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1
Pilcher, Jeremy. "Network Art Unbound?" Leonardo 45, no. 5 (October 2012): 470–71. http://dx.doi.org/10.1162/leon_a_00447.
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Internet art that visualizes data has art historical precedents that invite its critical effect to be engaged with as rhetoric and not only an accurate representation of a specific state of affairs. Art that opens an engagement with a representation of data as an iteration of an underlying system invites an awareness of the contingency of that system. The nature of the internet makes it an effective site for this to occur. Internet art may allow an awareness of how a system might become more ethical in the future.
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Wang, Bo, and Jian Yun. "State of Art Techniques in En-Route Caching." Advanced Materials Research 121-122 (June 2010): 1018–22. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.1018.
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The World Wide Web has been considered as a large distributed information system based development and use of computer technology that both provide access to shared data objects. En-Cache techniques are important for reducing Internet access latency, network traffic, and server load in different networks. In this paper, we survey the state-of-art techniques used in En-route Caching and discuss the recent development.
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EICHMANN, DAVID. "ADVANCES IN NETWORK INFORMATION DISCOVERY AND RETRIEVAL." International Journal of Software Engineering and Knowledge Engineering 05, no. 01 (March 1995): 143–60. http://dx.doi.org/10.1142/s0218194095000083.
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Access to information using the Internet has undergone dramatic change and expansion recently. The unrivaled success of the World Wide Web has altered the Internet from something approachable only by the initiated to something of a media craze — the information superhighway made manifest in the personal "home page". This paper surveys the beginnings of network information discovery and retrieval, how the Web has created a surprising level of integration of these systems, and where the current state of the art lies in creating globally accessible information spaces and supporting access to those information spaces.
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Broeckmann, Andreas. "Reseau/Resonance: Connective Processes and Artistic Practice." Leonardo 37, no. 4 (August 2004): 280–86. http://dx.doi.org/10.1162/0024094041724544.
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Most Internet art projects use the Net solely as a telematic and telecommunicative transmission medium that connects computers and servers and through which artists, performers and users exchange data, communicate and collaboratively create files and events. At the same time, however, some artists are exploring the electronic networks as specific socio-technical structures with their respective forms of social and machinic agency, in which people and machines interact in ways unique to this environment. The author discusses recent projects that use the Net as a performative space of social and aesthetic resonance in which notions of subjectivity, action and production are being articulated and reassessed. This text discusses the notion of “resonance” in order to think through these approaches to network-based art practices.
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CHERIYAN, JO, and G. P. SAJEEV. "m-PageRank: A NOVEL CENTRALITY MEASURE FOR MULTILAYER NETWORKS." Advances in Complex Systems 23, no. 05 (August 2020): 2050012. http://dx.doi.org/10.1142/s0219525920500125.
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A complex network is an explicit model for a real-world system such as technological networks, social networks, business networks, and biological networks. The social network is an Internet-based media for socially relevant activities like stay connected with families and friends, colleagues, and customers, for socializing, business, or both. The key nodes, usually called central nodes, capable of measuring the performance of various social network applications. Identifying influencing nodes is primary research for any network analysis research. Degree centrality, a locally computed metric, is simple to compute but not persuasive. The global metrics like betweenness centrality and PageRank are only useful for the systems with a simple structure, but incur a high computational cost with the addition of layers. This paper proposes a novel metric m-PageRank for ranking nodes in a multi-layer complex network. The m-PageRank is an advancement of PageRank. It integrates the existence of the rank of each layer from where the connection connects. The proposed metric was validated through simulations performed over various multilayer networks. The result shows that m-PageRank computes the rank of each node accurately. We observe that the comparison with state-of-the-art metrics demonstrated the suitability of the proposed metric.
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De la Torre, Gabriel G., Sara Gonzalez-Torre, Carlos Muñoz, and Manuel A. Garcia. "Wireless Computer-Supported Cooperative Work: A Pilot Experiment on Art and Brain–Computer Interfaces." Brain Sciences 9, no. 4 (April 25, 2019): 94. http://dx.doi.org/10.3390/brainsci9040094.
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The present case study looked into the feasibility of using brain–computer interface (BCI) technology combined with computer-supported cooperative work (CSCW) in a wireless network. We had two objectives; first, to test the wireless BCI-based configuration and the practical use of this idea we assessed workload perception in participants located several kilometers apart taking part in the same drawing task. Second, we studied the cortical activation patterns of participants performing the drawing task with and without the BCI technology. Results showed higher mental workload perception and broader cortical activation (frontal-temporal-occipital) under BCI experimental conditions. This idea shows a possible application of BCI research in the social field, where two or more users could engage in a computer networking task using BCI technology over the internet. New research avenues for CSCW are discussed and possibilities for future research are given.
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Li, Zhiyuan, Panpan Wu, and Yue Song. "A Fast Reroute Algorithm for Infotainment Service in Internet of Vehicles." International Journal of Distributed Systems and Technologies 7, no. 3 (July 2016): 63–77. http://dx.doi.org/10.4018/ijdst.2016070104.
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Failure recovery in Internet of Vehicles (IoVs) is critical to high quality service provisioning. The main challenge is how to achieve fast rerouting without introducing high complexity and resource usage due to the dynamic topology and the constraints on bandwidth. In this paper, we propose a traffic prediction-based fast reroute algorithm for use among the vehicles in IoVs. The proposed algorithm uses the Wavelet Neural Network (WNN) model to predict a vehicle's network traffic. When the predicted value is greater than the predefined network traffic threshold, both Adaptive Retransmission Trigger (ART) that contributes to switch to a better alternate path in advance and trigger efficient retransmission behaviors are enabled successively. Performance comparison of our proposed algorithm with Ant-based Delay-Sensitive Vehicular Routing (AntVehiNet) shows that WNNPFR can: (a) maximize the service data delivery rate by load balancing, (b) provide high quality of service delivery for multimedia streams by switching to a better path towards a target node in advance, (c) reduce useless data retransmissions when various network failures occur, and (d) maintain lower routing overhead.
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Sanagavarapu, Krishna Chaitanya. "A REVIEW ON CLOUD COMPUTING SERVICES AND CONVERGENCE AMONG CLOUD COMPUTING AND BIG DATA." International Journal of Engineering Technologies and Management Research 6, no. 9 (April 2, 2020): 84–91. http://dx.doi.org/10.29121/ijetmr.v6.i9.2019.454.
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Cloud Computing enables a practical, as needed and also scalable network access to a common swimming pool of configurable computing resources. This paper mainly focuses on a usual technique to integrate the Internet of Things (IoT) as well as Cloud Computer under the name of Cloud Things style. We evaluate the state-of-the-art for integrating Cloud Computer as well as the Internet of Things. The objective is to give a faster as well as easier platform for the customer to get information from an array of sensor nodes that has actually been set-up in a farming system.
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Tan, Junyang, Dan Xia, Shiyun Dong, Honghao Zhu, and Binshi Xu. "Research On Pre-Training Method and Generalization Ability of Big Data Recognition Model of the Internet of Things." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 5 (July 20, 2021): 1–15. http://dx.doi.org/10.1145/3433539.
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The Internet of Things and big data are currently hot concepts and research fields. The mining, classification, and recognition of big data in the Internet of Things system are the key links that are widely of concern at present. The artificial neural network is beneficial for multi-dimensional data classification and recognition because of its strong feature extraction and self-learning ability. Pre-training is an effective method to address the gradient diffusion problem in deep neural networks and could result in better generalization. This article focuses on the performance of supervised pre-training that uses labelled data. In particular, this pre-training procedure is a simulation that shows the changes in judgment patterns as they progress from primary to mature within the human brain. In this article, the state-of-the-art of neural network pre-training is reviewed. Then, the principles of the auto-encoder and supervised pre-training are introduced in detail. Furthermore, an extended structure of supervised pre-training is proposed. A set of experiments are carried out to compare the performances of different pre-training methods. These experiments include a comparison between the original and pre-trained networks as well as a comparison between the networks with two types of sub-network structures. In addition, a homemade database is established to analyze the influence of pre-training on the generalization ability of neural networks. Finally, an ordinary convolutional neural network is used to verify the applicability of supervised pre-training.
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Ruan, Jinjia, and Dongliang Xie. "Networked VR: State of the Art, Solutions, and Challenges." Electronics 10, no. 2 (January 13, 2021): 166. http://dx.doi.org/10.3390/electronics10020166.
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The networking of virtual reality applications will play an important role in the emerging global Internet of Things (IoT) framework and it is expected to provide the foundation of the expected 5G tactile Internet ecosystem. However, considerable challenges are ahead in terms of technological constraints and infrastructure costs. The raw data rate (5 Gbps–60 Gbps) required achieving an online immersive experience that is indistinguishable from real life vastly exceeds the capabilities of future broadband networks. Therefore, simply providing high bandwidth is insufficient in compensating for this difference, because the demands for scale and supply vary widely. This requires exploring holistic solutions that exceed the traditional network domain, and integrating virtual reality (VR) data capture, encoding, network, and user navigation. Emerging services are extremely inefficient in terms of mass use and data management, which significantly reduces the user experience, due to their heuristic design choices. Other key aspects must be considered, such as wireless operation, ultra-low latency, client/network access, system deployment, edge computing/cache, and end-to-end reliability. A vast number of high-quality works have been published in this area and they will be highlighted in this survey. In addition to a thorough summary of recent progress, we also present an outlook of future developments in the quality of immersive experience networks and unified data set measurement in VR video transmission, focusing on the expansion of VR applications, security issues, and business issues, which have not yet been addressed, and the technical challenges that have not yet been completely solved. We hope that this paper will help researchers and developers to gain a better understanding of the state of research and development in VR.
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Liu, Wenli. "Research on the Application of Multimedia Elements in Visual Communication Art under the Internet Background." Mobile Information Systems 2021 (April 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/5525648.
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With the rapid development of Internet technology, the dissemination of information is undergoing rapid changes. The Internet has become an indispensable part of people’s study, work, and life. In the context of the Internet, this paper analyzes the application of multimedia elements in visual communication art design, elaborates the basic concepts, visual features, and design principles of the visual communication art design, and analyzes the multifaceted aspects of multimedia elements. This paper discusses the basic methods and general rules of the application of multimedia elements in visual communication art design under the background of the Internet, as well as the research on the emotional factors and interactions of multimedia elements on people. Brain-like computing is the meaning of imitation brain. First is the structure, then the function, then from the structure design and analysis of the brain, and finally use the brain's thinking ability to solve problems. It summarizes the text, graphic images, and colors in the Internet background. The innovative application of multimedia elements such as page layout and animation in visual communication art design explains the influence of multimedia elements on the overall visual effect in visual communication art design. It explores how visual information can be conveyed more reasonably and effectively. In the context of the Internet, the application of multimedia elements has brought a new possibility for developing visual communication art design, providing a new platform for the traditional visual communication art design.
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Begum, Seema, Yao Nianmin, Syed Bilal Hussain Shah, Asrin Abdollahi, Inam Ullah Khan, and Liqaa Nawaf. "Source Routing for Distributed Big Data-Based Cognitive Internet of Things (CIoT)." Wireless Communications and Mobile Computing 2021 (July 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/5129396.
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Dynamic opportunistic channel access with software-defined radio at a network layer in distributed cognitive IoT introduces a concurrent channel selection along with end-to-end route selection for application data transmission. State-of-the-art cognitive IoT big data-based routing protocols are not explored in terms of how the spectrum management is being coordinated with the network layer for concurrent channel route selection during end-to-end channel route discovery for data transmission of IoT and big data applications. In this paper, a reactive big data-based “cognitive dynamic source routing protocol” is proposed for cognitive-based IoT networks to concurrently select the channel route at the network layer from source to destination. Experimental results show that the proposed protocol cognitive DSR with concurrent channel route selection criteria is outperformed. This will happen when it is compared with the existing distributed cognitive DSR with independent channel route application data transmission.
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Haq, Inam Ul, Qaisar Javaid, Zahid Ullah, Zafar Zaheer, Mohsin Raza, Muhammad Khalid, Ghufran Ahmed, and Saleem Khan. "E2-MACH: Energy efficient multi-attribute based clustering scheme for energy harvesting wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 10 (October 2020): 155014772096804. http://dx.doi.org/10.1177/1550147720968047.
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Internet of things have emerged enough due to its applications in a wide range of fields such as governance, industry, healthcare, and smart environments (home, smart, cities, and so on). Internet of things–based networks connect smart devices ubiquitously. In such scenario, the role of wireless sensor networks becomes vital in order to enhance the ubiquity of the Internet of things devices with lower cost and easy deployment. The sensor nodes are limited in terms of energy storage, processing, and data storage capabilities, while their radio frequencies are very sensitive to noise and interference. These factors consequently threaten the energy consumption, lifetime, and throughput of network. One way to cope with energy consumption issue is energy harvesting techniques used in wireless sensor network–based Internet of things. However, some recent studies addressed the problems of clustering and routing in energy harvesting wireless sensor networks which either concentrate on energy efficiency or quality of service. There is a need of an adequate approach that can perform efficiently in terms of energy utilization as well as to ensure the quality of service. In this article, a novel protocol named energy-efficient multi-attribute-based clustering scheme (E2-MACH) is proposed which addresses the energy efficiency and communication reliability. It uses selection criteria of reliable cluster head based on a weighted function defined by multiple attributes such as link statistics, neighborhood density, current residual energy, and the rate of energy harvesting of nodes. The consideration of such parameters in cluster head selection helps to preserve the node’s energy and reduce its consumption by sending data over links possessing better signal-to-noise ratio and hence ensure minimum packet loss. The minimized packet loss ratio contributes toward enhanced network throughput, energy consumption, and lifetime with better service availability for Internet of things applications. A set of experiments using network simulator 2 revealed that our proposed approach outperforms the state-of-the-art low-energy adaptive clustering hierarchy and other recent protocols in terms of first-node death, overall energy consumption, and network throughput.
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Srivastava, Prakash, and Rakesh Kumar. "An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET." Journal of Information Technology Research 9, no. 4 (October 2016): 45–63. http://dx.doi.org/10.4018/jitr.2016100104.
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A mobile ad hoc network (MANET) is an autonomous collection of independent nodes cooperating together to form an infrastructure less network spontaneously. For increasing usability of MANET domain which finds application in natural disaster such as earthquake, floods etc. it is also desired to be connected with Internet through Internet gateways. Therefore, an efficient gateway discovery mechanism is required for MANET-Internet integration. Existing schemes use one or multiple parameters for optimal selection of gateway which causes a particular gateway to be selected many times which results in higher delay latency and packet drops due to prevailing congestion at a particular gateway. To avoid this situation, the authors have utilized the potential of fuzzy logic to ascertain the decision of load balancing at the Internet gateway. Besides this, their scheme also incorporates an effective adaptive gateway discovery mechanism. Consequently, enhanced performance is achieved as compared to existing state-of-the-art related schemes. The proposed approach is evaluated by simulation and analytical validation.
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Akyildiz, Ian F., Özgür B. Akan, Chao Chen, Jian Fang, and Weilian Su. "InterPlaNetary Internet: state-of-the-art and research challenges." Computer Networks 43, no. 2 (October 2003): 75–112. http://dx.doi.org/10.1016/s1389-1286(03)00345-1.
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Akyildiz, I. F., O. B. Akan, Chao Chen, Jian Fang, and Weilian Su. "The state of the art in interplanetary Internet." IEEE Communications Magazine 42, no. 7 (July 2004): 108–18. http://dx.doi.org/10.1109/mcom.2004.1316541.
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Wang, Zhihui, Jingjing Yang, Benzhen Guo, and Xiao Zhang. "Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network." International Journal of Mobile Computing and Multimedia Communications 10, no. 1 (January 2019): 1–17. http://dx.doi.org/10.4018/ijmcmc.2019010101.
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At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.
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Kumar, Suhansanu, and Hari Sundaram. "Attribute-Guided Network Sampling Mechanisms." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–24. http://dx.doi.org/10.1145/3441445.
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This article introduces a novel task-independent sampler for attributed networks. The problem is important because while data mining tasks on network content are common, sampling on internet-scale networks is costly. Link-trace samplers such as Snowball sampling, Forest Fire, Random Walk, and Metropolis–Hastings Random Walk are widely used for sampling from networks. The design of these attribute-agnostic samplers focuses on preserving salient properties of network structure, and are not optimized for tasks on node content. This article has three contributions. First, we propose a task-independent, attribute aware link-trace sampler grounded in Information Theory. Our sampler greedily adds to the sample the node with the most informative (i.e., surprising) neighborhood. The sampler tends to rapidly explore the attribute space, maximally reducing the surprise of unseen nodes. Second, we prove that content sampling is an NP-hard problem. A well-known algorithm best approximates the optimization solution within 1 − 1/ e , but requires full access to the entire graph. Third, we show through empirical counterfactual analysis that in many real-world datasets, network structure does not hinder the performance of surprise based link-trace samplers. Experimental results over 18 real-world datasets reveal: surprise-based samplers are sample efficient and outperform the state-of-the-art attribute-agnostic samplers by a wide margin (e.g., 45% performance improvement in clustering tasks).
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Paul, Christiane. "Renderings of Digital Art." Leonardo 35, no. 5 (October 2002): 471–84. http://dx.doi.org/10.1162/002409402320774303.
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This essay identifies the current qualifier of choice, “new media,” by explaining how this term is used to describe digital art in various forms. Establishing a historical context, the author highlights the pioneer exhibitions and artists who began working with new technology and digital art as early as the late 1960s and early 1970s. The article proceeds to articulate the shapes and forms of digital art, recognizing its broad range of artistic practice: music, interactive installation, installation with network components, software art, and purely Internet-based art. The author examines the themes and narratives specific to her selection of artwork, specifically interactive digital installations and net art. By addressing these forms, the author illustrates the hybrid nature of this medium and the future of this art practice.
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Ahmed, Ghufran, Danish Mahmood, and Saiful Islam. "Thermal and energy aware routing in wireless body area networks." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985497. http://dx.doi.org/10.1177/1550147719854974.
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Unlike many other Internet of Things applications, in addition to the limited power of Internet of Medical Things devices, the safety aspect and reliability are the major concerns while designing wireless body area network routing solutions. In fact, the heat dissipation from Internet of Medical Things devices and changes in body posture cause tissue damage and frequent wireless links breakage, respectively. Hence, designing an efficient routing solution is extremely challenging in wireless body area network. Considering the safety aspects of human body, the power constraint of Internet of Medical Things devices, and topological variations in wireless body area network environment, this article presents a thermal-aware, energy-efficient, and reliable routing protocol named thermal and energy aware routing. In the given perspective, thermal and energy aware routing considers the weighted average of three costs while selecting the routing path: energy consumption, heat dissipation, and link quality (between communicating nodes). The proposed protocol is validated by a comparison with a state-of-the-art wireless body area network routing protocol. Simulation results demonstrate that the proposed protocol is efficient in terms of energy consumption, thermal impact, and packet reception rate.
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Ali, Jaffar, Javed Iqbal, Shabbir Majeed, Imran Ahmed Mughal, Awais Ahmad, and Salman Ahmed. "Wireless sensor network design for smart grids and Internet of things for ambient living using cross-layer techniques." International Journal of Distributed Sensor Networks 15, no. 7 (July 2019): 155014771986220. http://dx.doi.org/10.1177/1550147719862208.
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Due to the increased demand of wireless sensor networks for their characteristics like low energy consumption, robustness, and low cost in several demanding and complex applications like smart grid, health and safety, traffic and weather updates, there is need of monitoring the infrastructure in a timely manner with high reliability. In this article, a cross-layer data communication scheme target-aware cross-layer technique is proposed to enhance reliability and to reduce the latency in wireless sensor networks. The proposed scheme uses connected dominating set at network layer where nodes in the connected dominating set are directly connected to all the other nodes in the network. Each sensor node sends the data to the nodes present in the connected dominating set, which forwards it to their respective destinations. The proposed scheme reduces the chances of collision resulting reduced delays. Higher packet delivery ratio is achieved with the proposed scheme results in improved reliability. The proposed scheme is outperforming other state-of-the-art schemes in terms of packet delivery ratio, latency, and data throughput with the default 802.15.4 and delay-responsive cross layer in both static and mobile scenarios using network simulator tool.
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Graziani, Salvatore, and Maria Gabriella Xibilia. "Innovative Topologies and Algorithms for Neural Networks." Future Internet 12, no. 7 (July 11, 2020): 117. http://dx.doi.org/10.3390/fi12070117.
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The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved the state of the art in many applications, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks is devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. The papers of this Special Issue make significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. Twelve papers are collected in the issue, addressing many relevant aspects of the topic.
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Allen, Chris, Ivaylo Vassilev, Sharon Xiaowen Lin, David Culliford, and Anne Rogers. "The contribution of internet use in personal networks of support for long-term condition self-management." Chronic Illness 15, no. 3 (February 28, 2018): 220–35. http://dx.doi.org/10.1177/1742395318759588.
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Objectives To describe the individual and network characteristics of the personal communities of people using the internet and the role of offline support, network resources and community participation in using the internet for condition management. Methods Secondary analysis of survey data using logistic regression analysis to determine the factors associated with differential internet use for condition management. This study involved 300 participants from 19 primary care providers in Manchester in 2010 and 2011. Results Using the internet is associated with age, deprivation, education and having access to a personal network member who understands how to fix computer problems. Those using the internet for condition management received more offline emotional work. No associations were found between using the internet for health and other types of offline support. Those using the internet for support reported lower levels of happiness. Conclusion Network processes and engagement shape online contact and use of resources for condition management. Those with access to personal networks who provide emotional work are likely to make use of online resources during non-crisis situations, suggesting that these resources act as an extension of offline network support. Those with greater levels of unhappiness may more frequently look to the internet for support.
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Khattak, Hasan, Zoobia Ameer, Ud Din, and Muhammad Khan. "Cross-layer design and optimization techniques in wireless multimedia sensor networks for smart cities." Computer Science and Information Systems 16, no. 1 (2019): 1–17. http://dx.doi.org/10.2298/csis181115004k.
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The future smart cities vision can be developed through leveraging the potentials of Internet of Things (IoT) and wireless sensor network (WSN) technologies. WSN is a resource constrained network where network nodes are tiny devices that are run on battery power. Diverse types of applications such as environmental and habitual monitoring, detection, and tracking, use WSNs. The invention of new network protocols, the establishment of new models for communications, and testing the available solutions in real world environment are some of the current research issues in WSNs. Main challenges in such networks include energy conservation in an efficient way, dealing with variable channel capacity, and the resource constrained nature of such networks. The design of architecture for such networks has a vital role in solving the issues to some extent, i.e., the cross layer design approach is an architectural technique that offers the interaction of different layers together to enhance the performance, minimize the energy consumption, enhance the network life time, and provide Quality of Service (QoS) in real time communications. These are some of the current areas where cross-layer design approaches are being used. This paper presents different types of cross-layer design techniques in wireless multimedia sensor networks. Using such architectural techniques, different state of the art cross-layer optimization approaches are discussed while giving the reader an insight on prominent challenges and issues along with future directions.
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Vuletic, Dejan. "Human organ trafficking in the cyber space." Temida 12, no. 3 (2009): 63–74. http://dx.doi.org/10.2298/tem0903063v.
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The accelerated growth of the information-communication technology use brought about cyber crime as a new form of crime connected with the misuse of computer network. Human trafficking and human organ trafficking are changing in line with the state-of-art technological achievements i.e. becoming more and more characteristic of cyber space. Passing appropriate regulations at both national and international levels presents an important step in solving the problem of human organ trafficking through Internet.
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Pal Chowdhury, Arjun, Pranav Kulkarni, and Mahdi Nazm Bojnordi. "MB-CNN: Memristive Binary Convolutional Neural Networks for Embedded Mobile Devices." Journal of Low Power Electronics and Applications 8, no. 4 (October 13, 2018): 38. http://dx.doi.org/10.3390/jlpea8040038.
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Applications of neural networks have gained significant importance in embedded mobile devices and Internet of Things (IoT) nodes. In particular, convolutional neural networks have emerged as one of the most powerful techniques in computer vision, speech recognition, and AI applications that can improve the mobile user experience. However, satisfying all power and performance requirements of such low power devices is a significant challenge. Recent work has shown that binarizing a neural network can significantly improve the memory requirements of mobile devices at the cost of minor loss in accuracy. This paper proposes MB-CNN, a memristive accelerator for binary convolutional neural networks that perform XNOR convolution in-situ novel 2R memristive data blocks to improve power, performance, and memory requirements of embedded mobile devices. The proposed accelerator achieves at least 13.26 × , 5.91 × , and 3.18 × improvements in the system energy efficiency (computed by energy × delay) over the state-of-the-art software, GPU, and PIM architectures, respectively. The solution architecture which integrates CPU, GPU and MB-CNN outperforms every other configuration in terms of system energy and execution time.
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Yochum, Phatpicha, Liang Chang, Tianlong Gu, and Manli Zhu. "Learning Sentiment over Network Embedding for Recommendation System." International Journal of Machine Learning and Computing 11, no. 1 (January 2021): 12–20. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1008.
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With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.
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Pandit, Mohammad Khalid, Roohie Naaz Mir, and Mohammad Ahsan Chishti. "Adaptive Deep Neural Networks for the Internet of Things." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (December 18, 2020): 570–81. http://dx.doi.org/10.2174/2210327910666191223124630.
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Background: Deep neural networks have become the state of the art technology for real- world classification tasks due to their ability to learn better feature representations at each layer. However, the added accuracy that is associated with the deeper layers comes at a huge cost of computation, energy and added latency. Objective: The implementations of such architectures in resource constraint IoT devices are computationally prohibitive due to its computational and memory requirements. These factors are particularly severe in IoT domain. In this paper, we propose the Adaptive Deep Neural Network (ADNN) which gets split across the compute hierarchical layers i.e. edge, fog and cloud with all splits having one or more exit locations. Methods: At every location, the data sample adaptively chooses to exit from the NN (based on confidence criteria) or get fed into deeper layers housed across different compute layers. Design of ADNN, an adaptive deep neural network which results in fast and energy- efficient decision making (inference). : Joint optimization of all the exit points in ADNN such that the overall loss is minimized. Results: Experiments on MNIST dataset show that 41.9% of samples exit at the edge location (correctly classified) and 49.7% of samples exit at fog layer. Similar results are obtained on fashion MNIST dataset with only 19.4% of the samples requiring the entire neural network layers. With this architecture, most of the data samples are locally processed and classified while maintaining the classification accuracy and also keeping in check the communication, energy and latency requirements for time sensitive IoT applications. Conclusion: We investigated the approach of distributing the layers of the deep neural network across edge, fog and the cloud computing devices wherein data samples adaptively choose the exit points to classify themselves based on the confidence criteria (threshold). The results show that the majority of the data samples are classified within the private network of the user (edge, fog) while only a few samples require the entire layers of ADNN for classification.
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Cho, Seunghyun, Dong-Wook Kim, and Seung-Won Jung. "Quality enhancement of VVC intra-frame coding for multimedia services over the Internet." International Journal of Distributed Sensor Networks 16, no. 5 (May 2020): 155014772091764. http://dx.doi.org/10.1177/1550147720917647.
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In this article, versatile video coding, the next-generation video coding standard, is combined with a deep convolutional neural network to achieve state-of-the-art image compression efficiency. The proposed hierarchical grouped residual dense network exhaustively exploits hierarchical features in each architectural level to maximize the image quality enhancement capability. The basic building block employed for hierarchical grouped residual dense network is residual dense block which exploits hierarchical features from internal convolutional layers. Residual dense blocks are then combined into a grouped residual dense block exploiting hierarchical features from residual dense blocks. Finally, grouped residual dense blocks are connected to comprise a hierarchical grouped residual dense block so that hierarchical features from grouped residual dense blocks can also be exploited for quality enhancement of versatile video coding intra-coded images. Various non-architectural and architectural aspects affecting the training efficiency and performance of hierarchical grouped residual dense network are explored. The proposed hierarchical grouped residual dense network respectively obtained 10.72% and 14.3% of Bjøntegaard-delta-rate gains against versatile video coding in the experiments conducted on two public image datasets with different characteristics to verify the image compression efficiency.
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Ok, Jin-Sung, Soon-Do Kwon, Cheol-Eun Heo, and Young-Kyoon Suh. "A Survey of Industrial Internet of Things Platforms for Establishing Centralized Data-Acquisition Middleware: Categorization, Experiment, and Challenges." Scientific Programming 2021 (April 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/6641562.
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The development of industrial Internet of Things (IIoT), big data, and artificial intelligence technologies is leading to a major change in the production system. The change is being propagated into the wave of transforming the existing system with a vertical structure into the corresponding horizontal platform or middleware. Accordingly, the way of acquiring IIoT data from an individual system is being altered to the way of being increasingly centralized through an integrated middleware of a scalable server or through a large platform. That said, middleware-based IIoT data acquisition must consider multiple factors, such as infrastructure (e.g., operation environment and network), protocol heterogeneity, interoperability (e.g., links with legacy systems), real-time, and security. This manuscript explains these five aspects in detail and provides a taxonomy of eighteen state-of-the-art IIoT data-acquisition middleware systems based on these aspects. To validate one of these aspects (network), we present our evaluation results at a real production site where IIoT data-acquisition loss rates are compared between wireless (long-term evolution) and wired networks. As a result, the wired communication can be more suitable for centralized IIoT data-acquisition middleware than wireless networks. Finally, we discuss several challenges in establishing the best IIoT data-acquisition middleware in a centralized way.
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Natesan, Sathishkumar, and Rajakumar Krishnan. "Energy Efficient Composite Metric Based Routing Protocol for Internet of Things." JUCS - Journal of Universal Computer Science 26, no. 11 (November 28, 2020): 1366–81. http://dx.doi.org/10.3897/jucs.2020.072.
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The Routing Protocol for Low Power and Lossy Networks (RPL) is operated by gadgets comprised of many devices of embedded type with limited energy, memory as well as resources that do their process. The improvements in the life of the network and energy conservation are the key challenging features in Low Power and Lossy Networks (LLN). Obviously, the LLN has a key strategic part in routing. The Internet of Things (IoT) device is expected to make the apt choice. In LLN, the poor routing choice leads to traffic congestion, reduction in power as well as packet loss ratio. The task in the proposal analyzes Delay (D), Load (L) and Battery Discharge Index (BDI) pivoted Energy Efficient Composite Metric Routing (EECMR) protocol for LLN. The performance of the work in the proposal is evaluated by the COOJA simulator. It outperforms with respect to Network Lifetime (NL), Delay as well as Packet Delivery Ratio (PDR) contrasted to the routing metrics like Traffic Load (TL), Link Quality (LQ), Residual Energy (RE), RE-Battery Discharge Index (RE-BDI) and Hop Count (HC).
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Dang, L., Syed Hassan, Suhyeon Im, Jaecheol Lee, Sujin Lee, and Hyeonjoon Moon. "Deep Learning Based Computer Generated Face Identification Using Convolutional Neural Network." Applied Sciences 8, no. 12 (December 13, 2018): 2610. http://dx.doi.org/10.3390/app8122610.
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Generative adversarial networks (GANs) describe an emerging generative model which has made impressive progress in the last few years in generating photorealistic facial images. As the result, it has become more and more difficult to differentiate between computer-generated and real face images, even with the human’s eyes. If the generated images are used with the intent to mislead and deceive readers, it would probably cause severe ethical, moral, and legal issues. Moreover, it is challenging to collect a dataset for computer-generated face identification that is large enough for research purposes because the number of realistic computer-generated images is still limited and scattered on the internet. Thus, a development of a novel decision support system for analyzing and detecting computer-generated face images generated by the GAN network is crucial. In this paper, we propose a customized convolutional neural network, namely CGFace, which is specifically designed for the computer-generated face detection task by customizing the number of convolutional layers, so it performs well in detecting computer-generated face images. After that, an imbalanced framework (IF-CGFace) is created by altering CGFace’s layer structure to adjust to the imbalanced data issue by extracting features from CGFace layers and use them to train AdaBoost and eXtreme Gradient Boosting (XGB). Next, we explain the process of generating a large computer-generated dataset based on the state-of-the-art PCGAN and BEGAN model. Then, various experiments are carried out to show that the proposed model with augmented input yields the highest accuracy at 98%. Finally, we provided comparative results by applying the proposed CNN architecture on images generated by other GAN researches.
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Głowacz, Andrzej, Michał Grega, Mikołaj Leszczuk, Zdzisław Papir, Piotr Romaniak, Paweł Fornalski, Michał Lutwin, Jürgen Enge, Tabea Lurk, and Viliam Šimko. "Open internet gateways to archives of media art." Multimedia Tools and Applications 59, no. 3 (March 26, 2011): 897–920. http://dx.doi.org/10.1007/s11042-011-0784-3.
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Plotnikova, T. V., and O. V. Kotelnikova. "The Phenomenon of Cybercrime in the 21st Century." Pravo: istoriya i sovremennost', no. 3(12) (2020): 141–50. http://dx.doi.org/10.17277/pravo.2020.03.pp.141-150.
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The spread and development of the “Internet” has erased the boundaries of communication and the dissemination of information. With the massive spread of computers, the world community is faced with such phenomena as illegal activities using a computer (and / or against computers), as well as this activity through the Internet. Many scientists and experts distinguish between cybercrime as a legal category and as a social phenomenon. The first is characterized by a specific unlawful act using a computer, computer network or the Internet. The qualification of these acts within the framework of cybercrime is not in doubt and complexity, because here are clearly expressed signs of a virtual illegal act. These illegal acts are aimed at all spheres of public life. There is no clear division into specific types of cybercrime. For cybercriminal prosecution, it is necessary to prove their involvement in this illegal act. The “virtual traces” of an illegal act are easy to hide due to the ease of changing and destroying computer information. A procedure for processing and seizing evidence is quite complicated, and sometimes impossible. These facts make it difficult to prove the guilt and involvement of a person in a virtual crime, thereby contributing to an increase in the percentage of unsolved crimes. Cybercrime has become a threat to security of man, society and the government. It poses a real threat not only to individual states, but also to the entire world community as a whole.
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Ilyas, Muhammad, Zahid Ullah, Fakhri Alam Khan, Muhammad Hasanain Chaudary, Muhammad Sheraz Arshed Malik, Zafar Zaheer, and Hamood Ur Rehman Durrani. "Trust-based energy-efficient routing protocol for Internet of things–based sensor networks." International Journal of Distributed Sensor Networks 16, no. 10 (October 2020): 155014772096435. http://dx.doi.org/10.1177/1550147720964358.
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Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency.
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Matni, Nagib, Jean Moraes, Helder Oliveira, Denis Rosário, and Eduardo Cerqueira. "LoRaWAN Gateway Placement Model for Dynamic Internet of Things Scenarios." Sensors 20, no. 15 (August 4, 2020): 4336. http://dx.doi.org/10.3390/s20154336.
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Extended Range Wide Area Network (LoRaWAN) has recently gained a lot of attention from the industrial and research community for dynamic Internet of Things (IoT) applications. IoT devices broadcast messages for neighbor gateways that deliver the message to the application server through an IP network. Hence, it is required to deploy LoRaWAN gateways, i.e., network planning, and optimization, in an environment while considering Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) along with Quality of Service (QoS) requirements. In this article, we introduced a LoRaWAN gateway placement model for dynamic IoT applications called DPLACE. It divides the IoT devices into groups with some degree of similarity between them to allow for the placement of LoRaWAN gateways that can serve these devices in the best possible way. Specifically, DPLACE computes the number of LoRaWAN gateways based on the Gap statistics method. Afterward, DPLACE uses K-Means and Fuzzy C-means algorithms to calculate the LoRaWAN gateway placement. The simulations’ results proved the benefits of DPLACE compared to state-of-the-art LoRaWAN gateway placement models in terms of OPEX, CAPEX, and QoS.
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BARBOSA, ALVARO. "Public Sound Objects: a shared environment for networked music practice on the Web." Organised Sound 10, no. 3 (November 29, 2005): 233–42. http://dx.doi.org/10.1017/s135577180500097x.
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The Public Sound Objects (PSOs) project consists of the development of a networked musical system, which is an experimental framework to implement and test new concepts for online music communication. The PSOs project approaches the idea of collaborative musical performances over the Internet by aiming to go beyond the concept of using computer networks as a channel to connect performing spaces. This is achieved by exploring the internet's shared nature in order to provide a public musical space where anonymous users can meet and be found performing in collective sonic art pieces.The system itself is an interface-decoupled musical instrument, in which a remote user interface and a sound processing engine reside with different hosts in an extreme scenario where a user can access the synthesizer from any place in the world using the World Wide Web. Specific software features were implemented in order to reduce the disruptive effects of network latency, such as dynamic adaptation of the musical tempo to communication latency measured in real time and consistent sound panning with the object's behaviour at the graphical user interface.
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Dong, Junliang, Ruixia Feng, and Tinghua Li. "New media fine art education platform based on internet of things technology." International Journal of Internet Protocol Technology 14, no. 3 (2021): 131. http://dx.doi.org/10.1504/ijipt.2021.10040800.
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Feng, Ruixia, Tinghua Li, and Junliang Dong. "New media fine art education platform based on internet of things technology." International Journal of Internet Protocol Technology 14, no. 3 (2021): 131. http://dx.doi.org/10.1504/ijipt.2021.117410.
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Yang, Hao, Cheng Hua Wang, and Yi Jun Cui. "Design and Implementation of Wireless Sensor Network Nodes Based on ANT." Applied Mechanics and Materials 325-326 (June 2013): 1037–40. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1037.
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Progress as well as integration of wireless communication technology, computer technology and semiconductor technology, can integrate information such as the perception,acquisition and data processing in a very limited volume [. With the rapid development of wireless technology,wireless sensor networks greatly extends the applications of sensor network and Internet of Things as well as the new challenges to wireless communication technology. We will use STC12LE4052AD microcontroller and ultra-low power radio frequency chip nRF24AP2 embedded ANT protocol stack to complete the development of wireless sensor nodes in this paper.
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Akhriana, Asmah, and Andi Irmayana. "WEB APP PENDETEKSI JENIS SERANGAN JARINGAN KOMPUTER DENGAN MEMANFAATKAN SNORT DAN LOG HONEYPOT." CCIT Journal 12, no. 1 (February 6, 2019): 85–96. http://dx.doi.org/10.33050/ccit.v12i1.604.
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Along with the current development of Information Technology is always changing to make the security of an information is very important, especially on a network connected to the internet. But what is unfortunate is that the imbalance between each development of a technology is not accompanied by developments in the security system itself, so that there are quite a lot of systems that are still weak and have to be increased by the security wall. This study aims to design a Web-based App interface to facilitate users or administrators in securing network computers from various types of attacks. The Instrusion detection system (IDS) method is used to detect suspicious activity in a system or network using snort and honeypot. Honeypot is built on a computer along with Apache, MySQL, and Snort. Honeypot will act as a target to attract attackers and log information from the attacker and snort to apply the rules made from the web. The functional system will then be tested using the black box testing method. The results of this study concluded that Web App-based interfaces that are created can be used to help users and administrators in maintaining data and information on server computers from various types of attacks on computer networks
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Ehrlich, Marco, Arne Neumann, Alexander Biendarra, and Jürgen Jasperneite. "Quality-of-Service monitoring of hybrid industrial communication networks." at - Automatisierungstechnik 67, no. 1 (January 28, 2019): 69–78. http://dx.doi.org/10.1515/auto-2018-0057.
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Abstract Today many hybrid (wired & wireless) industrial communication networks with a huge variety of heterogeneous technologies and protocols are present in the manufacturing and automation domain. The increasing requirements regarding e. g., latency, reliability, or determinism create the need for a holistic network management concept in order to assure a network-wide Quality-of-Service (QoS) resource provisioning and the assurance of the admissioned resources. Consequently, a monitoring of the whole network is required to feed the network management system with the needed information about the underlying network processes. Various technical approaches using different methods of extracting the information from network traffic are available for the purpose of QoS parameter observance and measurement at the moment. Therefore, this paper provides a state of the art research about network management and QoS provisioning respectively QoS assurance concepts. In addition, the passive network monitoring approach using the flow export technique based on the Internet Protocol Flow Information Export (IPFIX) is investigated for a utilisation in the nowadays industry domain based on a conceptual case study with a wireless protocol. As a conclusion, an evaluation is performed in order to clarify the limits and the overall usability of IPFIX for the monitoring of industrial networks in order to support future network management systems.
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Khalil, Samar Samir, Sherin M. Youssef, and Sherine Nagy Saleh. "iCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection." Future Internet 13, no. 4 (April 5, 2021): 93. http://dx.doi.org/10.3390/fi13040093.
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Fake media is spreading like wildfire all over the internet as a result of the great advancement in deepfake creation tools and the huge interest researchers and corporations are showing to explore its limits. Now anyone can create manipulated unethical media forensics, defame, humiliate others or even scam them out of their money with a click of a button. In this research a new deepfake detection approach, iCaps-Dfake, is proposed that competes with state-of-the-art techniques of deepfake video detection and addresses their low generalization problem. Two feature extraction methods are combined, texture-based Local Binary Patterns (LBP) and Convolutional Neural Networks (CNN) based modified High-Resolution Network (HRNet), along with an application of capsule neural networks (CapsNets) implementing a concurrent routing technique. Experiments have been conducted on large benchmark datasets to evaluate the performance of the proposed model. Several performance metrics are applied and experimental results are analyzed. The proposed model was primarily trained and tested on the DeepFakeDetectionChallenge-Preview (DFDC-P) dataset then tested on Celeb-DF to examine its generalization capability. Experiments achieved an Area-Under Curve (AUC) score improvement of 20.25% over state-of-the-art models.
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Papadogiannaki, Eva, and Sotiris Ioannidis. "A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures." ACM Computing Surveys 54, no. 6 (July 2021): 1–35. http://dx.doi.org/10.1145/3457904.
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The adoption of network traffic encryption is continually growing. Popular applications use encryption protocols to secure communications and protect the privacy of users. In addition, a large portion of malware is spread through the network traffic taking advantage of encryption protocols to hide its presence and activity. Entering into the era of completely encrypted communications over the Internet, we must rapidly start reviewing the state-of-the-art in the wide domain of network traffic analysis and inspection, to conclude if traditional traffic processing systems will be able to seamlessly adapt to the upcoming full adoption of network encryption. In this survey, we examine the literature that deals with network traffic analysis and inspection after the ascent of encryption in communication channels. We notice that the research community has already started proposing solutions on how to perform inspection even when the network traffic is encrypted and we demonstrate and review these works. In addition, we present the techniques and methods that these works use and their limitations. Finally, we examine the countermeasures that have been proposed in the literature in order to circumvent traffic analysis techniques that aim to harm user privacy.
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Piotrowska, Agnieszka. "On Cross-Layer Interactions for Congestion Control in the Internet." Applied Sciences 11, no. 17 (August 25, 2021): 7808. http://dx.doi.org/10.3390/app11177808.
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Two key mechanisms of the Internet are congestion control in the Transmission Control Protocol (TCP) and Active Queue Management (AQM) in routers. The former divides the bandwidth between flows and prevents the Internet from congestion collapse. Simultaneously, the latter informs hosts of the forthcoming congestion by preventive dropping of packets in network nodes. Although these two key mechanisms may severely interact with each other, they are often being researched independently, in parallel. This has led to the development of a few new congestion controls and AQM algorithms known for excellent performance under the assumption that the counterpart remains unaltered. It is unclear, however, how these new solutions in both areas interact with each other. The purpose of this paper is to fill this gap. Namely, in an extensive set of simulations, the impact of interactions between the state-of-the-art congestion control and AQM algorithms on the TCP connection performance is studied. As a result, recommendations for using some particular TCP-AQM pairs, which are observed to perform especially well, are formulated.
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Sinha, Bam Bahadur, and R. Dhanalakshmi. "Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy." Computer Journal 63, no. 11 (November 19, 2019): 1624–32. http://dx.doi.org/10.1093/comjnl/bxz086.
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Abstract With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.
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Luo, Hongwei, Yijie Shen, Feng Lin, and Guoai Xu. "Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference." Security and Communication Networks 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/6664578.
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Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.
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Sri, Kurra Santhi, Komanduri Venkata Sesha Sai Rama Krishna, Venkata Bhujanga Rao Madamanchi, and Gondi Yasoda Devi. "Advanced System Control with Traffic Handling for Secure Communication in IoT Routing Protocol." Journal Européen des Systèmes Automatisés 54, no. 2 (April 27, 2021): 229–33. http://dx.doi.org/10.18280/jesa.540204.
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The Internet of Things (IoT) can simply be referred to as the network of things comprising software, sensors, electronics, allowing data to be collected and transmitted. The next step in the field of technology is the Internet of Things, bringing tremendous improvements to manufacturing, medicine, environmental treatment, and urban growth. In shaping this vision, multiple challenges need to be faced, such as technology interoperability problems, protection and data confidentiality standards and, last but not least, the implementation of energy efficient management systems. These devices with minimal human interference are capable of producing, sharing and consuming data. The networking of related as well as heterogeneous devices is often known to be IoT. The Internet of Things allows things to connect and interact with each other, thus minimizing human involvement in simple daily tasks. Addressing protection at all times or at any position for many users, companies, governments, and enterprises is really necessary and responsive. In this paper a secure IOT architecture for routing in a network with RPL Rapid Node Link Routing (RNLR) Model is proposed that performs traffic management and traffic analysis for secure communication using IoT routing protocol. It mainly aims to locate the malicious users in a IOT routing protocols. the proposed mechanism is compared with the state of the art work and compared results shows the proposed work performs well.
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Malik, Praveen Kumar, Rohit Sharma, Rajesh Singh, Anita Gehlot, Suresh Chandra Satapathy, Waleed S. Alnumay, Danilo Pelusi, Uttam Ghosh, and Janmenjoy Nayak. "Industrial Internet of Things and its Applications in Industry 4.0: State of The Art." Computer Communications 166 (January 2021): 125–39. http://dx.doi.org/10.1016/j.comcom.2020.11.016.
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Ojha, Tamoghna, Sudip Misra, and Narendra Singh Raghuwanshi. "Internet of Things for Agricultural Applications: The State of the Art." IEEE Internet of Things Journal 8, no. 14 (July 15, 2021): 10973–97. http://dx.doi.org/10.1109/jiot.2021.3051418.
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