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نتیجه جستجو - شبکه های حسگر بی سیم

تعداد مقالات یافته شده: 83
ردیف عنوان نوع
1 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
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 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
4 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
5 A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
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 مدلسازی ZigBee با صرفه جویی در مصرف انرژی (IEEE 802:15:4) شبکه محلی بدن مبتنی بر IOT در خانه های هوشمند
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 8
چکیده: رشد سریع شبکه های حسگر بی سیم و دستگاه های تعبیه شده و همچنین اجرای آنها در زمینه خانه های هوشمند ، فرصت های زیادی را برای توسعه راه حل های جدید به وجود می آورد. با این حال، پیدا کردن یک راه حل با کیفیت و با صرفه جویی در انرژی می تواند یک کار بسیار پیچیده باشد. در این مقاله، مدل پیشنهادی از یک شبکه انرژی ZigBee با کارایی انرژی، ارائه شده و از طریق شبیه سازی های مختلف برای بررسی و ارزیابی عملکرد آن در زمینه خانه های هوشمند مبتنی بر IoT با هدف ارائه راه حل های کیفی در حوزه های BAN و eHealth ارائه شده است .
مقاله ترجمه شده
8 Design of Optical and Wireless Sensors for Underground Mining Monitoring System
طراحی سنسورهای نوری و بی سیم برای سیستم معدن زیرزمینی-2018
Miner’health and safety is now considered as a major challenge, hence novel technologies such as optical sensors and wireless sensors networks (WSN) have been adopted to monitor underground mining. In this paper we propose a real time system for underground mining monitoring based on optical sensors and WSN. We treat the sensor deployment in underground tunnels and its impacts on underground mining monitoring system (UMMS). The dilemma of wireless and optical node deployment, efficient coverage and sensing in underground mining is detailed. Therefore, we are interested on the case of optical sensors use in UMMS. The introduced architecture may detect and localize damages such as strain vibration, temperature and humidity change. In this work, the suggested optical sensors (Raman, Brillouin, Fabery–Perot and Bragg) are used in UMMS
Keywords: component; UMMS; optical sensors; wireless sensors nteworks; undergound mining; deployment
مقاله انگلیسی
9 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
10 Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges
داده های دریایی بزرگ بی سیم: انتقال کارآمد،برنامه های مرتبط و چالش ها-2018
The vast volume of marine wireless sampling data and its continuously explosive growth herald the coming of the era of marine wireless big data. Two challenges imposed by these data are how to fast, reliably, and sustainably deliver them in extremely hostile marine environments and how to apply them after collection. In this article, we first propose an architecture of heterogeneous marine networks that flexibly exploits the existing underwater wireless techniques as a potential solution for fast data transmission. We then investigate the possibilities of and develop the schemes for energy-efficient and reliable undersea transmission without or with slight data rate reduction. After discussing the data transmission, we summarize the possible applications of the collected big data and particularly focus on the problems of applying these data in sea-surface object detection and marine object recognition. Open issues and challenges that need to be further explored regarding transmission and detection/recognition are also discussed in the article.
Keywords: Big Data,marine communication, marine engineering,object detection,object recognition,oceanographic techniques,wireless sensor networks
مقاله انگلیسی
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