Energy-Efficient RSSI-Based Localization for Wireless Sensor Networks

Yaghoubi, Forough; Abbasfar, Aliazam; Maham, Behrouz

doi:10.1109/lcomm.2014.2320939

Cited by 68 publications

(24 citation statements)

References 10 publications

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“…We already have literatures for sensor networks focused on the received signal strength, on which we focus in this paper [15,16]. In [15], the energy efficient localization scheme using the received signal strength for wireless sensor networks was proposed.…”

Section: Related Workmentioning

confidence: 99%

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Energy-efficient information dissemination based on received signal strength in wireless sensor networks

Hisamatsu

Hasegawa

Murata

2015

2015 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR)

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Power-saving is one of the important issues in wireless sensor networks and many studies on power-saving in wireless sensor networks have been done. However, most of these studies have focused on saving power in gathering the information. In this paper, we tackle the issue of the energy-efficient information dissemination in wireless sensor networks. We propose method of disseminating information while optimizing electric power consumption in wireless sensor networks. Our new dissemination method employs flooding and uses the receiver signal strength to determine the broadcast timing. In our method, the node farthest from the sending node will rebroadcast the message first and a broadcast is canceled when a node scheduled to broadcast receives a duplicate of the scheduled message from other nodes. We evaluate our proposed method by simulation. As a result, it is found that the electric power consumption of the proposal method in the entire network is one-third that of the flooding method at the maximum. Furthermore, we show that the farther the transmitting distance of the wireless radio wave becomes, the more efficiently our method can disseminate information to the network.

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Section: Related Workmentioning

confidence: 99%

“…In [15], the energy efficient localization scheme using the received signal strength for wireless sensor networks was proposed. By simulation, the authors showed the proposed scheme achieved both energy efficiency and localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

Energy-efficient information dissemination based on received signal strength in wireless sensor networks

Hisamatsu

Hasegawa

Murata

2015

2015 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR)

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“…Among them, algorithm based on signal with Time-Of-Arrival (TOA) [2] , algorithm based on different signal with Time-Difference-Of-Arrival (TDOA) [3] , algorithm based on signal with Angle-of-Arrival(AOA) [4] and algorithm based on signal with Received-Signal-Strength-Indication(RSSI) [5] , they belong to Range-Based localization algorithm. Centroid algorithm for solving polygon geometric center of gravity based on neighbor nodes [7] , algorithm based on nodes with Distance-Vector-Hop (DV-Hop) [4], Multi-Dimensional Scaling Map( MDS-MAP) algorithm [7] , these algorithms are fixed node localization algorithms and they belong to Rang-Free localization algorithm. The mobile node localization algorithm for WSN 459 was first proposed Monte Carlo location algorithm (MCL) by American Hu and Evans in 2004.…”

Section: Introductionmentioning

confidence: 99%

“…The mobile node localization algorithm for WSN 459 was first proposed Monte Carlo location algorithm (MCL) by American Hu and Evans in 2004. As workshop resources have extensive mobile characteristics, the paper has proposed a workshop mobile node localization algorithm (R-MCL) based on RSSI modified MCL according to reference [4], which is based on the RSSI and MCL localization algorithms, there are many problems, such as large computation, long computing time, large energy loss of nodes, and no motion prediction. In fact, the trajectory of workshop manufacturing resources is generally regular, i.e., the motion parameters cannot be changed commonly, so we can make use of the mobile node several time points of data, predict the trajectories of the mobile node at the present time, reduce the range of sampling algorithm, improve the sampling efficiency and the accuracy, so as to improve the positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

Indoor Mobile Node Localization Algorithm based on RSSI and Improved MCL

Liu¹,

Fang²

2018

dtcse

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With the characteristics of mobile sensor network for dynamic change of indoor network structure and node position, in order to overcome the shortage of localization accuracy and sampling efficiency of Monte Carlo localization algorithm in wireless sensor networks (WSN), a location algorithm based on RSSI and improved Monte Carlo localization is proposed to locate indoor mobile nodes. First, the paper describes the classical MCL algorithm and the received signal intensity RSSI model, and then an improved MCL algorithm is designed. The algorithm through introducing the received signal strength indicator model of range prediction, using distance information filtering samples, and finally using the filtered samples of the weighted average to estimate the location of nodes and reducing the sampling area, improves the sampling efficiency and positioning accuracy. The simulation results show that the improved MCL algorithm based on RSSI improves the convergence speed compared with the traditional algorithm, and uses the distance information to filter samples to reduce the computational overhead and improve the positioning accuracy. Under the same conditions, the improved MCL algorithm based on RSSI reduces the positioning error by about 45% compared with the traditional Monte Carlo algorithm.

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“…In fact, the transmit power will be subject to a large fluctuation because its value is dependent on the height and orientation of the node antenna, as well as antenna gain and its battery which will decrease with time. In [14] linear least squares is utilized to determine the location of the source node when path loss model parameters are unknown. The performance shows that the presented method outperforms other off-the-shelf source node localization algorithms when path-loss model parameters are unknown.…”

Section: Introductionmentioning

confidence: 99%