Journal articles: 'Massive Internet-of-things'

1

Lee, Byung Moo, and Hong Yang. "Massive MIMO With Massive Connectivity for Industrial Internet of Things." IEEE Transactions on Industrial Electronics 67, no. 6 (2020): 5187–96. http://dx.doi.org/10.1109/tie.2019.2924855.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Zhou, Hui, Changyang She, Yansha Deng, Mischa Dohler, and Arumugam Nallanathan. "Machine Learning for Massive Industrial Internet of Things." IEEE Wireless Communications 28, no. 4 (2021): 81–87. http://dx.doi.org/10.1109/mwc.301.2000478.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Bana, Alexandru-Sabin, Elisabeth de Carvalho, Beatriz Soret, et al. "Massive MIMO for Internet of Things (IoT) connectivity." Physical Communication 37 (December 2019): 100859. http://dx.doi.org/10.1016/j.phycom.2019.100859.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Hasegawa, So, Ryoma Kitagawa, Aohan Li, et al. "Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks." Applied Sciences 12, no. 15 (2022): 7424. http://dx.doi.org/10.3390/app12157424.

Full text

Abstract:

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

APA, Harvard, Vancouver, ISO, and other styles

5

Farhad, Arshad, Dae-Ho Kim, and Jae-Young Pyun. "Resource Allocation to Massive Internet of Things in LoRaWANs." Sensors 20, no. 9 (2020): 2645. http://dx.doi.org/10.3390/s20092645.

Full text

Abstract:

A long-range wide area network (LoRaWAN) adapts the ALOHA network concept for channel access, resulting in packet collisions caused by intra- and inter-spreading factor (SF) interference. This leads to a high packet loss ratio. In LoRaWAN, each end device (ED) increments the SF after every two consecutive failed retransmissions, thus forcing the EDs to use a high SF. When numerous EDs switch to the highest SF, the network loses its advantage of orthogonality. Thus, the collision probability of the ED packets increases drastically. In this study, we propose two SF allocation schemes to enhance the packet success ratio by lowering the impact of interference. The first scheme, called the channel-adaptive SF recovery algorithm, increments or decrements the SF based on the retransmission of the ED packets, indicating the channel status in the network. The second approach allocates SF to EDs based on ED sensitivity during the initial deployment. These schemes are validated through extensive simulations by considering the channel interference in both confirmed and unconfirmed modes of LoRaWAN. Through simulation results, we show that the SFs have been adaptively applied to each ED, and the proposed schemes enhance the packet success delivery ratio as compared to the typical SF allocation schemes.

APA, Harvard, Vancouver, ISO, and other styles

6

Kim-Hung, Le, and Quan Le-Trung. "User-Driven Adaptive Sampling for Massive Internet of Things." IEEE Access 8 (2020): 135798–810. http://dx.doi.org/10.1109/access.2020.3011496.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Lin, Lian Shi, Qing Hu, and Yu Ping Qui. "Information Processing and Key Technology Based on Internet of Thing Architecture for Intelligent Refrigerator." Applied Mechanics and Materials 278-280 (January 2013): 2012–15. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2012.

Full text

Abstract:

The Internet of things is a massive electronic equipment with internet interconnection of large scale virtual networks, including RFID, sensor and actuator electronic devices by the internet interconnection. In order to solve internet of things architecture intelligent refrigerator key technologies, The paper had discussed the internet of things architecture intelligent refrigerator definition, characteristic as well as reference architecture, focused on analysis intelligent refrigerator information space definition, information quantification method and mobile platform equipment internet of things key technology main problems and corresponding solution ways.

APA, Harvard, Vancouver, ISO, and other styles

8

Wiklundh, Kia, and Peter Stenumgaard. "EMC challenges for the era of massive Internet of Things." IEEE Electromagnetic Compatibility Magazine 8, no. 2 (2019): 65–74. http://dx.doi.org/10.1109/memc.2019.8753447.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Lv, Zhihan, Ranran Lou, Jinhua Li, Amit Kumar Singh, and Houbing Song. "Big Data Analytics for 6G-Enabled Massive Internet of Things." IEEE Internet of Things Journal 8, no. 7 (2021): 5350–59. http://dx.doi.org/10.1109/jiot.2021.3056128.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Balcı, Abdullah, and Radosveta Sokullu. "Massive connectivity with machine learning for the Internet of Things." Computer Networks 184 (January 2021): 107646. http://dx.doi.org/10.1016/j.comnet.2020.107646.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Omran Almagrabi, Alaa, Rashid Ali, Daniyal Alghazzawi, Bander A. Alzahrani, and Fahad M. Alotaibi. "Network Learning-Enabled Sensor Association for Massive Internet of Things." Computer Systems Science and Engineering 47, no. 1 (2023): 843–53. http://dx.doi.org/10.32604/csse.2023.037652.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Jiang, Wei, and Feng Yang. "Applied-Information Technology in Research of Security Processing Based on IOT Data." Advanced Materials Research 908 (March 2014): 509–12. http://dx.doi.org/10.4028/www.scientific.net/amr.908.509.

Full text

Abstract:

Internet of things (IOT) has become an important trend in the development of information technology. How to deal with huge amounts of internet data is becoming more and more important. In this paper, we have a further research in the technology of applied-information about mass content of network data security processing model. This model is mainly composed of massive internet of data acquisition, data storage, based on the rules of mass data processing and data security management, etc. The model can be applied to all kinds of massive internet data monitoring system based on rules, such as: the lake water quality monitoring system based on Internet of things, PM2.5 monitoring system, and so on.

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Aihua, Peisen Wang, Xiaqing Miao, Xiangming Li, Neng Ye, and Yun Liu. "A review on non-terrestrial wireless technologies for Smart City Internet of Things." International Journal of Distributed Sensor Networks 16, no. 6 (2020): 155014772093682. http://dx.doi.org/10.1177/1550147720936824.

Full text

Abstract:

Smart City Internet of Things will become a fundamental infrastructure to support massive machine-type communications between the widely deployed sensors serving big cities. Since there exists many location constraints for the existing terrestrial Internet of Things, the non-terrestrial Internet of Things sheds light on breaking these limits. Therefore, this article conducts a comprehensive survey on non-terrestrial Internet of Things technologies for Smart City, which is an important complement to terrestrial Internet of Things. We first present the application scenarios of Internet of Things and point out where the existing terrestrial Internet of Things cannot work perfectly. Two non-terrestrial Internet of Things technical proposals are then introduced, namely satellite Internet of Things and unmanned aerial vehicle Internet of Things. However, the focuses of these non-terrestrial Internet of Things are distinct, that is, the major problems of satellite and unmanned aerial vehicle Internet of Things are the high dynamic nature of channel and high maneuverability of unmanned aerial vehicles, respectively. The key technologies for satellite and unmanned aerial vehicle Internet of Things are then reviewed separately. Both physical and non-physical layer technologies are surveyed for satellite Internet of Things, and the route planning is mainly investigated for the unmanned aerial vehicle Internet of Things. Finally, we draw a conclusion and give some potential research directions of non-terrestrial Internet of Things.

APA, Harvard, Vancouver, ISO, and other styles

14

Long, Rong, Xiaohui Fan, Kai Wei, Junxuan Bai, and Shanpeng Xiao. "Internet-of-Things object model." Digital Twin 2 (April 12, 2022): 5. http://dx.doi.org/10.12688/digitaltwin.17562.1.

Full text

Abstract:

Background: With the advancement of communication technology and advanced sensors, there are massive demands for Internet-of-Things (IoT) applications in buildings, communities, factories, parks, etc. Accessing IoT devices provides convenience for scene management and monitoring, ameliorating production and life intelligently. However, due to the lack of a unified model for IoT devices, data is often skipped over IoT platforms and transmitted to applications directly. This leads to the fact that each manufacturer needs to produce its devices and develop its customized software, which hugely increases the development cycle. On the other hand, it is difficult to convey information between different systems, limiting cross- system control. Moreover, digital twin relies on large amounts of heterogeneous data, and it is impracticable to provide enough data without a unified model for device description. Methods: First, we illustrate the motivation, design goals, and design principles for creating the Internet-of-Things Object Model (IoT-OM). Then we propose a unified description to define IoT devices. The proposed concept has been accepted by several companies, and we analyse one platform that adopts the model. To demonstrate the effectiveness of the model, we introduce two projects based on the platform. One project is an intelligent fire protection system, and another project is an intelligent air quality monitoring system. Results: We measured the time taken by five companies when developing IoT devices and their applications, including the development cycle duration without utilizing the proposed model and the duration using the model at China Mobile’s OneNET platform. The results prove that the proposed model can significantly shorten the development cycle. Conclusions: This paper proposes a model for IoT devices, which helps to unify heterogeneous data among different manufacturers and helps to shorten the development cycles for developers.

APA, Harvard, Vancouver, ISO, and other styles

15

Kiran, Vinayak Shanbhag, and Sathish Dayakshini. "Low complexity physical layer security approach for 5G internet of things." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6466–75. https://doi.org/10.11591/ijece.v13i6.pp6466-6475.

Full text

Abstract:

Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).

APA, Harvard, Vancouver, ISO, and other styles

16

Li, Xue Mei, Liang Quan Ge, Zhang Jian Xin, and Chuan Chen. "Study on Security Problems of the Internet of Things." Applied Mechanics and Materials 303-306 (February 2013): 2425–28. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.2425.

Full text

Abstract:

Internet of things is a worldwide network which based on mass micro-devices, using the new generation as the backbone, and will be used in traditional and emerging industries. The possibility of seamlessly merging the real and the virtual world, through the massive deployment of embedded devices, opens up new exciting directions for both research and business. In this survey article, we provided an overview of the key issues such as security problems related to the development of IoT technologies and services.

APA, Harvard, Vancouver, ISO, and other styles

17

Shanbhag, Kiran Vinayak, and Dayakshini Sathish. "Low complexity physical layer security approach for 5G internet of things." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6466. http://dx.doi.org/10.11591/ijece.v13i6.pp6466-6475.

Full text

Abstract:

Fifth-generation (5G) massive machine-type communication (mMTC) is expected to support the cellular adaptation of internet of things (IoT) applications for massive connectivity. Due to the massive access nature, IoT is prone to high interception probability and the use of conventional cryptographic techniques in these scenarios is not practical considering the limited computational capabilities of the IoT devices and their power budget. This calls for a lightweight physical layer security scheme which will provide security without much computational overhead and/or strengthen the existing security measures. Here a shift based physical layer security approach is proposed which will provide a low complexity security without much changes in baseline orthogonal frequency division multiple access (OFDMA) architecture as per the low power requirements of IoT by systematically rearranging the subcarriers. While the scheme is compatible with most fast Fourier transform (FFT) based waveform contenders which are being proposed in 5G especially in mMTC and ultra-reliable low latency communication (URLLC), it can also add an additional layer of security at physical layer to enhanced mobile broadband (eMBB).

APA, Harvard, Vancouver, ISO, and other styles

18

Yu, Guanghua, Xiaoming Chen, and Derrick Wing Kwan Ng. "Low-Cost Design of Massive Access for Cellular Internet of Things." IEEE Transactions on Communications 67, no. 11 (2019): 8008–20. http://dx.doi.org/10.1109/tcomm.2019.2933208.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Shao, Xiaodan, Xiaoming Chen, Caijun Zhong, Junhui Zhao, and Zhaoyang Zhang. "A Unified Design of Massive Access for Cellular Internet of Things." IEEE Internet of Things Journal 6, no. 2 (2019): 3934–47. http://dx.doi.org/10.1109/jiot.2019.2893376.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Zhang, Bi Ying, Wen Hu, Jian Feng, and Wen He Sun. "Data Classification in Internet of Things Based on Evolutionary Neural Network." Advanced Materials Research 659 (January 2013): 202–7. http://dx.doi.org/10.4028/www.scientific.net/amr.659.202.

Full text

Abstract:

Data classification is the foundation for the intelligent identification and management of massive information in the internet of things. To classify the massive data accurately, an evolutionary neural network is presented. The input features and the structure of neural network are evolved simultaneously to consider their joint contribution to the performance of neural network. The sensitivity analysis is performed to guide the evolutionary algorithm to search the optimum solution. It can be seen from the experimental results that the proposed evolutionary algorithm optimized the structure of neural network and eliminate the tedious input features at the same time. The excellent classification accuracy is achieved finally.

APA, Harvard, Vancouver, ISO, and other styles

21

K., Lathika. "INTERNET OF THINGS (IOT) - APPLICATIONS AND CHALLENGES." International Journal of Scientific Research and Modern Education 1, no. 2 (2016): 160–65. https://doi.org/10.5281/zenodo.198812.

Full text

Abstract:

The internet of things (IoT) as the new influence to the human world and machine world is emerging as a new high tide wave in the development of internet. Internet of things is expected to have massive impact on the customer of electric equipment’s, business which will all be integrated and synchronized and also the fact is these are the early days. Looking at the potential of the wide suitability to all most all the vectors of the core areas like business, industries, manufacturing consumer goods etc., has a very wide area of applicability. The information of the paper very specifically focus on the adoption of this concept in our homes, identifying the top players in the markets in the technologies which are driving the same. Bringing to light the statistical analysis of ongoing consumer sentiments about the new smart devices brings out the embryonic opportunities of internet of things.

APA, Harvard, Vancouver, ISO, and other styles

22

Sania, Talha, Sharanya Devunuri, and V. Sravan Kumar Dr. "Application of Data Mining Techniques using Internet of Things." International Journal of Engineering Research & Science 7, no. 6 (2021): 01–07. https://doi.org/10.5281/zenodo.5044990.

Full text

Abstract:

Abstract— The generation and growing power of computer science have boosted data collection, storage, and manipulation as data sets are broad in size and complexity level. Internet of Things (IOT) is the most popular term in describing this new interconnected world. The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. As more and more devices connected to IoT, the latest algorithms should be applied to IOT. This paper explores a systematic review of various data mining models as well as its applications in the Internet of things along with its advantages and disadvantages.

APA, Harvard, Vancouver, ISO, and other styles

23

Ali, Rashid, Imran Ashraf, Ali Kashif Bashir, and Yousaf Bin Zikria. "Reinforcement-Learning-Enabled Massive Internet of Things for 6G Wireless Communications." IEEE Communications Standards Magazine 5, no. 2 (2021): 126–31. http://dx.doi.org/10.1109/mcomstd.001.2000055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Lee, Byung Moo, and Hong Yang. "Massive MIMO for Industrial Internet of Things in Cyber-Physical Systems." IEEE Transactions on Industrial Informatics 14, no. 6 (2018): 2641–52. http://dx.doi.org/10.1109/tii.2017.2787988.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Xu, Shaoyi, Yang Liu, and Weiliang Zhang. "Grouping-Based Discontinuous Reception for Massive Narrowband Internet of Things Systems." IEEE Internet of Things Journal 5, no. 3 (2018): 1561–71. http://dx.doi.org/10.1109/jiot.2018.2789679.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Jia, Rundong, Xiaoming Chen, Qiao Qi, and Hai Lin. "Massive Beam-Division Multiple Access for B5G Cellular Internet of Things." IEEE Internet of Things Journal 7, no. 3 (2020): 2386–96. http://dx.doi.org/10.1109/jiot.2019.2958129.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Wang, Hui Zhe, Guo Wen Lin, Jian Qin Wang, Wan Lin Gao, Yi Fei Chen, and Qing Ling Duan. "Management of Big Data in the Internet of Things in Agriculture Based on Cloud Computing." Applied Mechanics and Materials 548-549 (April 2014): 1438–44. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1438.

Full text

Abstract:

Internet of Things (IoT) is playing a more and more important role in modern agriculture development. However, problems of efficient storing and reasoning those massive heterogeneous sensor data collected from variety kinds of sensing equipment need to be resolved to implement Internet of Things in agriculture. This paper explores the architecture of Internet of Things in agriculture with heterogeneous sensor data, and proposes a design of implementation to Internet of Things in agriculture based on cloud computing. The design is based on two-tier storage structure of HBase, which is a distributed database with high scalability. It access database using MapReduce model, a distributed programming framework. Hence, this design provides scalable storage, efficient data access, and eases other processing of sensor data.

APA, Harvard, Vancouver, ISO, and other styles

28

Dinev, D., and A. Haka. "RSSI study of wireless Internet of Things technologies." Journal of Physics: Conference Series 2339, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2339/1/012014.

Full text

Abstract:

Abstract The Internet of Things (IoT) idea now entails a dispersed collection of different sensor networks with several functions that collect data, which is then analysed and used in applications such as smart cities. These networks are capable of transmitting massive volumes of data in a relatively efficient, yet unsecure wireless environment. These applications will only succeed if is developed a dependable a low-cost real-time method for pinpointing accurate location. Power consumption is another consideration for indoor localisation. Recent wireless technologies like ZigBee, Bluetooth Low Energy (BLE) and Long Range (LoRa), use less resources, making them ideal for interior positioning. When IoT devices are utilised, these technologies are compared in terms of precision of localisation and power consumption. Tracking the Received Signal Strength Indicator (RSSI) values can be used to locate mobile sensor nodes for low-power IoT networks. The RSSI values of sensor nodes in the BLE, ZigBee and LoRa networks for IoT were explore in this study.

APA, Harvard, Vancouver, ISO, and other styles

29

Hardeep, Singh*. "SUBSET OF DOMAINS IMPACTED BY INTERNET OF THINGS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 2 (2018): 68–78. https://doi.org/10.5281/zenodo.1174135.

Full text

Abstract:

Internet of Things; abbreviated as IoT; is a new trend in connectivity which is outside the realm of laptops and smartphones. IoT is a recent communication paradigm in which the items of everyday life are embedded with micro-controllers, transceivers for digital communication, and with suitable protocol stacks that allow them to communicate with one another and also with the humans. These network connected items or devices use unique addressing schemes and are smart enough to share information with human, with the cloud based applications and with each other as device to device communication; hence automating our tasks and lessening efforts to almost zero. The IoT, with the prospect of seamlessly integrating the real and the virtual worlds through the massive deployment of embedded devices, has opened up many new domains of its applications. This paper will introduce a subset of domains of applications of IoT including smart homes, smart wearables, smart cars, smart cities and smart industries.

APA, Harvard, Vancouver, ISO, and other styles

30

Wang, SongSong, and Ouguan Xu. "Interoperability Structure of Smart Water Conservancy Based on Internet of Things." International Journal of Distributed Sensor Networks 2024 (May 16, 2024): 1–13. http://dx.doi.org/10.1155/2024/7724783.

Full text

Abstract:

Massive smart water conservancy object (WCO) need to be connected for real-time monitoring and control, which produces massive data. Unfortunately, heterogeneous data structures and semantics lead to low interoperability between WCO and management systems. To address this challenge, we propose a novel interoperability structure for a smart water conservancy system based on the Internet of Things (IoT), and the key design includes a smart WCO terminal, interoperability network, special interoperability protocol, WCO information model, and cloud platform. Universal terminal and network are the base of interoperability hardware, and special interoperability protocol and information model for interconnection of WCO are designed for smart water conservancy management system. WCO can be connected to a water conservancy Big Data processing cloud platform for interoperability applications. The application results demonstrate that our proposed WCO’s interoperability structure has obvious advantages than the general IoT at WCO interoperability. The interoperability protocol is reliable, the information model can ease interoperability and security, and the semantic dictionary is very rich and covers all semantic services of WCO.

APA, Harvard, Vancouver, ISO, and other styles

31

Shen, Haijie, Yangyuan Li, Xinzhi Tian, et al. "Mass data processing and multidimensional database management based on deep learning." Open Computer Science 12, no. 1 (2022): 300–313. http://dx.doi.org/10.1515/comp-2022-0251.

Full text

Abstract:

Abstract With the rapid development of the Internet of Things, the requirements for massive data processing technology are getting higher and higher. Traditional computer data processing capabilities can no longer deliver fast, simple, and efficient data analysis and processing for today’s massive data processing due to the real-time, massive, polymorphic, and heterogeneous characteristics of Internet of Things data. Mass heterogeneous data of different types of subsystems in the Internet of Things need to be processed and stored uniformly, so the mass data processing method is required to be able to integrate multiple different networks, multiple data sources, and heterogeneous mass data and be able to perform processing on these data. Therefore, this article proposes massive data processing and multidimensional database management based on deep learning to meet the needs of contemporary society for massive data processing. This article has deeply studied the basic technical methods of massive data processing, including MapReduce technology, parallel data technology, database technology based on distributed memory databases, and distributed real-time database technology based on cloud computing technology, and constructed a massive data fusion algorithm based on deep learning. The model and the multidimensional online analytical processing model of the multidimensional database based on deep learning analyze the performance, scalability, load balancing, data query, and other aspects of the multidimensional database based on deep learning. It is concluded that the accuracy of multidimensional database query data is as high as 100%, and the accuracy of the average data query time is only 0.0053 s, which is much lower than the general database query time.

APA, Harvard, Vancouver, ISO, and other styles

32

Ren, Daowen, and Haiyan Wu. "Design and Implementation of Enterprise Financial Risk Control Information Management System Based on Big Data of Internet of Things." Mobile Information Systems 2022 (August 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/5677870.

Full text

Abstract:

The Internet of Things is a huge network. It is a combination of massive sensing devices and the Internet. In the Internet of Things, a large number of sensing devices are continuously collecting data and sending it to the data center. Data present massive characteristics, forming the big data of the Internet of Things. With the rapid development of informatization and network technology, almost all domestic enterprises have paid more and more attention to the research of enterprise network, informatization, and interactive experience. Under the background of the rapid development of e-commerce, the enterprise financial risk control information management system is bound to become the trend of information development. In the process of system analysis, this paper considers the sustainable development needs of the actual business of the enterprise financial risk control information management system and makes an in-depth study on the management and technology of the system development in strict accordance with the business process optimization and principles of the enterprise financial risk control information management system. This paper proposes to introduce the technology of Internet of Things into the enterprise financial risk control information management system, build the application mode framework of Internet of Things for enterprise asset management, and focus on the in-depth study of key technologies such as data collection and information transmission. The experimental results show that the time cost of sensor clustering is 1% of hierarchical clustering. In the worst case, the time cost of sensor clustering only accounts for 1/14 of hierarchical clustering.

APA, Harvard, Vancouver, ISO, and other styles

33

CHRISTOPHE, BENOIT. "MANAGING MASSIVE DATA OF THE INTERNET OF THINGS THROUGH COOPERATIVE SEMANTIC NODES." International Journal of Semantic Computing 06, no. 04 (2012): 389–408. http://dx.doi.org/10.1142/s1793351x12400120.

Full text

Abstract:

The Internet of Things refers to extending the Internet to physical entities of interest (EoI) to humans (e.g., a table, a room or another human being) sensed as a set of properties that can be observed, measured, accessed or triggered by devices such as actuators, sensors or other smart components. In this vision, the IoT foresees novel types of applications dynamically finding the associations between devices and EoIs around a common feature of interest (e.g., temperature of a room) to provide meaningful information as well as rich services to users about the things they are interested in. Growing interest in providing sensors and actuators has led to billions of services or data offered through different platforms, some of them wrapped with semantic descriptions to realize aforementioned associations through accurate search processes. However, due to the ubiquitous aspect of the IoT and the potential mobility of the devices that enable it, a centralized approach does not allow designing scalable processes to efficiently search and manage these associations or the devices and EoIs that compose them. As location seems to be an important parameter when searching the IoT, we believe that designing a framework composed of geographically distributed nodes with local reasoning capabilities is a much more scalable approach to realize the IoT vision. We describe our approach of such a vision by creating a federated network composed of such nodes that declare their location based on a formal model. In this vision, each node is capable of processing semantic descriptions of devices or EoIs to share deduced associations with other peers that are selected based on their location nearness.

APA, Harvard, Vancouver, ISO, and other styles

34

Cheng, Diancheng, Fan Wu, Cong Zhang, and Yuan’an Liu. "Adaptive Multi-Source Ambient Backscatter Communication Technique for Massive Internet of Things." Electronics 14, no. 8 (2025): 1532. https://doi.org/10.3390/electronics14081532.

Full text

Abstract:

Ambient backscatter communication (AmBC) has been regarded as an energy- and spectrum-efficient backscatter scheme for the massive Internet of Things (IoT). However, most existing AmBC systems are non-adaptive end-to-end systems, which cannot fully accommodate the forthcoming massive communications of the sixth-generation (6G) wireless communication systems. Adaptive backscatter communication has emerged as a research hotspot in AmBC in recent years. In this paper, we propose a novel adaptive backscatter technique on passive backscatter devices (BDs) in massive IoT scenarios. We first design a low-power adaptive strategy for the AmBC system where the backscatter receiver (BR) assigns a decision threshold to the passive BDs for the local adaptive backscatter mode chosen. Then, we propose the decision threshold design method by solving a joint sum rate maximization problem where the reflection coefficients (RCs) and transmit time allocation (TA) of different backscatter modes are also jointly optimized. Finally, simulations are provided to verify the efficiency of the proposed adaptive backscatter technique in terms of sum rate and outage probability performances. The results show that our proposed adaptive multi-source AmBC system can achieve a 34.8% average sum rate performance improvement compared with traditional AmBC systems under a common setup, and it performs better than other existing adaptive backscatter systems. Moreover, the numeric results confirm the accuracy and tightness of our derivation of outage probabilities.

APA, Harvard, Vancouver, ISO, and other styles

35

Zhan, Yamei, and Zhaopeng Xuan. "Medical Record Encryption Storage System Based on Internet of Things." Wireless Communications and Mobile Computing 2021 (November 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/2109267.

Full text

Abstract:

The Internet of Things takes data as the center, and its core is data storage and management. With the emergence and rapid development of wireless communication technology, with the huge number of terminals in human society, massive data will be generated. Undoubtedly, data storage and management technology will attract much attention. In view of this, this paper proposes a data storage scheme based on the Internet of Things. This paper introduces the Internet of Things technology, designs it from the perspective of the massive data storage system of the Internet of Things, realizes the intelligent processing of data storage, and provides security guarantee for information services. By combing the business process management of doctors, nurses and patients, this paper constructs a medical record encryption management system, makes a comparative analysis before and after the system goes online, and carries out simulation experiments. The simulation results show that (1) the cost of paper is significantly reduced, and the related forms of medical records are more unified and standard, (2) medical record inquiry and reading are more convenient and controllable, and (3) the safety of medical records is well guaranteed. Except that the relevant doctors and nurses of patients can view the relevant medical records, and others have no authority to query and access them. Therefore, the encrypted medical record storage system based on Internet of Things technology can effectively solve the collection, statistics, and integration of patient treatment information, which can be summarized into a unified, shared, and interconnected electronic medical record management system to realize the collection of patient treatment information in the whole process.

APA, Harvard, Vancouver, ISO, and other styles

36

KSHITIJ, NAUTIYAL, and KUMAR RAJEEV. "Functions of Smart Security Using the Internet of Things-A Study." International Journal of Interdisciplinary Research and Innovations 11, no. 4 (2023): 43–50. https://doi.org/10.5281/zenodo.10255306.

Full text

Abstract:

Abstract: The Internet of Things (IoT) describes the network of physical objects— "things"—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These devices range from ordinary household objects to sophisticated industrial tools. With more than 7 billion connected IoT devices today, experts are expecting this number to grow to 22 billion by 2025.  a significant number of Internet-connected devices generate massive amounts of data. The main difficulty in the IoT market is Protecting IoT devices and network data. Privacy, confidentiality, integrity, and dependability problems must be addressed while transferring user data across devices. This research analyses the communications action of IoT devices and mobile apps, security threats to IoT technology, IoT tools, manufacturers, and simulators. This article deals also with the role of the Internet of Things in advancing smart security. Keywords:  Internet of things, Smart Security Analyses.Title: Functions of Smart Security Using the Internet of Things-A StudyAuthor: KSHITIJ NAUTIYAL, RAJEEV KUMARInternational Journal of Interdisciplinary Research and InnovationsISSN 2348-1218 (print), ISSN 2348-1226 (online)Vol. 11, Issue 4, October 2023 - December 2023Page No: 43-50Research Publish JournalsWebsite: www.researchpublish.comPublished Date: 04-December-2023DOI: https://doi.org/10.5281/zenodo.10255306Paper Download Link (Source)https://www.researchpublish.com/papers/functions-of-smart-security-using-the-internet-of-things-a-study

APA, Harvard, Vancouver, ISO, and other styles

37

Fu, Yanxia, Yanli Ren, Guorui Feng, Xinpeng Zhang, and Chuan Qin. "Non-Interactive and Secure Data Aggregation Scheme for Internet of Things." Electronics 10, no. 20 (2021): 2464. http://dx.doi.org/10.3390/electronics10202464.

Full text

Abstract:

The popularity of mobile devices in Internet of Things has brought great convenience to the lives of the people. Massive data generated in the IoT are outsourced and stored on cloud platforms so that data aggregation and analysis can be performed on the massive data. However, these data often contain sensitive information of mobile devices, so effective protection of mobile user privacy is the primary condition for further development of IoT. Most of the current data aggregation schemes require a lot of interactions between users, and thus this paper designs a non-interactive secure multidimensional data aggregation scheme. This scheme adopts an additive secret sharing technique to mask the shared data and send it to two non-colluding servers, and then the servers aggregate the ciphertext respectively. Different from the existing schemes, our proposed scheme achieves non-interaction between users, and the aggregation result is kept confidential to the server and supports mobile users offline. Finally, we perform an experimental evaluation which proves the effectiveness of our scheme.

APA, Harvard, Vancouver, ISO, and other styles

38

Nihad, Marwah, Alaa Hassan, and Nadia Ibrahim. "A Survey on Intermediate Data Management for Big Data and Internet of Things." International Journal of Engineering & Technology 7, no. 4.37 (2018): 86. http://dx.doi.org/10.14419/ijet.v7i4.37.23622.

Full text

Abstract:

The field internet of things and Big Data has become a necessity in our everyday lives due to the broadening of its technology and the exponential increase in devices, services, and applications that drive different types of data. This survey shows the study of Internet of Things (IoT), Big Data, data management, and intermediate data. The survey discusses intermediate data on Big Data and Internet of Things (IoT) and how it is managed. Internet of Things (IoT) is an essential concept of a new technology generation. It is a vision that allows the embedded devices or sensors to be interconnected over the Internet. The future Internet of Things (IoT) will be greatly presented by the massive quantity of heterogeneous networked embedded devices that generate intensively "Big data". Referring to the term intermediate data as the information that is provoked as output data along the process. However, this data is temporary and is erased as soon as you run a model or a sample tool. Also, the existence of intermediate data in both of the Internet of Things (IoT) and Big Data are explained. Here, various aspects of the internet of things, Big Data, intermediate data and data management will be reviewed. Moreover, the schemes for managing this data and its framework are discussed.

APA, Harvard, Vancouver, ISO, and other styles

39

Chen, Yuanyi, Yanyun Tao, Zengwei Zheng, and Dan Chen. "Graph-based service recommendation in Social Internet of Things." International Journal of Distributed Sensor Networks 17, no. 4 (2021): 155014772110090. http://dx.doi.org/10.1177/15501477211009047.

Full text

Abstract:

While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

40

Zikria, Yousaf Bin, Muhammad Khalil Afzal, and Sung Won Kim. "Internet of Multimedia Things (IoMT): Opportunities, Challenges and Solutions." Sensors 20, no. 8 (2020): 2334. http://dx.doi.org/10.3390/s20082334.

Full text

Abstract:

With the immersive growth of the Internet of Things (IoT) and real-time adaptability, quality of life for people is improving. IoT applications are diverse in nature and one crucial aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). It generates a massive volume of data with different characteristics and requirements than the IoT. The real-time deployment scenarios vary from smart traffic monitoring to smart hospitals. Hence, Timely delivery of IoMT data and decision making is critical as it directly involves the safety of human beings. In this paper, we present a brief overview of IoMT and future research directions. Afterward, we provide an overview of the accepted articles in our special issue on the IoMT: Opportunities, Challenges, and Solutions.

APA, Harvard, Vancouver, ISO, and other styles

41

Espinosa, Monica, Manuel Perez, Tatiana Zona, and Xavier Lagrange. "Radio Access Mechanism for Massive Internet of Things Services Over White Spaces." IEEE Access 9 (2021): 120911–23. http://dx.doi.org/10.1109/access.2021.3105131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Zhang, Shuai, Xiaoming Xu, Jianhua Peng, Kaizhi Huang, and Zhigang Li. "Physical layer security in massive internet of things: delay and security analysis." IET Communications 13, no. 1 (2019): 93–98. http://dx.doi.org/10.1049/iet-com.2018.5570.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Zhai, Daosen, Ruonan Zhang, Lin Cai, and F. Richard Yu. "Delay Minimization for Massive Internet of Things With Non-Orthogonal Multiple Access." IEEE Journal of Selected Topics in Signal Processing 13, no. 3 (2019): 553–66. http://dx.doi.org/10.1109/jstsp.2019.2898643.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Okegbile, Samuel D., and Olabisi I. Ogunranti. "Users emulation attack management in the massive internet of things enabled environment." ICT Express 6, no. 4 (2020): 353–56. http://dx.doi.org/10.1016/j.icte.2020.06.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Caballero, Víctor, Sergi Valbuena, David Vernet, and Agustín Zaballos. "Ontology-Defined Middleware for Internet of Things Architectures." Sensors 19, no. 5 (2019): 1163. http://dx.doi.org/10.3390/s19051163.

Full text

Abstract:

The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.

APA, Harvard, Vancouver, ISO, and other styles

46

Iqbal, Adeel, Ali Nauman, Yazdan Ahmad Qadri, and Sung Won Kim. "Optimizing Spectral Utilization in Healthcare Internet of Things." Sensors 25, no. 3 (2025): 615. https://doi.org/10.3390/s25030615.

Full text

Abstract:

The mainstream adoption of Internet of Things (IoT) devices for health and lifestyle tracking has revolutionized health monitoring systems. Sixth-generation (6G) cellular networks enable IoT healthcare services to reduce the pressures on already resource-constrained facilities, leveraging enhanced ultra-reliable low-latency communication (eURLLC) to make sure critical health data are transmitted with minimal delay. Any delay or information loss can result in serious consequences, making spectrum availability a crucial bottleneck. This study systematically identifies challenges in optimizing spectrum utilization in healthcare IoT (H-IoT) networks, focusing on issues such as dynamic spectrum allocation, interference management, and prioritization of critical medical devices. To address these challenges, the paper highlights emerging solutions, including artificial intelligence-based spectrum management, edge computing integration, and advanced network architectures such as massive multiple-input multiple-output (mMIMO) and terahertz (THz) communication. We identify gaps in the existing methodologies and provide potential research directions to enhance the efficiency and reliability of eURLLC in healthcare environments. These findings offer a roadmap for future advancements in H-IoT systems and form the basis of our recommendations, emphasizing the importance of tailored solutions for spectrum management in the 6G era.

APA, Harvard, Vancouver, ISO, and other styles

47

Mali, Pritibala, and Prof Pankaj Raghuwanshi. "Machine Learning for Intrusion Detection for Massive IoT Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28455.

Full text

Abstract:

Wide area networks such as fog and internet of things often encounter network level security. There would exist a continued trade-off between the error rate (authentication metric), system overhead, computational complexity and latency of the system. Hence an extremely meticulous system design with appropriate choice of stochastic parameters and authentication scheme should be adopted. In this proposed work, an acceleration learning based LSTM network has been proposed to detect attacks in IoT networks. It can be observed from the obtained results that the proposed system attains better performance compared to previously existing system. The performance enhancement can be attributed to additional features computed and the LSTM with acceleration used to train and further detect errors. Keywords: Internet of Things (IoT), Network Level Security, Neural Networks, Deep Learning, Accuracy, Gateway Utility.

APA, Harvard, Vancouver, ISO, and other styles

48

Shamshekhar, S. Patil, and Biradar Arun. "Novel authentication framework for securing communication in internet-of-things." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 1092–100. https://doi.org/10.11591/ijece.v10i1.pp1092-1100.

Full text

Abstract:

Internet-of-Things (IoT) offers a big boon towards a massive network of connected devices and is considered to offer coverage to an exponential number of the smart appliance in the very near future. Owing to the nascent stage of evolution of IoT, it is shrouded by security loopholes because of various reasons. Review of existing research-based solution highlights the usage of conventional cryptographic-based solution over the traditional mechanism of data forwarding process between IoT nodes and gateway. The proposed system presents a novel solution to this problem by a model that is capable of performing a highly secured and cost-effective authentication process. The proposed system introduces Authentication Using Signature (AUS) as well as Security with Complexity Reduction (SCR) for the purpose to resist participation of any form of unknown threats. The outcome of the model shows better security strength with faster response time and energy saving of the IoT nodes.

APA, Harvard, Vancouver, ISO, and other styles

49

Dezfouli, Behnam, and Yuhong Liu. "Editorial: Special Issue “Edge and Fog Computing for Internet of Things Systems”." Sensors 22, no. 12 (2022): 4387. http://dx.doi.org/10.3390/s22124387.

Full text

Abstract:

Employing edge and fog computing for building IoT systems is essential, especially because of the massive number of data generated by sensing devices, the delay requirements of IoT applications, the high burden of data processing on cloud platforms, and the need to take immediate actions against security threats.

APA, Harvard, Vancouver, ISO, and other styles

50

Чеверева, С. А. "The Internet of Things as a source of big data." Экономика и предпринимательство, no. 2(139) (May 15, 2022): 840–43. http://dx.doi.org/10.34925/eip.2022.139.2.160.

Full text

Abstract:

В данной статье рассматривается связь Интернета вещей и Больших Данных. В частности, данное исследование описывает как элементы Интернета Вещей генерируют массивные данные и возможны ли чудеса в реальном мире при комбинации этих двух пространственных структур. За последние десять лет появилось множество «умных» приборов в рамках концепции Internet of Things (IoT -Иинтернет вещей). IoT - это общая сеть физических объектов, которые могут изменять пара метры и внешней среды, и свои. Они могут собирать информацию, а также могут передавать ее на иные устройства. Информационное пространство расширяется до мира материальных объектов с помощью Интернета вещей. This article discusses the connection between the Internet of Things and Big Data. In particular, this study describes how elements of the Internet of Things generate massive data and whether miracles are possible in the real world with a combination of these two spatial structures. Over the past ten years, many "smart" devices have appeared within the framework of the Internet of Things (IoT - Internet of Things) concept. IoT is a common network of physical objects that can change the parameters of both the external environment and their own. They can collect information, and they can also transmit it to other devices. The information space is expanding to the world of material objects with the help of the Internet of Things.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Massive Internet-of-things'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles