Distribution of WLAN Received Signal Strength Indication for Indoor Location Determination

2006 ◽  
Author(s):  
K. Kaemarungsi
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Indoor Location ◽  
Location Determination ◽  
Received Signal Strength Indication

Improved Algorithm on Wide Range Positioning for Partial Discharge in Substation

2013 ◽  
Vol 860-863 ◽  
pp. 2177-2181
Author(s):  
Xi Ran Wang ◽  
Huai Dong Liu ◽  
Yi Fan He ◽  
Qi Ming Zhao ◽  
He Wu
Keyword(s):  
Electrical Discharge ◽  
Partial Discharge ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Experimental Result ◽  
Wide Range ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
Improved Algorithm

This paper proposes a Improved positioning algorithm of electrical partial discharge applied for substations. This algorithm is based on received signal strength indication, and taken practical condition of sensors into consideration by replenishing beacon nodes. Compared with traditional trilateral weighting positioning algorithm, this paper introduces indefinite amount of localization perpendicular lines and combined them with trilateral districts to calculate the weighting result, which can reduce error. This model meets the requirement of reality that the height of electrical discharge spots differentiate from the height of the plane formed by beacon nodes (signal sensors). The experimental result indicates that the revised position model proposed by this paper can effectively fit the condition of monitoring hardware. Error of this algorithm is less than that of traditional trilateral localization.

Localization in Wireless Sensor Networks Using Soft Computing Approach

2017 ◽  
Vol 11 (3) ◽  
pp. 42-53 ◽  
Author(s):  
Sunil Kumar Singh ◽  
Prabhat Kumar ◽  
Jyoti Prakash Singh
Keyword(s):  
Ad Hoc ◽  
Low Cost ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Scheme ◽  
Anchor Nodes ◽  
Received Signal Strength Indication ◽  
Logic System

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).

scholarly journals Research on Wi-Fi indoor positioning in a smart exhibition hall based on received signal strength indication

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Qing Yang ◽  
Shijue Zheng ◽  
Ming Liu ◽  
Yawen Zhang
Keyword(s):  
Nearest Neighbor ◽  
Science And Technology ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Experimental Simulation ◽  
K Nearest Neighbor ◽  
Improvement Strategy ◽  
Fingerprint Algorithm ◽  
Received Signal Strength Indication

AbstractTo improve the management of science and technology museums, this paper conducts an in-depth study on Wi-Fi (wireless fidelity) indoor positioning based on mobile terminals and applies this technology to the indoor positioning of a science and technology museum. The location fingerprint algorithm is used to study the offline acquisition and online positioning stages. The positioning flow of the location fingerprint algorithm is discussed, and the improvement of the location fingerprint algorithm is emphasized. The raw data of the RSSI (received signal strength indication) is preprocessed, which makes the location fingerprint data more effective and reliable, thus improving the positioning accuracy. Three different improvement strategies are proposed for the nearest neighbor classification algorithm: a balanced joint metric based on distance weighting and a compromise between the two. Then, in the experimental simulation, the positioning results and errors of the traditional KNN (k-nearest neighbor) algorithm and three improvement strategy algorithms are analyzed separately, and the effectiveness of the three improved strategy algorithms is verified by experiments.

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