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نتیجه جستجو - WSNS

تعداد مقالات یافته شده: 53
ردیف عنوان نوع
1 An efficient biometric based authenticated geographic opportunistic routing for IoT applications using secure wireless sensor network
یک مسیریابی فرصت طلبانه جغرافیایی معتبر مبتنی بر بیومتریک برای برنامه های IoT با استفاده از شبکه حسگر بی سیم امن-2021
The applications of Wireless Sensor Networks (WSNs) are been broadly utilized in the field of Internet of Things (IoT) under communication framework. Notwithstanding services gave by the WSNs, numerous IoT-related applications necessitate reliable and secure delivery of data over unsteady remote connec tions. In-order to ensure secure and reliable delivery of data, many existing paper works accomplish authentication based routing algorithms with numerous forwarders within the Wireless Sensor Networks. Be that as it may; these types of approaches are vulnerable to genuine attacks like Denial of Service (DoS), where countless duplicate data packets are intentionally dispatched to destination node which disturbs the typical activities of wireless sensor networks. So, here we propose a new scheme of security algorithm for the wireless sensor networks. Our method, Biometric based-Authenticated Geographic Opportunistic Routing (BAGOR) algorithm depends on the user biometrics to shield the violation of DoS attacks, in order to meet out the validness requirements and reliability in the network. By examining biometric and statistic state information (SSI) of remote connections, BAGOR uses a trust model as statistic state information to get better proficiency of packet delivery. Dissimilar to past pioneering routing algorithm, BAGOR guarantees data honesty by building up an entropy-deployed selective validation algorithm and can detach DoS aggressors and reduce the computational expense. Thus, the eveloped procedure is assessed and compared with already existing security techniques. The simulations show that BAGOR decreasing system traffic, shielding against Denial of Service attacks, and expanding the lifetime of a sensor node in the network. Thus, the usefulness and execution of the whole system is enhanced.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Biometric authentication | BAGOR algorithm | Denial-of-Service attacks | Geographic opportunistic routing | Statistic state information
مقاله انگلیسی
2 A distributed coverage hole recovery approach based on reinforcement learning for Wireless Sensor Networks
رویکرد بازیابی چاله پوشش توزیع شده مبتنی بر یادگیری تقویتی برای شبکه های حسگر بی سیم-2020
In Wireless Sensor Networks (WSNs), various anomalies may arise and reduce their reliability and effi- ciency. For example, Coverage Hole can occur in such networks due to several causes, such as damaging events, sensors battery exhaustion, hardware failure, and software bugs. Modern trends to use relocation of deployed sensor nodes when the manual addition of nodes is neither doable nor economical in many applications have attracted attention. The lack of central supervision and control in harsh and hostile en- vironments have encouraged researchers to shift from centralized to distributed node relocation schemes. In this paper, a new game theory approach based on reinforcement learning to recover Coverage Holes in a distributed way is proposed. For the formulated potential game, sensor nodes can recover Coverage Holes using only local acquaintances. To reduce the coverage gaps, the combined action of node repo- sition and sensing range adjustment is chosen by each sensor node. The simulation results prove that, unlike previous methods, the proposed approach can sustain a network overall coverage in the presence of random damage events.
Keywords: WSN | Coverage hole recovery | Game theory | Reinforcement learning | Energy consumption | Coverage
مقاله انگلیسی
3 Enhanced authentication and key management scheme for securing data transmission in the internet of things
تصدیق پیشرفته و برنامه مدیریت کلیدی برای اطمینان از انتقال داده ها در اینترنت اشیا-2019
The Internet of Things (IoT), with its smartness and intelligence, is gradually changing human life by al- lowing everyday objects to be connected to the Internet. With the prevalence of the IoT, wireless sensor networks (WSNs) are attracting worldwide attention, because they cover a wide range of IoT applica- tions. The sensors collect data from the physical world and communicate with each other through wire- less links. Ensuring the security and privacy of WSNs’ communication is challenging. Recently, a secure authentication and key management scheme was proposed to secure data transmission in WSNs. In this paper, we show that this scheme has various security flaws, such as replay attack, denial of service attack, impersonation attack, and lack of mutual authentication and session key agreement. Then, we propose an enhanced scheme to overcome the identified security weaknesses. The security of the enhanced scheme is formally verified using the Burrows–Abadi–Needham logic and the Automated Validation of Internet Security Protocols and Applications tool. Our proposed scheme is more secure, efficient, and suitable for WSN-based IoT applications than recent related methods.
Keywords: Privacy | Mutual authentication | Key agreement | Elliptic curve cryptography | BAN logic | AVISPA
مقاله انگلیسی
4 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities
Keywords: Structural health monitoring | Data mining | Artificial intelligence | Probabilistic approach | Signal time delay
مقاله انگلیسی
5 Data mining methodology employing artificial intelligence and a probabilistic approach for energy-efficient structural health monitoring with noisy and delayed signals
روش داده کاوی با استفاده از هوش مصنوعی و یک رویکرد احتمالی برای نظارت بر سلامت ساختاری کارآمد با انرژی با سیگنال های پر سر و صدا و تأخیر-2019
Numerous methods have been developed in the context of expert and intelligent systems for structural health monitoring (SHM) with wireless sensor networks (WSNs). However, these techniques have been proven to be efficient when dealing with continuous signals, and the applicability of such expert sys- tems with discrete noisy signals has not yet been explored. This study presents an intelligent data min- ing methodology as part of an expert system developed for SHM with noisy and delayed signals, which are generated by a through-substrate self-powered sensor network. The noted sensor network has been demonstrated as an effective means for minimizing energy consumption in WSNs for SHM. Experimen- tal vibration tests were conducted on a cantilever plate to evaluate the developed expert system for SHM. The proposed data mining method is based on the integration of pattern recognition, an innova- tive probabilistic approach, and machine learning. The novelty of the proposed system for SHM with data interpretation methodology lies in the integration of the noted intelligent techniques on discrete, binary, noisy, and delayed patterns of signals collected from self-powered sensing technology in the applica- tion to a practical engineering problem, i.e., data-driven energy-efficient SHM. Results confirm that the proposed data mining method employing a probabilistic approach can be effectively used to reconstruct delayed and missing signals, thereby addressing the important issue of energy availability for intelligent SHM systems being used for damage identification in civil and aerospace structures. The applicability and effectiveness of the expert system with the data mining approach in detecting damage with noisy sig- nals was demonstrated for plate-like structures with an accuracy of 97%. The present study successfully contributes to advance data mining and signal processing techniques in the SHM domain, indicating a practical application of expert and intelligent systems applied to damage detection in SHM platforms. Findings from this research pave a way for development of the data analysis techniques that can be em- ployed for interpreting noisy and incomplete signals collected from various expert systems such as those being used in intelligent infrastructure monitoring systems and smart cities.
Keywords: Structural health monitoring | Data mining | Artificial intelligence| Probabilistic approach | Signal time delay
مقاله انگلیسی
6 Big Data Challenges and Data Aggregation Strategies in Wireless Sensor Networks
چالش های داده بزرگ و استراتژی های جمع آوری داده ها در شبکه های حسگر بی سیم-2018
The emergence of new data handling technologies and analytics enabled the organization of big data in processes as an innovative aspect in wireless sensor networks (WSNs). Big data paradigm, combined with WSN technology, involves new challenges that are necessary to resolve in parallel. Data aggregation is a rapidly emerging research area. It represents one of the processing challenges of big sensor networks. This paper introduces the big data paradigm, its main dimensions that represent one of the most challenging concepts, and its principle analytic tools which are more and more introduced in the WSNs technology. The paper also presents the big data challenges that must be overcome to efficiently manipulate the voluminous data, and proposes a new classification of these challenges based on the necessities and the challenges of WSNs. As the big data aggregation challenge represents the center of our interest, this paper surveys its proposed strategies in WSNs.
INDEX TERMS: Big data, data aggregation, wireless sensor networks
مقاله انگلیسی
7 A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing
مدل تحمل نظارت بر گسل چند عامل بر اساس یک خوشه GPU برای پردازش داده های بزرگ-2018
High-performance computing clusters are widely used in large-scale data mining applica tions, and have higher requirements for persistence, stability and real-time use and sre therefore computationally intensive. To support large-scale data processing, we design a multi-factor real-time monitoring fault tolerance (MRMFT) model based on a GPU clus ter. However, the higher clock frequency of GPU chips results in excessively high energy consumption in computing systems. Moreover, the ability to support a long-lasting high temperature operation varies greatly between different GPUs owing to the individual dif ferences between the chips. In this paper, we design a GPU cluster energy consumption monitoring system based on wireless sensor networks (WSNs) and propose an energy con sumption aware checkpointing (ECAC) for high energy consumption problems with the following two advantages: the system sets checkpoints according to actual energy con sumption and the device temperature to improve the utilization of checkpoints and re duce time cost; and it exploits the parallel computing features of CPU and GPU to hide the CPU detection overhead in GPU parallel computation, and further reduce the time and energy consumption overhead in the fault tolerance phase. Using ECAC as the constraint and aiming for a persistent and reliable operation, the dynamic task migration mechanism is designed, and the reliability of the cluster is greatly improved. The theoretical analysis and experiment results show that the model improves the persistence and stability of the computing system while reducing checkpoint overhead.
Keywords: Big data processing ، GPU cluster ، Persistence computing ، Energy consumption ، Fault tolerance ، Energy consumption aware heckpointing ، Task migration
مقاله انگلیسی
8 Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors
بهینه سازی مصرف انرژی برای جمع آوری داده های بزرگ در شبکه های حسگر بی سیم مقیاس بزرگ با جمع کننده های سیار-2018
Big sensor-based data environment and the emergence of large-scale wireless sensor networks (LS-WSNs), which are spread over wide geographic areas and contain thousands of sensor nodes, require new techniques for energy-efficient data collection. Recent approaches for data collection in WSNs have focused on techniques using mobile data collectors (MDCs) or sinks. Compared to traditional methods using static sinks, the MDC techniques give two advantages for data collection in LSWSNs. These techniques can handle data collection over spatially separated geographical regions, and have been shown to require lower node energy consumption. Two common models for data collection using MDCs have been proposed: data collection using data mule (MULE), and sensor network with mobile access point (SENMA). The MULE and SENMA approaches can be characterized as representative of the multihop and the single-hop approaches for mobile data collection in WSNs. Although the basic architectures for MULE and SENMA have been well studied, the emergence of LS-WSNs which require partitioning the network into multiple groups and clusters prior to data collection has not been particularly addressed. This paper presents analytical approaches to determine the node energy consumption for LS-WSN MDC schemes and gives models for determining the optimal number of clusters for minimizing the energy consumption. The paper alsoaddressesthetradeoffswhentheLS-WSNMULEandSENMA models perform well.
Index Terms: Data collection using data mule (MULE), energy consumption, large-scale wireless sensor networks (LS-WSNs),mobile data collectors, sensor network with mobile access point (SENMA)
مقاله انگلیسی
9 Adaptive Queuing Censoring for Big Data Processing
سانسور کردن صف ادغام برای پردازش داده های بزرگ-2018
In the era of big data, adaptive censoring (AC) provides us a natural option of trimming data by only keeping the statistical informative data. However, the data chosen by AC may arrive in clusters, which do not relieve the computational resource requirement as expected. In this letter, we exploit queuing theory to model a single sink node with abundant sensor nodes. By adding a buffer to censored distributed wireless sensor networks (WSNs), the uncensored data can be modeled as a queue. With the buffer, the new algorithm entails simple, closed-form updates, and has no loss in terms of estimation accuracy comparing to the original AC method. The proposed model can further reduce the communication cost of distributed WSNs. The proposed model is illustrated in a linear regression setting. Numerical results validate the effectiveness of the proposed model in dealing with data congestion problem.
Index Terms: Adaptive censoring (AC), big data, data conges tion, M/C/1/K, parameter estimation
مقاله انگلیسی
10 HACH : الگوریتم اکتشافی برای خوشه بندی پروتکل سلسله مراتبی در شبکه حسگر بی سیم
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 38
شبکه های حسگر بی سیم (WSN) ها نیاز به پروتکل های مدیریت انرژی برای کارآمد بودن محدودیت های انرژی مصرفی با سنسورهای باتری را دارند تا طول عمر شبکه خود را افزایش دهند. این مقاله یک الگوریتم جدید اکتشافی برای سلسله مراتب خوشه ای (HACH) ارائه می دهد که به طور پیوسته انتخاب گره های غیر فعال و گره های خوشه ای را در هر دور انجام می دهد. انتخاب گره غیر فعال استفاده از مکانیسم زمانبندی زمانی تصادفی را برای تعیین گره هایی که می توانند در حالت خواب قرار گیرند بدون تأثیری بر پوشش شبکه تاثیر می گذارد. همچنین، الگوریتم خوشه بندی از یک اپراتور متقاطع اکتشافی جدید برای ترکیب دو راه حل متفاوت برای دستیابی به یک راه حل بهبود یافته استفاده می کند که باعث افزایش توزیع گره های خوشه و متناسب کردن مصرف انرژی در WSN می شود. الگوریتم پیشنهادی از طریق آزمایشهای شبیه سازی و با برخی از الگوریتم های موجود مقایسه می شود. پروتکل ما نشان می دهد عملکرد بهبود یافته از لحاظ طول عمر طولانی و حفظ عملکرد مطلوب حتی تحت تنظیمات مختلف ناهمگونی انرژی.
کلید واژه ها: شبکه های حسگر بی سیم | زمانبندی خواب | خوشه بندی | متقاطع اکتشافی | پوشش | ناهمگونی انرژی
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