Experimental analysis of RSSI for distance and position estimation

Daiya, Vinita; Ebenezer, Jemimah; Murty, S.A.V. Satya; Raj, Baldev

doi:10.1109/icrtit.2011.5972367

Cited by 32 publications

(15 citation statements)

References 2 publications

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“…Xu et al [32] proposed an RSSI-based differential correlation algorithm to correct the distance measurement results and found that the proposed algorithm can improve the absolute accuracy as well as the relative accuracy of the location. Daiya et al [33] performed the RSSI measurements between two sensor nodes at a varying distance for indoor and outdoor environment. They found that the estimation errors for approximate location of the sensor node are 5-10%.…”

Section: Related Workmentioning

confidence: 99%

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

Miao

Zhang

2018

Journal of Sensors

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The range measurement is the premise for location, and the precise range measurement is the assurance of accurate location. Hence, it is essential to know the accurate internode distance. It is noted that the path loss model plays an important role in improving the quality and reliability of ranging accuracy. Therefore, it is necessary to investigate the path loss model in actual propagation environment. Through the analysis of experiments performed at the wheat field, we find that the best fitted parametric exponential decay model (OFPEDM) can achieve a higher distance estimation accuracy and adaptability to environment variations in comparison to the traditional path loss models. Based on the proposed OFPEDM, we perform the RSSI-based location experiments in wheat field. Through simulating the location characteristics in MATLAB, we find that for all the unknown nodes, the location errors range from 0.0004 m to 5.1739 m. The location error in this RSSI-based location algorithm is acceptable in the wide areas such as wheat field. The findings in this research may provide reference for location estimation in large-scale farmland.

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Section: Related Workmentioning

confidence: 99%

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

Miao

Zhang

2018

Journal of Sensors

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“…Moreover, we also assume that each node knows the distance to each of its neighboring nodes. This can be achieved through various distance estimation algorithms such as TOA (Time of Arrival) [36], TDOA (Time Difference of Arrival) [37], RSSI (Received Signal Strength Indicator) [38].…”

Section: A Assumptionmentioning

confidence: 99%

STETS: A novel energy-efficient time synchronization scheme based on embedded networking devices

Qiu

Lin

Guo

et al. 2015

Microprocessors and Microsystems

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“…According to formulas (6) and (7), we can reach the following conclusion: the geometric error in each plane is influenced by the projected area of shape constructed by the unit vectors from the blind node to anchor nodes. The bigger International Journal of Distributed Sensor Networks the projective area is, the smaller the geometric error is; on the contrary, the geometric error is bigger.…”

Section: Experiments For Laicmentioning

confidence: 99%

“…So far, there have been many WSN location algorithms, among which location algorithms based on RSSI [6,7], time of arrival (TOA), angle of arrival (AOA), and distance vectorhop (DV-hop) [8,9] are most popular. RSSI is a measurement of the power present in a received radio signal, which would decrease with the increase of distance between the sender and the receiver.…”

Section: Introductionmentioning

confidence: 99%

Location Algorithm for Nodes of Ship-Borne Wireless Sensor Networks

Yang

Zhang

2013

International Journal of Distributed Sensor Networks

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Node location is of great significance as a supporting technology of wireless sensor network (WSN). The information without position would be greatly devalued. So this paper is about location algorithm for nodes of ship-borne WSN based on research on structure of ship and work environment of ship-borne WSN. The whole location process consists of two steps: one is location algorithm among cabins (LAAC), and the other one is location algorithm in the cabin (LAIC). LAAC refers to location with the topology of ship-borne WSN. We can learn which cabin the node lies in. LAIC refers to location based on received signal strength indication (RSSI), we can get distance relationship between nodes by RSSI, and then obtain the specific location by solving this distance relationship. In the last part, this paper verifies the designed location algorithm by experimenting on "A" ship. Experiments show that the location algorithm designed by this paper is feasible.

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Experimental analysis of RSSI for distance and position estimation

Cited by 32 publications

References 2 publications

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

The Accurate Location Estimation of Sensor Node Using Received Signal Strength Measurements in Large-Scale Farmland

STETS: A novel energy-efficient time synchronization scheme based on embedded networking devices

Location Algorithm for Nodes of Ship-Borne Wireless Sensor Networks

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