGN handles a vast amount of data that is collected by IoT devices from different points related to water supply and quality. The data is processed in a distributed way by a number of fog servers that are located at the edge of the network. The fog controllers are deployed at the fog layer in order to take action locally for frequent events. The cloud layer has a cloud controller to take actions globally for infrequent events. The LoRaWAN provides communication technology that allows devices to operate regularly. The SDN technology decouples network traffic to control data routing decisions efficiently. A primitive evaluation under the Mininet emulator, focusing on SDN, shows the feasibility and efficiency of the architecture.
39
Zubenko, D., S. Zakurday, and O. Donets. "Use of neural networks in the problems of operation and repair of rolling stock of electric transport." Lighting engineering and power engineering 2, no. 58 (September 30, 2020): 10–12. http://dx.doi.org/10.33042/2079-424x-2020-2-58-10-12.
Full textAbstract:
Deep neural networks have achieved great success in controlled learning problems, and MIL as a typical poorly controlled learning method is effective for many applications in computer vision, biometrics, natural language processing, etc. In this article, we review several neural networks with multiple instances ( MINN), which neural networks seek to solve MIL problems. MINNs perform MILs in the end, which take bags with different numbers of instances as input and directly output the tags of the bags. All parameters in MINN can be optimized by back propagation. In addition to revising old MINNs, we offer a new type of MINN for exploring bag representations, which differs from existing MINNs that focus on the evaluation of an instance label. In addition, recent tricks developed in deep learning have been studied in MINN; we find that deep supervision is effective for a better understanding of bag views. In experiments, the proposed MINNs achieve the most advanced or competitive performance on several MIL tests. Moreover, for testing and learning it is very fast, for example, it takes only 0.0.0 03 s to predict the bag and a few seconds to learn on the MIL datasets on a moderate processor. Initially, several instances (MILs) were proposed to predict bounce activity [1]. Now it is widely applied to many domains and is an important problem in computer training. Many multimedia data have a multiplier (MI) structure; For example, a text article contains several paragraphs, the image can be divided into several local areas, and gene expression data contains several genes. MIL is useful for processing and understanding MI data. Studying multiple instances is a type of weak controlled learning (WSL). Each sample is executed in the form of labeled data, which consist of a wide variety of instances associated with the functions of input. The purpose of MIL in the dual task is to prepare a classifier for prediction of test packet labels, based on the assumption that there is a positive packet and contains at least one positive instance, while the data is negative if it consists only of negative instances.
40
Cho, Choongsang, Young Han Lee, Jongyoul Park, and Sangkeun Lee. "A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation." Electronics 10, no. 3 (February 2, 2021): 348. http://dx.doi.org/10.3390/electronics10030348.
Full textAbstract:
Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.
41
Mamis, Subria. "Teknologi Informasi Dan Komunikasi Sebagai Media Baru Dalam Komponen Pembelajaran." Al-MUNZIR 13, no. 2 (November 23, 2020): 253. http://dx.doi.org/10.31332/am.v13i2.2049.
Full textAbstract:
AbstrakTulisan ini dimaksud untuk mendeskripsikan media baru, Ujian Nasional Berbasis Komputer (UNBK), sebagai wahana evaluasi pembelajaran yang bertujuan untuk melihat sejauh mana teknologi informasi dan komunikasi merambah kedunia pendidikan. Pengumpulan data dilakukan dengan wawancara mendalam kepada pihak terkait, observasi dan juga dokumentasi. Hasil-hasil temuan saintifik yang diperoleh adalah bahwa Media Baru secara kualitas memberikan evaluasi yang unggul dengan menampilkan sifatnya yang memberikan efisiensi biaya, bahan, dan waktu penyelenggaranan ujian. Dengan media baru jangkauan wilayah yang luas dapat dicapai. Selain itu, sifat transparansinya dalam mekanisme penyajian soal dan juga dalam keamanan naskah menjadi akibat hadirnya new media di ranah pendidikan. Tulisan ini dikaji dengan pendekatan teori New Media yang merefleksikan media evaluasi belajar siswa yang ditandai dengan penggunaan jaringan internet, Teori Determinisme Teknologi yang mengimplikaskan penggunaan teknologi sebagai faktor dominan yang berpengaruh dalam media evaluasi belajar, Teori Konstruktivisme pendidikan yang megindikasikan siswa belajar aktif dengan kehadiran new media dalam evaluasi belajar dan juga Teori Difusi Inovasi yang merepresentasikan inovasi kebijakan pemerintah yang didiseminasikan kepada sekolah-sekolah penyelenggara ujian berbasis komputer. Sehingga, dapat disimpulkan bahwa presensi new media mengubah perubahan perilaku dari satu sisi evaluasi pembelajaran.Kata Kunci : Media baru, Evaluasi Pembelajaran, Ujian Nasional Berbasis KomputerAbstractThis article is intended to describe the new media for the Computer-Based National Examination (UNBK) as a vehicle for evaluating learning that aims to see the extent to which information and communication technology has penetrated the world of education. Data collection was carried out by in-depth interviews with related parties, observation and even documentation. The scientific findings obtained are that New Media gives a superior evaluation quality by displaying its properties that provide cost efficiency, materials, and test execution time. With new media a wide area coverage can be achieved. In addition, the transparency in the mechanism for presenting questions and also in safeguarding texts is the result of the presence of new media in the field of education. This paper discusses the New Media theory approach which reflects the media for evaluating student learning characterized by the use of internet networks; The theory of technological determinism; which implies the use of technology as the dominant influencing factor in learning evaluation media, the theory of educational constructivism; which indicates students learn actively with the presence of new media in learning evaluation, and also the Innovation Diffusion Theory; which represents government policy innovation disseminated to school administrators computer based exam. So, it can be concluded that the presence of new media changes behavior change from one side of learning evaluation.Keywords: New Media, Learning Evaluation, Computer-Based National Examination
42
Song, Jiancheng, Liang Liu, Yulei Liu, Jie Xi, and Wenbin Zhai. "Path Planning for Multi-Vehicle-Assisted Multi-UAVs in Mobile Crowdsensing." Wireless Communications and Mobile Computing 2022 (January 20, 2022): 1–21. http://dx.doi.org/10.1155/2022/9778188.
Full textAbstract:
Due to the capability of fast deployment and controllable mobility, unmanned aerial vehicles (UAVs) play an important role in mobile crowdsensing (MCS). However, constrained by limited battery capacity, UAVs cannot serve a wide area. In response to this problem, the ground vehicle is introduced and used to transport, release, and recycle UAVs. However, existing works only consider a special scenario: one ground vehicle with multiple UAVs. In this paper, we consider a more general scenario: multiple ground vehicles with multiple UAVs. We formalize the multi-vehicle-assisted multi-UAV path planning problem, which is a joint route planning and task assignment problem (RPTSP). To solve RPTSP, an efficient multi-vehicle-assisted multi-UAV path planning algorithm (MVP) is proposed. In MVP, we first allocate the detecting points to proper parking spots and then propose an efficient heuristic allocation algorithm EHA to plan the paths of ground vehicles. Besides, a genetic algorithm and reinforcement learning are utilized to plan the paths of UAVs. MVP maximizes the profits of an MCS carrier with a response time constraint and minimizes the number of employed vehicles. Finally, performance evaluation demonstrates that MVP outperforms the baseline algorithm.
43
Bensaci, Ramla, Belal Khaldi, Oussama Aiadi, and Ayoub Benchabana. "Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation." Applied Sciences 11, no. 21 (October 29, 2021): 10176. http://dx.doi.org/10.3390/app112110176.
Full textAbstract:
Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.
44
Gavish, Bezalel. "Configuring wide area computer networks." OR Spektrum 14, no. 3 (September 1992): 115–28. http://dx.doi.org/10.1007/bf01783514.
Full text
45
Ali, Md Sadek, Yu Li, Md Khalid Hossain Jewel, Oluwole John Famoriji, and Fujiang Lin. "Channel Estimation and Peak-to-Average Power Ratio Analysis of Narrowband Internet of Things Uplink Systems." Wireless Communications and Mobile Computing 2018 (July 5, 2018): 1–15. http://dx.doi.org/10.1155/2018/2570165.
Full textAbstract:
Narrowband Internet of Things (NB-IoT) is a cellular based promising low-power wide-area network (LPWN) technology standardized by the 3rd Generation Partnership Project (3GPP) in release-13 as a part of the future 5th Generation (5G) wireless communication systems. The main design target of NB-IoT was to enhance radio coverage by repeating signal over an additional period of time for the ultralow-end IoT devices that would be operated in extreme coverage environments. But the power efficiency of the low-cost NB-IoT user equipment (NB-IoT UE) in the uplink is the major concern. Coverage improvement from signal repetitions depends on the channel estimation quality at extremely bad radio conditions. The typical operating signal-to-noise ratio (SNR) for NB-IoT is expected to be much lower than the zero. In this paper, we have proposed two efficient narrowband demodulation reference signal (NDMRS)-assisted channel estimation algorithms based on the conventional least squares (LS) and minimum mean square error (MMSE) estimation methods. The theoretical analysis and the link-level performance of our proposed estimation methods are presented. Simulation results exhibit that the proposed methods provide better estimation precision compared to the traditional LS and MMSE methods at the low SNR situations. Furthermore, we have analyzed the raised-cosine (RC) and square-root-raised cosine (RRC) pulse shaping to reduce peak-to-average power ratio (PAPR) as an uplink transmit filter. The PAPR values are evaluated through extensive computer simulations for both single-tone and multi-tone transmissions. Our evaluation results vindicate that the RRC pulse shaping with lower PAPR values is feasible to design of practical NB-IoT uplink transmitter and increases power efficiency.
46
Ahmad, Sultan, Sudan Jha, Hikmat A. M. Abdeljaber, Mohammad Khalid Imam Rahmani, Mohammad Maqbool Waris, Ajit Singh, and Muhammad Yaseen. "An Integration of IoT, IoC, and IoE towards Building a Green Society." Scientific Programming 2022 (July 5, 2022): 1–8. http://dx.doi.org/10.1155/2022/2673753.
Full textAbstract:
Energy waste altogether adds to expanded expenses in the car fabricating industry, which is liable to energy use limitations and tax assessment from national and global strategy creators and confinements and charges from national energy suppliers. This checking is essential for energy sparing since it empowers organizations to roll out operational improvements to diminish energy utilization and expenses. The primary test to energy observation is the need to incorporate assembling and energy checking and control gadgets that help diverse correspondence conventions and are generally dispersed over a wide region. One of the most significant challenges in the advancement of the Internet of Things (IoT) has been the powering of billions of connected devices. Evaluation of digital services considering an energy impression of the Internet normally requires models of the energy intensity of the Internet. A typical way to deal with the display of the energy intensity is to consolidate assessments of market studies of introduced gadgets on a national or worldwide scale and their related power utilization with the aggregate information volume transported at a similar scale. Energy sources are a fundamental part of society development, and a steady power supply is essential for today’s progress. End-use energy is transferred to various consumers via power transmission and circulation networks after being transformed to optional energy as electricity by various power facilities. The power grid serves as the physical stage for both wide-area electric power sharing and display exchanges, and it is at the heart of auxiliary energy sources. In this manner, it attempts to connect the part of a center point between essential energy and end-use energy. With the bidirectional power stream given by the Energy Internet, different techniques are elevated to enhance and increase the energy usage between Energy Internet and Main-Grid. Energy proficiency and, in addition, quick information transmission are fundamental to green correspondences-based applications for IoT. Here, we are trying to provide a state-of-the-art survey over various Internet of Energy techniques along with IoT.
47
Adas, Ahmed A. A. "Evaluation methodology for wide area networks." Computer Networks and ISDN Systems 18, no. 2 (February 1990): 115–31. http://dx.doi.org/10.1016/0169-7552(90)90139-j.
Full text
48
Sarpinah Sh Naimullah, Bibi, Kasumawati Lias, Norlida Buniyamin, Ahmad Tirmizi Jobli, and Mazlina Mansor Hassan. "Specific absorption rate distribution evaluation in a different substrate for hyperthermia treatment." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (September 1, 2022): 1311. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1311-1319.
Full textAbstract:
Hyperthermia treatment procedure (HTP) isatreatment that uses high heatgenerated from electromagnetic (EM) waves, which is about 42 °C to 45 °C within a particular duration. However, poor focus position distance on the treated tissue has become a significant concern among the researchers since it may contribute to a wide area of unwanted hot spots, which lead to severe adverse health effects on healthy tissue.This paper presents a specific absorption rate (SAR) distribution evaluation of different microstrip antenna substrates with different electrical permittivities, contributing to different sizes of microstrip antenna patches, which then provide different attainment of the SAR distribution on the treated tissue. Operating frequencies of 434MHz, 915 MHz, and 2,450 MHz with 10 W operating powerare utilized. ASEMCAD X is used to conduct a simulation in obtaining the SAR distribution, which determines the focus position distance on different tumour (malignant tissue) sizes. Based on the results, the suitable substrate for frequency 915 MHz and 2,450MHz is RT5880, and RT5870, while RO3210 and RT6010 performed their best at 434MHz and 2,450MHz. The finding of this study can be used for further research in optimizing microstrip antenna development for HTP.
49
Li, Juan. "Design of an Effective Archive Management System with a Compression Approach for Network Information Technology." Wireless Communications and Mobile Computing 2022 (April 1, 2022): 1–10. http://dx.doi.org/10.1155/2022/3503841.
Full textAbstract:
Due to the recent advances of Internet and information technologies, massive quantity of archive data gets generated and it becomes difficult to handle it using conventional techniques. Archive management is the field of management related to the maintenance and utilization of archives, once they have been sent from the client to the repository. The drastic increase in the size of archive data necessitates effective storage schemes, which can be accomplished by the use of data compression approaches. Generally, data compression techniques are used for reducing the count of data being saved from a system or network without compromising the data quality. With this motivation, this study designs an effective archive storage system with a compression approach for network management (EASS-CANM). The major intention of the EASS-CANM technique is to archive the textual and image data effectively in its compact form in order to reduce the storage area. In the context of archive management, the EASS-CANM technique might be considered a successful instrument. The proposed EASS-CANM technique involves a two-stage process: textual data compression and image compression. At the initial stage, neighborhood indexing sequence (NIS) with the Prediction by Partial Matching (PPM) technique was applied for textual data compression. Secondly, fruit fly optimization (FFO) with modified Haar wavelet (MHW) is used for effective image compression where the optimal threshold selection process takes place utilizing the FFO technique. We improved the Haar wavelet filtering process in order to preserve higher image quality and clarity (MHW). With the introduction of MHW, a new transformation is made possible, allowing for improved compression outcomes as well as improved PSNR and CR values. In order to demonstrate the improved outcomes of the EASS-CANM approach, a series of simulations are performed utilizing a benchmark dataset. The experimental results reported the supremacy of the EASS-CANM technique on existing approaches. The benchmark dataset is used to conduct a wide range of studies to see whether the EASS-CANM approach improves archival efficacy. According to the full comparative result analysis, the EASS-CANM strategy is more effective than existing approaches in terms of numerous evaluation criteria. Therefore, the EASS-CANM technique can be used effectively in the administration of archives.
50
Kandula, Srikanth, Ishai Menache, Roy Schwartz, and Spandana Raj Babbula. "Calendaring for wide area networks." ACM SIGCOMM Computer Communication Review 44, no. 4 (February 25, 2015): 515–26. http://dx.doi.org/10.1145/2740070.2626336.
Full text