Description: A Survey of Spectrum Sensing Algorithms The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given. Platform: |
Size: 453502 |
Author:chenpeng3361 |
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Description: 压缩传感在无线传感器网络中的应用。通过压缩传感，可以有效压缩数据量-Compressed sensing in wireless sensor network applications. Through the compression sensor can effectively reduce the amount of data Platform: |
Size: 900096 |
Author:高艺 |
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Description: 在无线传感器网络中使用压缩传感技术，可以有效提高网络的生存周期，减少数据传输量-In wireless sensor networks using compressed sensing technology can effectively improve the network life cycle, reducing the volume of data transmission Platform: |
Size: 3096576 |
Author:高艺 |
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Description: TinyOS是UC Berkeley（加州大学伯克利分校）开发的开放源代码操作系统，专为嵌入式无线传感网络设计，操作系统基于构件（component-based）的架构使得快速的更新成为可能，而这又减小了受传感网络存储器限制的代码长度。
TinyOS目前的最新版本为TinyOS 2.1
-TinyOS is a UC Berkeley (University of California, Berkeley) development of the open-source operating system, designed for embedded wireless sensor network design, operating systems, component-based (component-based) architecture makes it possible to quickly update, which in turn reduced smaller by the sensing network storage limit code size. TinyOS is currently the latest version of TinyOS 2.1 Platform: |
Size: 819200 |
Author:hzzc1987 |
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Description: Abstract—Wireless networks in combination with image sensors
open up a multitude of previously unthinkable sensing
applications. Capable tools and testbeds for these wireless image
sensor networks can greatly accelerate development of complex,
yet efficient algorithms that meet application requirements. In
this paper, we introduce WiSNAP (Wireless Image Sensor
Network Application Platform), Platform: |
Size: 773120 |
Author:alaa |
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Description: Data aggregation is essential that exploits correlated sensing data and aggregates at intermediate nodes to reduce the number of messages and to improve the energy efficiency in wireless sensor networks. This project considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. We propose an ant colony algorithm for data aggregation in wireless sensor networks. Simulation results have shown that our algorithm can reduce significant energy costs.-Data aggregation is essential that exploits correlated sensing data and aggregates at intermediate nodes to reduce the number of messages and to improve the energy efficiency in wireless sensor networks. This project considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. We propose an ant colony algorithm for data aggregation in wireless sensor networks. Simulation results have shown that our algorithm can reduce significant energy costs. Platform: |
Size: 4031488 |
Author:nani |
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Description: A wireless sensor network may comprise thousands of sensor nodes. Each sensor node has a sensing
capability as well as limited energy supply, compute power, memory and communication ability. Besides military applications, wireless sensor networks may be used to monitor microclimates and wildlife habitats [56], the structural integrity of bridges and buildings, building security, location of valuable assets (via sensors placed on these valuable assets), traffic, and so on. However, realizing the full potential of wireless sensor networks poses myriad research challenges ranging from hardware and architectural issues, to programming languages and operating systems for sensor networks, to security concerns, to algorithms for sensor network deployment, operation and management. Iyengar and Brooks [26, 27] and Culler and Hong [12] provide good overviews of the breadth of sensor network research topics as well as of applications for sensor networks. Platform: |
Size: 205824 |
Author:ahmad |
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Description: Monitoring applications define an important
class of applications used in wireless sensor networks.
In these applications the network perceives
the environment and searches for event
occurrences (phenomena) by sensing different
physical properties, such as temperature, humidity,
pressure, ambient light, movement, and presence
(for target tracking). In such cases the
location information of both phenomena and
nodes is usually required for tracking and correlation
purposes. In this work we summarize most
of the concepts related to localization systems for
WSNs as well as how to localize the nodes in
these networks (which allows the localization of
phenomena). By dividing the localization systems
into three distinct components — distance/angle
estimation, position computation, and localization
algorithm — besides providing a didactic
viewpoint, we show that these components can
be seen as subareas of the localization problem
that need to be analyzed and studied separately. Platform: |
Size: 134144 |
Author:ginanjar |
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Description: The spectrum sensing problem has gained new
aspects with cognitive radio and opportunistic spectrum access
concepts. It is one of the most challenging issues in cognitive
radio systems. In this paper, a survey of spectrum sensing
methodologies for cognitive radio is presented. Various aspects
of spectrum sensing problem are studied from a cognitive radio
perspective and multi-dimensional spectrum sensing concept is
introduced. Challenges associated with spectrum sensing are
given and enabling spectrum sensing methods are reviewed.
The paper explains the cooperative sensing concept and its
various forms. External sensing algorithms and other alternative
sensing methods are discussed. Furthermore, statistical modeling
of network traffic and utilization of these models for prediction
of primary user behavior is studied. Finally, sensing features of
some current wireless standards are given. Platform: |
Size: 456704 |
Author:Deepesh |
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Description: 此压缩包包含了压缩感知与唔想传感器网络经典的文章十多篇，对研究压缩感知与无线传感网络的帮助很大-This package contains the compressed sensing A No sensor network with more than a dozen articles classic article, the study of compressed sensing and wireless sensor networks is very helpful Platform: |
Size: 7052288 |
Author:zhangli |
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Description: 一篇关于无线传感器网络中电源管理控制和优化策略高质量论文。-A Wireless Sensor Network (WSN) is a distributed wireless network consisting of low-cost,
battery-powered nodes that have sensing and wireless communication capabilities. Platform: |
Size: 7095296 |
Author:fsn |
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Description: A discription of wireless sensing network and its various application and how denial of service attack work Platform: |
Size: 88064 |
Author:sonu |
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Description: A discription of Sink hole attack and how to detect sink hole attack in wireless sensing network. Platform: |
Size: 76800 |
Author:sonu |
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Description: ecent advances in electrical engineering and computer science, esp ecially in micro-
electro-mechanical systems (MEMS), very-large-scale integration (VLSI) and op erating
system theory, have made wireless sensor network (WSN) technologies an imp ortant
and promising research area. As the circuitry b ecame smaller and cheap er, the idea
of co op eratively sensing physical or environmental conditions, such as magnetic eld
intensity, temp erature, light intensity, sound, vibration, pressure, motion or p ollutants
with the help of a network of sensor no des has b ecome feasible. Platform: |
Size: 7168 |
Author:ghadi |
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Description: Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Variety of sensing capabilities results in profusion of application areas. However, the characteristics of wireless sensor networks require more effective methods for data forwarding and processing.
In WSN, the sensor nodes have a limited transmission range, and their processing and storage capabilities as well as their energy resources are also limited. Routing protocols for wireless sensor networks are responsible for maintaining the routes in the network and have to ensure reliable multi-hop communication under these conditions. In this paper, we give a survey of routing protocols for Wireless Sensor Network and compare their strengths and limitations.
Platform: |
Size: 223232 |
Author:pardeep |
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Description: Wireless Sensor Network is the network of power-limited sensing devices called sensors. Wireless sensor network is differ other networks in terms of optimization of amount of energy because when these sensors sense and transmit data to other sensors present in the network, considerable amount of energy is dissipated. Various routing algorithms are proposed to limit the powers used by the wireless sensors. Hierarchical routing protocols with the concept of clustering like LEACH and and DEEC are already best known for maintaining energy efficiency. In this submission, we will compare these two protocols.-Wireless Sensor Network is the network of power-limited sensing devices called sensors. Wireless sensor network is differ other networks in terms of optimization of amount of energy because when these sensors sense and transmit data to other sensors present in the network, considerable amount of energy is dissipated. Various routing algorithms are proposed to limit the powers used by the wireless sensors. Hierarchical routing protocols with the concept of clustering like LEACH and and DEEC are already best known for maintaining energy efficiency. In this submission, we will compare these two protocols. Platform: |
Size: 11264 |
Author:SamSholi |
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Description: 2013年剑桥出版，《Compressive sensing for wireless networks》无线网络中应用压缩感知技术的重要资料-Issued by cambridge in 2013, a important reference book for the compressive sensing technology in wireless network Platform: |
Size: 4413440 |
Author:周宁 |
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Description: 采用加权网络中节点强度和权重都是幂率分布的模型，有小波分析的盲信号处理，基于互功率谱的时延估计，用MATLAB实现的压缩传感，仿真效果非常好，虚拟力的无线传感网络覆盖。-Using weighted model nodes in the network strength and weight are power law distribution, There Wavelet Analysis Blind Signal Processing, Based on the time delay estimation of power spectrum, Using MATLAB compressed sensing, Simulation of the effect is very good, Virtual power wireless sensor network coverage. Platform: |
Size: 9216 |
Author:ainxer |
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