Distributed cooperative target detection and localization in decentralized wireless sensor networks

Shahbazian, Reza; Ghorashi, Seyed Ali

doi:10.1007/s11227-016-1877-6

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…Therefore, the location problem can be transformed into solving the minimum value problem of nonlinear equations, that is, solving the estimated coordinate (x, y) to minimize the value of f(x, y) in Eq. (2). Fitness function is used to evaluate the quality of particle position and guide the search direction of the algorithm.…”

Section: Description On Location Problemmentioning

confidence: 99%

“…Node location is a key technology in wireless sensor network, and its location accuracy is directly related to the overall performance of wireless sensor network (WSN) system [1,2]. At present, the researches of WSN location algorithm have achieved rich results, and many novel solutions and ideas are used to solve the localization problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Research on range-free location algorithm for wireless sensor network based on particle swarm optimization

Xue

2019

J Wireless Com Network

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Location technology is the key support technology of wireless sensor network (WSN). The hop number and hop distance information obtained by traditional distance vector hop (DV-Hop) location algorithm can only be acquired by solving the nonlinear equations, and the solution of the equation determines the accuracy of node location. Although the least squares method has better estimation performance, the solution results are sensitive to the average hop distance, which will lead to the large error in the solution of the equation. In order to solve the problem of location error caused by initial value sensitivity of least squares method in the coordinate calculation stage of unknown nodes and beacon nodes, a range-free location algorithm based on particle swarm optimization (PSO) is proposed in this paper. The proposed approach solves the problem of location error caused by initial value sensitivity of least squares method, obtains relatively accurate solution, and improves the accuracy of location algorithm. The experimental results show that the PSO algorithm has faster convergence speed and higher location accuracy than the non-optimization algorithm.

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Section: Description On Location Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on range-free location algorithm for wireless sensor network based on particle swarm optimization

Xue

2019

J Wireless Com Network

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“…Therefore, many positioning algorithms are introduced that normally benefit from ranging information such as the received signal strength (RSS), time of arrival (TOA) and angle of arrival (AOA) techniques. TOA is a ranging technique to extract the distance between a transmitter and receiver by measuring the time of transferred signals [14]. AOA is normally obtained by antenna arrays in which the timedifference of received signals in antennas is used to extract signal's angle information [3].…”

Section: Introductionmentioning

confidence: 99%

A novel smartphone application for indoor positioning of users based on machine learning

Homayounvala

Nabati

Shahbazian

et al. 2019

Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Smartphones are linked with individuals and are valuable and yet easily available sources for characterizing users' behavior and activities. User's location is among the characteristics of each individual that can be utilized in the provision of location-based services (LBs) in numerous scenarios such as remote health-care and interactive museums. Mobile phone tracking and positioning techniques approximate the position of a mobile phone and thereby its user, by disclosing the actual coordinate of a mobile phone. Considering the advances in positioning techniques, indoor positioning is still a challenging issue, because the coverage of satellite signals is limited in indoor environments. One of the promising solutions for indoor positioning is fingerprinting in which the signals of some known transmitters are measured in several reference points (RPs). This measured data, which is called dataset is stored and used to train a mathematical

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“…In multi-hop WSNs, the cost of propagating the reports of the event detection from the detecting nodes to the sink can be significant. Collaboration of neighboring nodes on deciding whether or not there is sufficient evidence exists to trigger the propagation of target detection notification would be of benefits regarding energy saving and accurate detection of the target as well as the detection delay [13], [14]. Accordingly, a classifier based distributed detection system was proposed by [15].…”

Section: Introductionmentioning

confidence: 99%

Multi Objects Detection and Tracking System for Smart Home using Wireless Sensor Network

Jadaa¹

2019

IJATCSE

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The current developed event detection and tracking systems have several limitations that make them not suitable or efficient to be utilized in smart homes. These limitations include dealing with direction changes and varying speeds of an evolving events, node fault tolerance, energy consumption, missing of target, tracking precision, dynamicity of the target, and prediction accuracy. The situation become more difficult in term of multi-targets. Efficient tracking after the detection of an event based on robust data control and management would definitely help in making reliable distributed control decisions, therefore a reliable system is highly needed. In this paper we proposed a system for monitoring and detecting of continuous moving objects according to certain requirements and specifications that are related to smart home concept. The system can be detecting and tracking multi objects in the monitoring area that are moving randomly with varying speed by using ordinary sensors. The proposed system able to collect and analysis the data during their development in time and space. In this paper have been used a dynamic sensing model for accurate observation and delectability of the event. The simulation results of the proposed system show high detection and tracking accuracy for detection and tracking multi continues moving objects. The proposed system shows high tracking accuracy in term of single reach to 97 % and multi-targets (up to 5) reach to more than 94 %.

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Distributed cooperative target detection and localization in decentralized wireless sensor networks

Cited by 13 publications

References 30 publications

Research on range-free location algorithm for wireless sensor network based on particle swarm optimization

Research on range-free location algorithm for wireless sensor network based on particle swarm optimization

A novel smartphone application for indoor positioning of users based on machine learning

Multi Objects Detection and Tracking System for Smart Home using Wireless Sensor Network

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