Relevant bibliographies by topics / Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing

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Academic literature on the topic 'Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing"

1

Zhou, Chengcheng, Qian Liu, and Ruolei Zeng. "Novel Defense Schemes for Artificial Intelligence Deployed in Edge Computing Environment." Wireless Communications and Mobile Computing 2020 (August 3, 2020): 1–20. http://dx.doi.org/10.1155/2020/8832697.

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The last few years have seen the great potential of artificial intelligence (AI) technology to efficiently and effectively deal with an incredible deluge of data generated by the Internet of Things (IoT) devices. If all the massive data is transferred to the cloud for intelligent processing, it not only brings considerable challenges to the network bandwidth but also cannot meet the needs of AI applications that require fast and real-time response. Therefore, to achieve this requirement, mobile or multiaccess edge computing (MEC) is receiving a substantial amount of interest, and its importance is gradually becoming more prominent. However, with the emerging of edge intelligence, AI also suffers from several tremendous security threats in AI model training, AI model inference, and private data. This paper provides three novel defense strategies to tackle malicious attacks in three aspects. First of all, we introduce a cloud-edge collaborative antiattack scheme to realize a reliable incremental updating of AI by ensuring the data security generated in the training phase. Furthermore, we propose an edge-enhanced defense strategy based on adaptive traceability and punishment mechanism to effectively and radically solve the security problem in the inference stage of the AI model. Finally, we establish a system model based on chaotic encryption with the three-layer architecture of MEC to effectively guarantee the security and privacy of the data during the construction of AI models. The experimental results of these three countermeasures verify the correctness of the conclusion and the feasibility of the methods.
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Yadav, Rahul, and Weizhe Zhang. "MeReg: Managing Energy-SLA Tradeoff for Green Mobile Cloud Computing." Wireless Communications and Mobile Computing 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6741972.

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Mobile cloud computing (MCC) provides various cloud computing services to mobile users. The rapid growth of MCC users requires large-scale MCC data centers to provide them with data processing and storage services. The growth of these data centers directly impacts electrical energy consumption, which affects businesses as well as the environment through carbon dioxide (CO2) emissions. Moreover, large amount of energy is wasted to maintain the servers running during low workload. To reduce the energy consumption of mobile cloud data centers, energy-aware host overload detection algorithm and virtual machines (VMs) selection algorithms for VM consolidation are required during detected host underload and overload. After allocating resources to all VMs, underloaded hosts are required to assume energy-saving mode in order to minimize power consumption. To address this issue, we proposed an adaptive heuristics energy-aware algorithm, which creates an upper CPU utilization threshold using recent CPU utilization history to detect overloaded hosts and dynamic VM selection algorithms to consolidate the VMs from overloaded or underloaded host. The goal is to minimize total energy consumption and maximize Quality of Service, including the reduction of service level agreement (SLA) violations. CloudSim simulator is used to validate the algorithm and simulations are conducted on real workload traces in 10 different days, as provided by PlanetLab.
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Zhang, Xuanyu, Yining Gao, Guangyi Xiao, Bo Feng, and Wenshu Chen. "A Real-Time Garbage Truck Supervision and Data Statistics Method Based on Object Detection." Wireless Communications and Mobile Computing 2020 (October 10, 2020): 1–9. http://dx.doi.org/10.1155/2020/8827310.

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Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.
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B. A. Alaasam, Ameer. "The Challenges and Prerequisites of Data Stream Processing in Fog Environment for Digital Twin in Smart Industry." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 15 (August 11, 2021): 126. http://dx.doi.org/10.3991/ijim.v15i15.24181.

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<p class="0abstract">Smart industry systems are based on integrating historical and current data from sensors with physical and digital systems to control product states. For example, Digital Twin (DT) system predicts the future state of physical assets using live simulation and controls the current state through real-time feedback. These systems rely on the ability to process big data stream to provide real-time responses. For, example it is estimated that one autonomous vehicle (AV) could produce 30 terabytes of data per day. AV will not be on the road before using an effective way to managing its big data and solve latency challenges. Cloud computing failed in the latency challenge, while Fog computing addresses it by moving parts of the computations from the Cloud to the edge of the network near the asset to reduce the latency. This work studies the challenges in data stream processing for DT in a fog environment. The challenges include fog architecture, the necessity of loosely-coupling design, the used virtual machine versus container, the stateful versus stateless operations, the stream processing tools, and live migration between fog nodes. The work also proposes a fog computing architecture and provides a vision of the prerequisites to meet the challenges.</p>
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Jayasinghe, Upul, Gyu Myoung Lee, Áine MacDermott, and Woo Seop Rhee. "TrustChain: A Privacy Preserving Blockchain with Edge Computing." Wireless Communications and Mobile Computing 2019 (July 8, 2019): 1–17. http://dx.doi.org/10.1155/2019/2014697.

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Recent advancements in the Internet of Things (IoT) has enabled the collection, processing, and analysis of various forms of data including the personal data from billions of objects to generate valuable knowledge, making more innovative services for its stakeholders. Yet, this paradigm continuously suffers from numerous security and privacy concerns mainly due to its massive scale, distributed nature, and scarcity of resources towards the edge of IoT networks. Interestingly, blockchain based techniques offer strong countermeasures to protect data from tampering while supporting the distributed nature of the IoT. However, the enormous amount of energy consumption required to verify each block of data make it difficult to use with resource-constrained IoT devices and with real-time IoT applications. Nevertheless, it can expose the privacy of the stakeholders due to its public ledger system even though it secures data from alterations. Edge computing approaches suggest a potential alternative to centralized processing in order to populate real-time applications at the edge and to reduce privacy concerns associated with cloud computing. Hence, this paper suggests the novel privacy preserving blockchain called TrustChain which combines the power of blockchains with trust concepts to eliminate issues associated with traditional blockchain architectures. This work investigates how TrustChain can be deployed in the edge computing environment with different levels of absorptions to eliminate delays and privacy concerns associated with centralized processing and to preserve the resources in IoT networks.
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Fu, Chao, Qing Lv, and Reza G. Badrnejad. "Fog computing in health management processing systems." Kybernetes 49, no. 12 (January 4, 2020): 2893–917. http://dx.doi.org/10.1108/k-09-2019-0621.

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Purpose Fog computing (FC) is a new field of research and has emerged as a complement to the cloud, which can mitigate the problems inherent to the cloud computing (CC) and internet of things (IoT) model such as unreliable latency, bandwidth constraints, security and mobility. Because there is no comprehensive study on the FC in health management processing systems techniques, this paper aims at surveying and analyzing the existing techniques systematically as well as offering some suggestions for upcoming works. Design/methodology/approach The paper complies with the methodological requirements of systematic literature reviews (SLR). The present paper investigates the newest systems and studies their practical techniques in detail. The applications of FC in health management systems have been categorized into three major groups, including review articles, data analysis, frameworks and models mechanisms. Findings The results have indicated that despite the popularity of FC as having real-time processing, low latency, dynamic configuration, scalability, low reaction time (less than a second), high bandwidth, battery life and network traffic, a few issues remain unanswered, such as security. The most recent research has focused on improvements in remote monitoring of the patients, such as less latency and rapid response. Also, the results have shown the application of qualitative methodology and case study in the use of FC in health management systems. While FC studies are growing in the clinical field, CC studies are decreasing. Research limitations/implications This study aims to be comprehensive, but there are some limitations. This research has only surveyed the articles that are mined, according to a keyword exploration of FC health, FC health care, FC health big data and FC health management system. Fog-based applications in the health management system may not be published with determined keywords. Moreover, the publications written in non-English languages have been ignored. Some important research studies may be printed in a language other than English. Practical implications The results of this survey will be valuable for academicians, and these can provide visions into future research areas in this domain. This survey helps the hospitals and related industries to identify FC needs. Moreover, the disadvantages and advantages of the above systems have been studied, and their key issues have been emphasized to develop a more effective FC in health management processing mechanisms over IoT in the future. Originality/value Previous literature review studies in the field of SLR have used a simple literature review to find the tasks and challenges in the field. In this study, for the first time, the FC in health management processing systems is applied in a systematic review focused on the mediating role of the IoT and thereby provides a novel contribution. An SLR is conducted to find more specific answers to the proposed research questions. SLR helps to reduce implicit researcher bias. Through the adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, SLR effectively forces researchers to search for studies beyond their subject areas and networks.
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Ma, Xingmin, Shenggang Xu, Fengping An, and Fuhong Lin. "A Novel Real-Time Image Restoration Algorithm in Edge Computing." Wireless Communications and Mobile Computing 2018 (August 9, 2018): 1–13. http://dx.doi.org/10.1155/2018/3610482.

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Owning to the high processing complexity, the image restoration can only be processed offline and hardly be applied in the real-time production life. The development of edge computing provides a new solution for real-time image restoration. It can upload the original image to the edge node to process in real time and then return results to users immediately. However, the processing capacity of the edge node is still limited which requires a lightweight image restoration algorithm. A novel real-time image restoration algorithm is proposed in edge computing. Firstly, 10 classical functions are used to determine the population size and maximum iteration times of traction fruit fly optimization algorithm (TFOA). Secondly, TFOA is used to optimize the optimal parameters of least squares support vector regression (LSSVR) kernel function, and the error function of image restoration is taken as an adaptive function of TFOA. Thirdly, the LLSVR algorithm is used to restore the image. During the image restoration process, the training process is to establish a mapping relationship between the degraded image and the adjacent pixels of the original image. The relationship is established; the degraded image can be restored by using the mapping relationship. Through the comparison and analysis of experiments, the proposed method can meet the requirements of real-time image restoration, and the proposed algorithm can speed up the image restoration and improve the image quality.
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Li, Xianwei, and Baoliu Ye. "Latency-Aware Computation Offloading for 5G Networks in Edge Computing." Security and Communication Networks 2021 (September 22, 2021): 1–15. http://dx.doi.org/10.1155/2021/8800234.

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With the development of Internet of Things, massive computation-intensive tasks are generated by mobile devices whose limited computing and storage capacity lead to poor quality of services. Edge computing, as an effective computing paradigm, was proposed for efficient and real-time data processing by providing computing resources at the edge of the network. The deployment of 5G promises to speed up data transmission but also further increases the tasks to be offloaded. However, how to transfer the data or tasks to the edge servers in 5G for processing with high response efficiency remains a challenge. In this paper, a latency-aware computation offloading method in 5G networks is proposed. Firstly, the latency and energy consumption models of edge computation offloading in 5G are defined. Then the fine-grained computation offloading method is employed to reduce the overall completion time of the tasks. The approach is further extended to solve the multiuser computation offloading problem. To verify the effectiveness of the proposed method, extensive simulation experiments are conducted. The results show that the proposed offloading method can effectively reduce the execution latency of the tasks.
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Wang, Siye, Ziwen Cao, Yanfang Zhang, Weiqing Huang, and Jianguo Jiang. "A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data." Wireless Communications and Mobile Computing 2020 (February 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6937912.

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The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.
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Huo, Yan, Chengtao Yong, and Yanfei Lu. "Re-ADP: Real-Time Data Aggregation with Adaptive ω-Event Differential Privacy for Fog Computing." Wireless Communications and Mobile Computing 2018 (July 8, 2018): 1–13. http://dx.doi.org/10.1155/2018/6285719.

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In the Internet of Things (IoT), aggregation and release of real-time data can often be used for mining more useful information so as to make humans lives more convenient and efficient. However, privacy disclosure is one of the most concerning issues because sensitive information usually comes with users in aggregated data. Thus, various data encryption technologies have emerged to achieve privacy preserving. These technologies may not only introduce complicated computing and high communication overhead but also do not work on the protection of endless data streams. Considering these challenges, we propose a real-time stream data aggregation framework with adaptive ω-event differential privacy (Re-ADP). Based on adaptive ω-event differential privacy, the framework can protect any data collected by sensors over any dynamic ω time stamp successively over infinite stream. It is designed for the fog computing architecture that dramatically extends the cloud computing to the edge of networks. In our proposed framework, fog servers will only send aggregated secure data to cloud servers, which can relieve the computing overhead of cloud servers, improve communication efficiency, and protect data privacy. Finally, experimental results demonstrate that our framework outperforms the existing methods and improves data availability with stronger privacy preserving.
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Dissertations / Theses on the topic "Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing"

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Karunanidhi, Karthikeyan. "ARROS; distributed adaptive real-time network intrusion response." Ohio : Ohio University, 2006. http://www.ohiolink.edu/etd/view.cgi?ohiou1141074467.

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Barnes, Richard Neil. "Global synchronization of asynchronous computing systems." Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-10262001-094922.

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West, Richard. "Adaptive real-time management of communication and computation resources." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/9237.

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Drougas, Ioannis. "Rate allocation in distributed stream processing systems." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077971&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268240766&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2008.
Includes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 93-98). Also issued in print.
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Chen, Liang. "A grid-based middleware for processing distributed data streams." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1157990530.

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Olander, Peter Andrew. "Built-in tests for a real-time embedded system." Thesis, 1991. http://hdl.handle.net/10413/5680.

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Beneath the facade of the applications code of a well-designed real-time embedded system lies intrinsic firmware that facilitates a fast and effective means of detecting and diagnosing inevitable hardware failures. These failures can encumber the availability of a system, and, consequently, an identification of the source of the malfunction is needed. It is shown that the number of possible origins of all manner of failures is immense. As a result, fault models are contrived to encompass prevalent hardware faults. Furthermore, the complexity is reduced by determining syndromes for particular circuitry and applying test vectors at a functional block level. Testing phases and philosophies together with standardisation policies are defined to ensure the compliance of system designers to the underlying principles of evaluating system integrity. The three testing phases of power-on self tests at system start up, on-line health monitoring and off-line diagnostics are designed to ensure that the inherent test firmware remains inconspicuous during normal applications. The prominence of the code is, however, apparent on the detection or diagnosis of a hardware failure. The authenticity of the theoretical models, standardisation policies and built-in test philosophies are illustrated by means of their application to an intricate real-time system. The architecture and the software design implementing the idealogies are described extensively. Standardisation policies, enhanced by the proposition of generic tests for common core components, are advocated at all hierarchical levels. The presentation of the integration of the hardware and software are aimed at portraying the moderately complex nature of the task of generating a set of built-in tests for a real-time embedded system. In spite of generic policies, the intricacies of the architecture are found to have a direct influence on software design decisions. It is thus concluded that the diagnostic objectives of the user requirements specification be lucidly expressed by both operational and maintenance personnel for all testing phases. Disparity may exist between the system designer and the end user in the understanding of the requirements specification defining the objectives of the diagnosis. It is thus essential for complete collaboration between the two parties throughout the development life cycle, but especially during the preliminary design phase. Thereafter, the designer would be able to decide on the sophistication of the system testing capabilities.
Thesis (M.Sc.)-University of Natal, Durban, 1991.
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Books on the topic "Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing"

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Real-time systems: Design principles for distributed embedded applications. Boston: Kluwer Academic Publishers, 1997.

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IEEE Computer Society. TC on Distributed Processing., ed. Object-oriented real-time dependable systems: Proceedings : the Eighth IEEE International Workshop on Object-Oriented Real-Time Dependable Systems : January 15 to 17, 2003, Guadalajara, Mexico. Los Alamitos, Calif: IEEE Computer Society Press, 2003.

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service), SpringerLink (Online, ed. Real-Time Systems: Design Principles for Distributed Embedded Applications. Boston, MA: Springer Science+Business Media, LLC, 2011.

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service), SpringerLink (Online, ed. Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications. Boston, MA: Springer US, 2011.

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IEEE Computer Society. TC on Distributed Processing., ed. Sixth International Workshop on Object-Oriented Real-Time Dependable Systems: Proceedings : 8-10 January, 2001, Rome, Italy. Los Alamitos, Calif: IEEE Computer Society Press, 2001.

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Workshop on Object-Oriented Real-Time Dependable Systems (5th 1999 Monterey, Calif.). Fifth International Workshop on Object-Oriented Real-Time Dependable Systems: Proceedings, November 18-20, 1999, Monterey, California, USA. Los Alamitos, Calif: IEEE Computer Society Press, 2000.

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Workshop on Object-Oriented Real-Time Dependable Systems (3rd 1997 Newport Beach, Calif.). Third International Workshop on Object-oriented Real-time Dependable Systems: Proceedings, February 5-7, 1997, Newport Beach, California, U.S.A. Los Alamitos, Calif: IEEE Computer Society Press, 1997.

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Workshop on Object-Oriented Real-Time Dependable Systems (4th 1999 Santa Barbara, Calif.). Fourth International Workshop on Object-Oriented Real-Time Dependable Systems: Proceedings, January 27-29, 1999, Santa Barbara, California, USA. Los Alamitos, Calif: IEEE Computer Society, 1999.

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Ariz.) IEEE International Workshop on Object-Oriented Real-Time Dependable Systems (10th 2005 Sedona. Proceedings: 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems, WORDS 2005, 2-4 February 2005, Sedona, Arizona. Los Alamitos, Calif: IEEE Computer Society, 2005.

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IEEE Computer Society. TC on Distributed Processing., ed. Proceedings of the Seventh IEEE International Workshop on Object-Oriented Real-Time Dependable Systems (WORDS 2002): 7-9 January 2002, San Diego, California. Los Alamitos, Calif: IEEE Computer Society Press, 2002.

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Book chapters on the topic "Computer networks Real-time data processing. Adaptive computing systems. Electronic data processing"

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Acharya, Subrata. "PITWALL." In Situational Awareness in Computer Network Defense, 320–43. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0104-8.ch018.

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The continuous growth in the Internet’s size, the amount of data traffic, and the complexity of processing this traffic give rise to new challenges in building high performance network devices. Such an exponential growth, coupled with the increasing sophistication of attacks, is placing stringent demands on the performance of network Information Systems. These challenges require new designs, architecture, and algorithms for raising situational awareness, and hence, providing performance improvements on current network devices and cyber systems. In this research, the author focuses on the design of architecture and algorithms for optimization of network defense systems, specifically firewalls, to aid not only adaptive and real-time packet filtering but also fast content based routing (differentiated services) for today’s data-driven networks.
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Garg, Pradeep Kumar. "The Internet of Things-Based Technologies." In Advances in Wireless Technologies and Telecommunication, 37–65. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4685-7.ch003.

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The internet of things (IoT) is the network of physical objects—devices, vehicles, buildings, and other objects—embedded with software, electronic devices, sensors, and network connectivity that enable these objects to collect and share information or data. Its applications include smart homes, healthcare, industries, transportation systems, logistics, and energy. Building an IoT real-time-based application involves the proper selection of combination of sensors, technology, networks, and communication modules, supported with the concepts of data processing, remote sensing, cloud computing, etc. This chapter highlights advantages and disadvantages IoT and various techniques, such as computer vision, remote sensing, artificial intelligence, cloud computing, big data, ubiquitous computing, which are widely used in various applications. Many new IoT-based applications will evolve, as new devices, sensors, chips, and computational techniques are developed.
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