scholarly journals Performance Analysis of Different Types of Sensor Networks for Cognitive Radios

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Chunxuan Ye ◽  
Zinan Lin ◽  
Alpaslan Demir ◽  
Yan Li
Keyword(s):  
Sensor Networks ◽  
Performance Analysis ◽  
Data Fusion ◽  
Sensor Network ◽  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Fusion Center ◽  
Multiple Sensors ◽  
Different Types ◽  
Information Combining

We consider the problem of using multiple sensors to detect whether a certain spectrum is occupied or not. Each sensor sends its spectrum sensing result to a data fusion center, which combines all the results for an overall decision. With the existence of wireless fading on the channels from sensors to data fusion center, we examine three different mechanisms on the transmissions from sensors to data fusion center: (1) direct transmissions; (2) transmissions with the assistance of relays and (3) transmissions with the assistance of an intermediate fusion helper which fuses the sensing results from the sensors and sends the fused result to the data fusion center. For each mechanism, we analyze the correct probability of the overall decision made by the data fusion center. Our evaluation establishes that a sensor network with an intermediate fusion helper performs almost as good as a sensor network with relays, but providing energy and spectral advantages. Both networks significantly outperform the sensor network without relay or intermediate fusion helper. Such analysis facilitates the design of sensor networks. Furthermore, we generalize this evaluation to sensor networks with an arbitrary number of sensors and to sensor networks applying various information combining rules. Our simulations validate the analytical results.

System Design and Data Fusion in Body Sensor Networks

2012 ◽  
pp. 1-25 ◽  
Author(s):  
Kai Lin ◽  
Min Chen ◽  
Joel J. P. C. Rodrigues ◽  
Hongwei Ge
Keyword(s):  
Sensor Networks ◽  
Data Fusion ◽  
System Design ◽  
Sensor Network ◽  
Evaluation Method ◽  
Data Evaluation ◽  
Body Sensor Networks ◽  
Sensory Data ◽  
Redundant Data ◽  
Healthcare Monitoring

Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.

Performance Analysis of Multi-Hop Routing Protocol With Optimized Grid-Based Clustering for Wireless Sensor Network

2020 ◽  
pp. 254-282
Author(s):  
Saloni Dhiman ◽  
Deepti Kakkar ◽  
Gurjot Kaur
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Sensor Network ◽  
Routing Protocol ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grid Based

Wireless sensor networks (WSNs) consist of several sensor nodes (SNs) that are powered by battery, so their lifetime is limited, which ultimately affects the lifespan and hence performance of the overall networks. Till now many techniques have been developed to solve this problem of WSN. Clustering is among the effective technique used for increasing the network lifespan. In this chapter, analysis of multi-hop routing protocol based on grid clustering with different selection criteria is presented. For analysis, the network is divided into equal-sized grids where each grid corresponds to a cluster and is assigned with a grid head (GH) responsible for collecting data from each SN belonging to respective grid and transferring it to the base station (BS) using multi-hop routing. The performance of the network has been analyzed for different position of BS, different number of grids, and different number of SNs.

scholarly journals Pseudo-Random Encryption for Security Data Transmission in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2452 ◽  
Author(s):  
Liang Liu ◽  
Wen Chen ◽  
Tao Li ◽  
Yuling Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Transmission ◽  
Multiple Scales ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Fusion Center ◽  
Inverse Mapping ◽  
The Matrix

The security of wireless sensor networks (WSN) has become a great challenge due to the transmission of sensor data through an open and wireless network with limited resources. In the paper, we discussed a lightweight security scheme to protect the confidentiality of data transmission between sensors and an ally fusion center (AFC) over insecure links. For the typical security problem of WSN’s binary hypothesis testing of a target’s state, sensors were divided into flipping and non-flipping groups according to the outputs of a pseudo-random function which was held by sensors and the AFC. Then in order to prevent an enemy fusion center (EFC) from eavesdropping, the binary outputs from the flipping group were intentionally flipped to hinder the EFC’s data fusion. Accordingly, the AFC performed inverse flipping to recover the flipped data before data fusion. We extended the scheme to a more common scenario with multiple scales of sensor quantification and candidate states. The underlying idea was that the sensor measurements were randomly mapped to other quantification scales using a mapping matrix, which ensured that as long as the EFC was not aware of the matrix, it could not distract any useful information from the captured data, while the AFC could appropriately perform data fusion based on the inverse mapping of the sensor outputs.

scholarly journals Performance Analysis Of Cognitive Radio Networks With Diversity And Faded Channels

2021 ◽  
Author(s):  
Sattar J. Hussain
Keyword(s):  
Cognitive Radio ◽  
Performance Analysis ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Multipath Fading ◽  
Radio Networks ◽  
Fusion Center ◽  
Detection Accuracy ◽  
Amplify And Forward ◽  
End To End

This dissertation presents new approaches for cognitive radio networks that combat fading effects and improve detection accuracy. We propose an advance framework for performance analysis of cooperative spectrum sensing over non-identical Nakagami- A detect-amplify-and-forward strategy is proposed to mitigate bandwidth requirements of relaying local observations to a fusion center. The end-to-end performance of a relay-based cooperative spectrum sensing over independent identically distributed Rayleigh fading channels is also investigated in this dissertation. Specifically, we aim to incorporate sensing time, end-to-end SNR, and end-to-end channel statistic into the performance analysis of cooperative CR networks. We also propose a cluster-based cooperative spectrum sensing approach to overcome the bandwidth limitations of the reporting links. The approach reduces the number of reporting terminals to a minimal reporting set and replaces the global fusion center by a local fusion center to mitigate the destructive channel conditions of global relaying channels. A new approach is proposed to select the location of the local fusion center using the general center scheme in graph theory. We aim to show that multipath fading on relaying channels yields similar performance degradations as multipath fading on sensing channels. With the detect-amplify-and forward strategy, refraining the heavily faded relays improves the detection accuracy. A gain of 3 dB is achieved by switching from amplify-and-forward strategy to detect-amplify-and-forward strategy with 3 cooperative users. Compared to the non-cooperative spectrum sensing, a gain of up to 8 dB is achieved with 4 cooperative users and equal gain combining receiver. Similar experimental set up but with selection combining receiver, a gain of 5 dB is achieved.

scholarly journals Data Fusion through Fuzzy-Bayesian Networks for Belief Generation in Cognitive Agents

2019 ◽  
Vol 26 (2) ◽  
pp. 69-80
Author(s):  
Munyque Mittelmann ◽  
Jerusa Marchi ◽  
Aldo Von Wangenheim
Keyword(s):  
Data Fusion ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Physical World ◽  
Autonomous Agent ◽  
Environmental Aspects ◽  
Sensory Data ◽  
Multiple Sensors ◽  
Physical Sensors ◽  
Different Types

Situation Awareness provides a theory for agents decision making to allow perception and comprehension of his environment. However, the transformation of the sensory stimulus in beliefs to favor the BDI reasoning cycle is still an unexplored subject. Autonomous agent projects often require the use of multiple sensors to capture environmental aspects. The natural variability of the physical world and the imprecision contained in linguistic concepts used by humans mean that sensory data contain different types of uncertainty in their measurements. Thus, to obtain the Situational Awareness for Agents with physical sensors, it is necessary to define a data fusion process to perform uncertainty treatment. This paper presents a model to beliefs generation using fuzzy-bayesian inference. An example in robotics navigation and location is used to illustrate the proposal.

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