RSSI based Improved Weighted Centroid Localization Algorithm in WSN

Sinha, Somnath; Ashwini, S R

doi:10.1109/incet51464.2021.9456134

Cited by 21 publications

(6 citation statements)

References 10 publications

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“…Furthermore, we enhance our proposed improved MM algorithm by integrating the WCLA to increase localization accuracy. As described in [35], [41], [42] and [43], WCLA uses a different approach for estimating location than trilateration and MM. WCLA relies significantly on the RSSI-to-distance conversion to calculate the weight used to adjust all AP coordinates' centroid.…”

Section: B Distance-based Techniques: MM and Wclamentioning

confidence: 99%

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

Adiyatma,

Suroso,

Cherntanomwong

2023

IEEE Access

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Internet of Things (IoT)-based Indoor localization is the most commonly used system to determine target locations indoors. It applies to various purposes, e.g., indoor navigation, asset tracking in warehouse management, and tracking people in hospitals. Distance-based techniques using the Received Signal Strength Indicator (RSSI), e.g., Min-Max, are widely applied because they can be directly implemented without prerequisite work such as site surveys. However, a challenging indoor environment with high numbers of interiors and people can obstruct signal propagation. This obstruction can reduce the accuracy of translating RSSI to distance using the path loss model, which will degrade the localization accuracy. In this paper, we introduce two improved Min-Max (MM) algorithms, i.e., Three Layer Bounding Box Min-Max (TLB-MM) and Weighted Centroid TLB-MM (WC-TLB-MM), to alleviate the issue and achieve higher localization accuracy. The novelty of the proposed TLB-MM is incorporating RSSI error functions to generate three-layer bounding boxes: the inner, middle, and outer in the Min-Max algorithm. Meanwhile, WC-TLB-MM enhanced the TLB-MM algorithm by integrating the Weighted Centroid Localization Algorithm (WCLA) in the calculation process. We validate our proposal by conducting various experiments using Wi-Fi at 2.4 GHz deployed in a laboratory room of 10.17 m × 9.12 m. Experimental results demonstrate that TLB-MM improved the accuracy performance to 55.78% and 30.86%, while WC-TLB-MM gave 40.93% and 7.65% compared to Min-Max and WCLA, respectively. From these results, our proposed methods are proven simple yet applicable to RSSI-based indoor localization systems.

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Section: B Distance-based Techniques: MM and Wclamentioning

confidence: 99%

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

Adiyatma,

Suroso,

Cherntanomwong

2023

IEEE Access

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“…Singh [26] discussed the challenges and potential solutions related to machine learning-based indoor localization systems. Literature [27] proposed a new method for evaluating WSN technology for indoor localization using weight range localizer (WRL) and relative span exponential weight range localizer (RS-WRL) [28,29] based on the RSSI to estimate the target node's position. This algorithm effectively improves positioning accuracy and reduces errors compared to the RSSI algorithm with a fixed loss factor.…”

Section: Introductionmentioning

confidence: 99%

Research on WSN Reliable Ranging and Positioning Algorithm for Forest Environment

Wu,

Yu,

et al. 2023

Preprint

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Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

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“…Their research used WSN to cluster sensors and actuators and collect data in a central location [17]. In terms of communication quality in WSN, it is introduced Received Signal Strength Indicator (RSSI) as a parameter for the localisation-positioning of sensor nodes and deciding the direction of the node to send the data [18], [19], [20]. From the above research, the RSSI value is not an indicator to decide transmission power which is usually defaulted by the processor module.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Energy Consumption in Sensor Node Based on Received Signal Strength Indicator (Rssi) and Sleep Awake Method

Rudati,

Feriyonika,

Sudarsa

et al. 2023

AEJ

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The transmission power management in Wireless Sensor Networks (WSN) is a critical problem. This research investigated optimizing power consumption based on transmission power (Tx Power) level according to RSSI and periodic transmission time. We investigated the RSSI value by varying Tx Power Level to get the optimum Tx Power Level. We found the optimum periodic transmission time by transmitting the data with various transmission times. By varying the Tx Power Level, we found the optimum Tx Power Level, resulting in the power consumption decreasing by about 42% and the power supply’s lifetime increasing by about 71% in the 280 m distance between the sensor node and gateway, with a 108 Wh power supply. By varying the periodic transmission time, we found that the optimum periodic transmission time is 8 seconds. Combining the optimum Tx Power Level and periodic transmission time, we found that the power supply’s lifetime is 40 times longer. This result is helpful for WSN applications in remote areas.

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RSSI based Improved Weighted Centroid Localization Algorithm in WSN

Cited by 21 publications

References 10 publications

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

TLB & WC-TLB-MM: The Improved Min-Max Algorithms for Multi Targets Indoor Localization

Research on WSN Reliable Ranging and Positioning Algorithm for Forest Environment

Optimization of Energy Consumption in Sensor Node Based on Received Signal Strength Indicator (Rssi) and Sleep Awake Method

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