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Received signal strength indicator (RSSI) gives a coarse initial measure of the inter-node distance at a low cost without the need for additional equipment or complexity. This necessitates the need for a mechanism to obtain accurate node locations from the noisy RSSI distance estimates. In this paper, an iterative nonlinear manifold learning technique, incremental locally linear embedding (ILLE),...
When localizing the position of an unknown node for wireless sensor networks, the received signal strength indicator (RSSI) value is usually considered to fit a fixed attenuation model with a corresponding communication distance. However, due to some negative factors, the relationship is not valid in the actual localization environment, which leads to a considerable localization error. Therefore,...
Wireless Sensor Networks (WSNs) are most growing research area because of its low cost, infrastructure less, increase capabilities of nodes, real time and accurate. Localization is a major issue in the wireless sensor networks because it has a number of sensor nodes which are deployed at positions and they may not be fixed at their own position. In localization different techniques are used for distance...
In wireless sensor networks, nodes can be static or mobile, depending on the application requirements. Dealing with mobility can pose some formidable challenges in protocol design, particularly, at the link and network layers. These difficulties require mobility adaption algorithms to efficiently localize mobile nodes and predict the quality of link that can be established with these nodes. An off...
Numerous localization protocols in Wireless Sensor Networks are based on Received Signal Strength Indicator. Because absolute positioning is not always available, localization based on RSSI is popular. More, no extra hardware is needed unlike solutions based on infra-red or ultrasonic. Moreover, the theory gives a RSSI as a function of distance. However, using RSSI as a distance metric involves errors...
The development of the large scale sensor networks needs the real time localization problem to be solved with few costs. Some methods that were developed for the time synchronization such as Time of Flight measure or signal level measure such as RSSI, can be used to localize sensor nodes in the network. These methods give different results depending on the estimation processes used for computing the...
Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization algorithms, which increase the cost and greatly limit the range of location-based applications. In this paper we present a method which can effectively...
A lot of research has been done in finding range-based methods such as computing RSSI, time of arrival, angle of arrival or time difference of arrival and range-free methods such as centroid computation, DV-hop and approximate position in triangle, for localization in wireless sensor networks. Range-based methods estimate location more precisely than range-free methods. However, range-free methods...
For the problems of traditional RSSI localization inaccurate and modeling difficult in WSN, this paper puts forward a support vector regression (SVR) learning algorithm based on RSSI and LQI. By training the samples with RSSI and LQI values as input while coordinates as output, we get the localization model. It differs from other RF-based algorithm in that it can estimate node locations directly according...
Node Localization plays an important role in the applications of wireless sensor networks (WSNs). The whole localization mechanism of WSN target nodes consists of three key steps, the identification and data exchange step, the range estimation step, and the position estimation step. Based on the RSSI (Received Signal Strength Indication) technology, the BP (Back Propagation)-loss channel model is...
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