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نتیجه جستجو - Wireless sensor Networks

تعداد مقالات یافته شده: 111
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 Real-time monitoring of the athletes musculoskeletal health based on an embedded processor
نظارت بر زمان واقعی سلامت اسکلتی عضلانی ورزشکار بر اساس پردازنده تعبیه شده-2021
In this paper, we use wireless sensor network in motion analysis and computer vision processing. We mainly monitor and analyze various kinds of motion through wireless sensor network. When dealing with computer vision, we use wireless sensor networks to capture moving objects and analyze their detailed motion parameters. We mainly use two types of sensors to receive information, and then fuse the data they receive. These data can be applied in various fields, such as robot field. In this study, we also proposed the predictive data analysis technology, which can be used to determine the health status of the body by monitoring the robot and movement under certain conditions. At the same time, this technology can also be applied to the rehabilitation training of the injured patients. This technique can not only be used in exercise analysis, but also provide some useful suggestions and optimal exercise time for patients and rehabilitation training, which is very helpful for patients and rehabilitation and strength training. In this paper, the pilot and work is simulated by different actions, and a large number of visual motion data are generated in the experiment. In addition, we also explored some elderly patients with dyskinesia, and carried out disease monitoring and exercise monitoring on them.
Keywords: Athlete’s health Monitoring | Predictive data analysis technique (PDAT) | Field programmable gate array (FPGA)
مقاله انگلیسی
3 On a multisensor knowledge fusion heuristic for the Internet of Things
مکاشفه ترکیبی دانش چندحسگری برای اینترنت اشیا-2021
Internet of Things (IoT) is envisioned as the interconnection of the Internet with sensing and actuating devices. IoT systems are usually designed to collect massive amounts of data from multiple and possibly conflicting sources. Nevertheless, data must be refined before being stored in a repository, so as information can be correctly extracted for further uses. Knowledge fusion is an important technique to identify and eliminate erroneous data from compromised sources or any mistakes that might have occurred during the extraction process. We propose a new multisensor knowledge fusion heuristic (MKFH) for IoT supporting the knowledge extraction and transfer needed to further knowledge management, also discuss the role of reinforcement learning over integration on a multi-application wireless sensor/actuator network (WSAN). Results shows that the proposed multisensor knowledge fusion heuristic is compatible with the IoT paradigm and enhances integration.
Keywords: Multisensor knowledge fusion | Multisensor data fusion | Wireless sensor networks | Internet of things | Knowledge management
مقاله انگلیسی
4 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
5 A Deep Reinforcement Learning-Based On-Demand Charging Algorithm for Wireless Rechargeable Sensor Networks
الگوریتم شارژ تقاضایی مبتنی بر یادگیری تقویتی عمیق برای شبکه های حسگر قابل شارژ بی سیم-2020
Wireless rechargeable sensor networks are widely used in many fields. However, the limited battery capacity of sensor nodes hinders its development. With the help of wireless energy transfer technology, employing a mobile charger to charge sensor nodes wirelessly has become a promising technology for prolonging the lifetime of wireless sensor networks. Considering that the energy consumption rate varies significantly among sensors, we need a better way to model the charging demand of each sensor, such that the sensors are able to be charged multiple times in one charging tour. Therefore, time window is used to represent charging demand. In order to allow the mobile charger to respond to these charging demands in time and transfer more energy to the sensors, we introduce a new metric: charging reward. This new metric enables us to measure the quality of sensor charging. And then, we study the problem of how to schedule the mobile charger to replenish the energy supply of sensors, such that the sum of charging rewards collected by mobile charger on its charging tour is maximized. The sum of the collected charging reward is subject to the energy capacity constraint on the mobile charger and the charging time windows of all sensor nodes. We first prove that this problem is NP-hard. Due to the complexity of the problem, then deep reinforcement learning technique is exploited to obtain the moving path for mobile charger. Finally, experimental simulations are conducted to evaluate the performance of the proposed charging algorithm, and the results show that the proposed scheme is very promising.
Keywords: wireless rechargeable sensor networks | time window | mobile charging | deep reinforcement learning technique
مقاله انگلیسی
6 On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction
بهبود طول عمر شبکه های حسگر بی سیم با خوشه بندی مبتنی بر فازی و کاهش داده های مبتنی بر یادگیری ماشین-2019
A useful approach to increase the lifetime of wireless sensor networks is clustering. Exchange of messages due to successive and recurrent reclustering burdens the sensor nodes and causes power loss. This paper presents a modified clustering methodology that diminishes the overhead in clustering and message exchanges thereby effectively scheduling the clustering task. The network is clustered subject to the remaining energy of sensor nodes. Energy based parameters decide cluster head nodes and ancillary nodes and the member nodes are linked with them. The roles of the head nodes of the cluster are interchanged depending on the nodes’ states. Reclustering of nodes is accomplished to achieve minimum energy consumption by calculating the update cycle using a fuzzy inference system. The average sensed data rate of cluster members, the distance at which the member nodes are from the sink and the power of cluster head nodes are counted to achieve better energy saving. Cluster member nodes apply machine learning at regular intervals to classify data based on their similarity. The classified data are transmitted to the cluster head after a reduction in the number of message transfers. The proposed method improves the energy usage of clustering and data transmission.
Keywords: Wireless sensor networks | Machine learning | Clustering | Network lifetime | Fuzzy | Energy efficiency
مقاله انگلیسی
7 جلوگیری از حملهBlack Hole در شبکه سنسور بی سیم با استفاده از HMM
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 18
مقاله ترجمه شده
8 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
9 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
10 الگوریتم زمانبندی گره مبتنی بر توری برای شبکه‎های حسگر بی‌سیم
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 13
نحوه‌ی کاهش مصرف انرژی شبکه و افزایش عمر مفید شبکه‌ی حسگر بی‌سیم یکی از مباحث پژوهشی مهم در حوزه‌ی شبکه‌ی حسگر بی‌سیم است. با پیش‌فرض اطمینان از پوشش شبکه، الگوریتم زمانبندی گره که گره‎های زائد ا به حالت خواب می‌برد، اقدام کارآمدی برای کاهش مصرف انرژی است. در این مقاله، یک سازوکار زمانبندی گره مبتنی بر توری پیشنهاد شده است. این سازوکار، وزن تمامی گره‌های موجود در هر توری را محاسبه نموده و سپس مشخص می‌سازد آیا گره جزو گره‌های با پوشش زائد است یا خیر. نتیجه‎‌ی شبیه‌سازی متلب نشان می‌دهد که این سازوکار می‌توان به خوبی زمان بقای شبکه را افزایش دهد. کلیدواژه ها: شبکه‌ی حسگر بی‌سیم | مش‌بندی | زمانبندی گره | الگوریتم زمانبندی
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