Neural network based mobile phone localization using Bluetooth connectivity

Li, Shuai; Liu, Bo; Chen, Baogang; Lou, Yunjiang

doi:10.1007/s00521-012-0950-1

Cited by 30 publications

(16 citation statements)

References 11 publications

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“…There exists a dilemma between the relatively high price of GPS devices and the dependence of location information acquisition on GPS for most phones in current stage. Li et al [19] formulate the problem as an optimization problem defined on the Bluetooth network. The solution to this optimization problem is not unique.…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Nandi

Dewanji

Roy

et al. 2014

International Journal of Distributed Sensor Networks

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Sensor networks aim at monitoring their surroundings for event detection and object tracking. But, due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN). In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon. We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs, and the other one for the adjacent nodes. This second type of detection probability is new in sensor network literature. We develop schemes under the model selection procedure and multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.

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Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Nandi

Dewanji

Roy

et al. 2014

International Journal of Distributed Sensor Networks

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“…Achieving position of a sensor node in a WSN has been an active research area [5][6][7]. Localization here is usually based on less in quantity anchors, whose positions are accurately in advance known to calculate or estimate the locations of the target nodes through the measuring distance according to time of arrival (TOA) [5], time difference of arrival (TDOA) [8] or received signal strength (RSS) [9].…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

Zhang

Xiong

Bao-guo

2017

Neural Process Lett

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Ranging error is known to degrade significantly the target node localization accuracy. This paper investigates the use of computationally efficient positioning solution of least square (LS) in closed-form, to reduce localization accuracy loss caused by ranging error. For range-based node localization, the LS solution based on least square criterion has been confirmed to exhibit capability of optimum estimation but extensively achieve at a very complex calculation. In this paper we consider the problem how to acquire such LS solution provided with estimation performance at low complex calculation. In this paper, we use the Gauss noise model and use the weighted least squares criterion and the effective calculation method to solve the linearized equation derived from the RSS measurement, and put forward a new approach to estimate the performance of the target node location estimation. Based on the Fisher information matrix, the Cramér-Rao lower bound of target position estimation is derived based on received signal strength. We obviously indicate that the proposed algorithm can approximately achieve the LS solution in estimation performance at a markedly low complex calculation. Simulations are performed to show the improvement of the proposed algorithm.

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“…To mitigate this challenge, a distributed algorithm is required for parallel programming, which may lead to consensus problems, that is, the inability of the various processes to agree and communicate with one another on a single data value. A number of techniques with application to localization in a distributed, routing-free, and range-free wireless sensor networks have been proposed to solve this problem [19][20][21]. The topology of a neural network can be recurrent or with feedback contained in the network from the output back to the input and feed-forward where the data flows from the input to the output units with no feedback connections.…”

Section: Introductionmentioning

confidence: 99%

Radial and Sigmoid Basis Function Neural Networks in Wireless Sensor Routing Topology Control in Underground Mine Rescue Operation Based on Particle Swarm Optimization

Ansong

Yao

Huang

2013

International Journal of Distributed Sensor Networks

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The performance of a proposed compact radial basis function was compared with the sigmoid basis function and the gaussian-radial basis function neural networks in 3D wireless sensor routing topology control, in underground mine rescue operation. Optimised errors among other parameters were examined in addition to scalability and time efficiency. To make the routing path efficient in emergency situations, the sensor sequence and deployment as well as transmission range were carefully considered. In times of danger and unsafe situations, data-mule robot with Through The Earth (TTE) radio would be used to carry water, food, equipments, and so forth to miners underground and return with information. Using Matlab, the optimised vectors with high survival rate and fault tolerant, based on rock type, were generated as inputs for the neural networks. Particle swarm optimisation with adaptive mutation was used to train the neurons. Computer simulation results showed that the neural network learning algorithm minimized the error between the neural network output and the desired output such that final error values were either the same as the error goal or less than the error goal. Thus, the proposed algorithm shows high reliability and superior performance.

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Neural network based mobile phone localization using Bluetooth connectivity

Cited by 30 publications

References 11 publications

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

Radial and Sigmoid Basis Function Neural Networks in Wireless Sensor Routing Topology Control in Underground Mine Rescue Operation Based on Particle Swarm Optimization

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