Robust node position estimation algorithms for wireless sensor networks based on improved adaptive Kalman filters

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

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Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽

10.3390/s19194179 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4179 ◽

Cited By ~ 1

Author(s):

Stelian Dolha ◽

Paul Negirla ◽

Florin Alexa ◽

Ioan Silea

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Signal Strength ◽

Data Access ◽

Received Signal Strength ◽

Position Estimation ◽

Wireless Sensor ◽

Node Localization ◽

Received Signal Strength Indicator ◽

Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

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A Descend-Based Evolutionary Approach to Enhance Position Estimation in Wireless Sensor Networks

2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06) ◽

10.1109/ictai.2006.9 ◽

2006 ◽

Cited By ~ 6

Author(s):

Vincent Tam ◽

King-yip Cheng ◽

King-shan Lui

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Position Estimation ◽

Wireless Sensor ◽

Evolutionary Approach

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