Ranging the Distance Between Wireless Sensor Nodes Using the Deviation Correction Method of Received Signal Strength

Wireless sensor network (WSN) is formed by a large number of low-cost sensors. In order to exchange information, sensor nodes communicate in an ad hoc manner. The acquired information is useful only when the location of sensors is known. To use GPS-aided devices in each sensor makes sensors more costly and energy hungry. Hence, finding the location of nodes in WSNs becomes a major issue. In this paper, the authors propose a combination of range based and range-free localization scheme. In their scheme, for finding the distance, they use received signal strength indication (RSSI), which is a range based center of gravity technique. For finding the location of non-anchor nodes, the authors assign weights to anchor and non-anchor nodes based on received signal strength. The weight, which is assigned to anchor and non-anchor nodes, are designed by fuzzy logic system (FLS).

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Characterization and Modeling of Received Signal Strength and Charging Time for Wireless Energy Transfer

Advances in Electrical Engineering ◽

10.1155/2015/621306 ◽

2015 ◽

Vol 2015 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Uthman Baroudi ◽

Amin-ud-din Qureshi ◽

Samir Mekid

Keyword(s):

Wireless Sensor Networks ◽

Energy Transfer ◽

Sensor Networks ◽

Signal Strength ◽

Sensor Nodes ◽

Wireless Sensor ◽

Wireless Energy Transfer ◽

Wireless Sensor Nodes ◽

Charging Time ◽

Indoor And Outdoor

Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.

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Calibration and Measurement of Signal Strength for Sensor Localization

Localization Algorithms and Strategies for Wireless Sensor Networks ◽

10.4018/978-1-60566-396-8.ch005 ◽

2009 ◽

pp. 122-145 ◽

Cited By ~ 6

Author(s):

Neal Patwari ◽

Piyush Agrawal

Keyword(s):

Parameter Estimation ◽

Path Loss ◽

Signal Strength ◽

Sensor Nodes ◽

Wireless Sensor ◽

Sensor Localization ◽

Wireless Sensor Nodes ◽

Present Measurement ◽

Localization Algorithms ◽

Negative Impacts

A number of practical issues are involved in the use of measured received signal strength (RSS) for purposes of localization. This chapter focuses on device effects and modeling problems which are not well covered in the literature, such as transceiver device manufacturing variations, battery effects on transmit power, nonlinearities in RSSI circuits, and path loss model parameter estimation. The authors discuss both the negative impacts of these effects and inaccuracies, and adaptations used by particular localization algorithms to be robust to them, without discussing any algorithm in detail. The authors present measurement methodologies to characterize these effects for wireless sensor nodes, and report the results from several calibration experiments to quantify each discussed effect and modeling issue.

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Research on Improved Multidimensional Scaling Localization Algorithm for Wireless Sensor Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.44-47.3932 ◽

2010 ◽

Vol 44-47 ◽

pp. 3932-3936

Author(s):

Liang Tao ◽

Shuai Xu ◽

Hai Yong Chen ◽

He Xu Xun

Keyword(s):

Multidimensional Scaling ◽

Search Algorithm ◽

Correction Method ◽

Sensor Nodes ◽

Wireless Sensor ◽

Localization Algorithm ◽

Wireless Sensor Nodes ◽

Anchor Nodes ◽

Hardware Configuration ◽

Positioning Algorithm

Wireless sensor networks, which are energy limited, low hardware configuration and proneness to invalidation, puts a high demand on the positioning algorithm. Therefore the improved multidimensional scaling (IMDS) algorithm is proposed. In IMDS, firstly, local positioning areas (LPA) are established by an adaptive search algorithm. So the centralized multidimensional scaling (MDS) algorithm is changed into a distributed one. Then the shortest path distances between nodes on LPA are corrected with the geometric correction method (GCM) and adjusting weight correction method (AWCM). The distances between nodes become more accurate. Finally, with information of the public nodes of LPA and anchor nodes, we get the wireless sensor nodes coordinates through coordinate transformation by the SMACOF algorithm and the classical MDS algorithm.

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