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    Wyświetlanie 1-4 z 4
    Tytuł:
    Node Localization based on Anchor Placement using Fuzzy C-Means in a Wireless Sensor Network
    Autorzy:
    Irid, Sidi Mohammed Hadj
    Hadjila, Mourad
    Hachemi, Mohammed Hicham
    Souiki, Sihem
    Mosteghanemi, Reda
    Mostefai, Chaima
    Powiązania:
    https://bibliotekanauki.pl/articles/2200723.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    WSN
    localization algorithm
    anchors
    GM-SDP-2
    WLS
    CRLB
    Fuzzy C-Means
    RMSE
    CDF
    Opis:
    Localization is one of the oldest mathematical and technical problems that have been at the forefront of research and development for decades. In a wireless sensor network (WSN), nodes are not able to recognize their position. To solve this problem, studies have been done on algorithms to achieve accurate estimation of nodes in WSNs. In this paper, we present an improvement of a localization algorithm namely Gaussian mixture semi-definite programming (GM-SDP-2). GMSDP is based on the received signal strength (RSS) to achieve a maximum likelihood location estimator. The improvement lies in the placement of anchors through the Fuzzy C-Means clustering method where the cluster centers represent the anchors’ positions. The simulation of the algorithm is done in Matlab and is based on two evaluation metrics, namely normalized root-mean-squared error (RMSE) and cumulative distribution function (CDF). Simulation results show that our improved algorithm achieves better performance compared to those using a predetermined placement of anchors.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 1; 99--104
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wireless Sensor Node Localization based on LNSM and Hybrid TLBO : Unilateral technique for Outdoor Location
    Autorzy:
    Kaundal, V.
    Sharma, P.
    Prateek, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/226010.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    log normal shadowing model (LNSM)
    teacher learning based optimization (TLBO)
    trilateration
    unilateral
    RSSI
    ZigBee
    wireless sensor network
    Opis:
    The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm) - Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid-TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.
    Źródło:
    International Journal of Electronics and Telecommunications; 2017, 63, 4; 389-397
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Localization technique of IoT Nodes Using Artificial Neural Networks (ANN)
    Autorzy:
    Krupanek, Beata
    Bogacz, Ryszard
    Powiązania:
    https://bibliotekanauki.pl/articles/1844456.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wireless networks
    node localization
    location errors
    WSN
    IoT
    neural networks
    Opis:
    One of the ways to improve calculations related to determining the position of a node in the IoT measurement system is to use artificial neural networks (ANN) to calculate coordinates. The method described in the article is based on the measurement of the RSSI (Received Signal Strength Indicator), which value is then processed by the neural network. Hence, the proposed system works in two stages. In the first stage, RSSI coefficient samples are taken, and then the node location is determined on an ongoing basis. Coordinates anchor nodes (i.e. sensors with fixed and previously known positions) and the matrix of RSSI coefficients are used in the learning process of the neural network. Then the RSSI matrix determined for the system in which the nodes with unknown positions are located is fed into the neural network inputs. The result of the work is a system and algorithm that allows determining the location of the object without processing data separately in nodes with low computational performance.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 4; 769-774
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Overcoming Localization Errors due to Node Power Drooping in a Wireless Sensor Network
    Autorzy:
    Khan, S. A.
    Daachi, B.
    Djouani, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/226280.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wireless sensor networks
    RSSI
    localization
    range-free scheme
    energy considerations
    neural networks
    Opis:
    Received Signal Strength Indication (RSSI) plays a vital role in the range-free localization of sensor nodes in a wireless sensor network and a good amount of research has been made in this regard. One important factor is the battery voltage of the nodes (i.e., the MICAz sensors) which is not taken into account in the existing literature. As battery voltage level performs an indispensable role for the position estimation of sensor nodes through anchor nodes therefore, in this paper, we take into a account this crucial factor and propose an algorithm that overcomes the problem of decaying battery. We show the results, in terms of more precise localization of sensor nodes through simulation. This work is an extension to [1] and now we also use neural network to overcome the localization errors generated due to gradual battery voltage drooping.
    Źródło:
    International Journal of Electronics and Telecommunications; 2011, 57, 3; 341-346
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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