Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

Wireless sensor networks (WSNs) have many applications in the field of disaster management, military, healthcare and environmental monitoring. Capability of WSNs is further enhanced by the efficient localization algorithms. Localization is the process by which a sensor node determines its own location after deployment. Neural approaches are gaining popularity in evolving new localization algorithms that are capable of optimizing various parameters of WSNs. In this paper, we analyse two backpropagation algorithms based on multi-layer Perceptron (MLP) neural network. The network is trained using static sensor nodes placed in a grid with their coordinates known. The input values are distances from each anchor nodes to a particular sensor node. The output is the actual coordinates of the sensor nodes. After training, the network will be able to predict the coordinates of unknown sensor nodes. This MLP model is analyzed for Bayesian regularization and Levenberg-Marquardt training algorithm. Both algorithms are tested for the robustness and cross-validation. The simulation results demonstrate the effectiveness of the proposed model on localization error.

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Comparative analysis of Bayesian regularization and Levenberg-Marquardt training algorithm for localization in wireless sensor network

Intelligent sensing, communication, and computing is leading the development of Wireless Sensor Networks (WSNs) and its applications. Artificial Neural Network (ANNs) is being extensively used to develop localization framework in WSN. In this paper, we present a comparative analysis of some conjugate gradient based Feedforward Artificial Neural Network (FFANNs) for developing localization framework in wireless sensor networks (WSNs). Localization is the process by which the sensor nodes in the network can identify their own location in the overall network. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Artificial neural network based localization framework are gaining importance due to faster speed of convergence and low cost of computation. We present analysis of conjugate gradient backpropagation with Powell-Beale restarts, conjugate gradient backpropagation with Fletcher-Reeves update, conjugate gradient backpropagation with Polak-Ribiere updates, one-step secant, and scaled conjugate gradient backpropagation training algorithms in this paper. Comprehensive evaluation of these training algorithms is done in perfect simulation scenario. The proposed method effectively demonstrates that conjugate gradient FFANNs based sensor motes can be designed for developing cost-effective localization framework.

Artificial Neural Networks for developing localization framework in Wireless Sensor Networks

Localization of nodes in wireless sensor networks without the use of GPS is important for applications such as military surveillance, environmental monitoring, robotics, domotics, animal tracking, and many others. Low cost and energy efficient sensors require methods that compute their position using indirect information such as RSSI (Received Signal Strength Indicator). This work presents an artificial neural networks (ANNs) approach to localization in wireless sensor networks through the adjustment of the ANNs structures using Genetic Algorithms. A population of feedforward ANNs containing their structure in a genetic code is evolved during 20 generations. Each individual is evaluated through the training of the artificial neural network and further calculation of its root mean square error for all the testing set. The RSSI measurements were used as the artificial neural networks inputs to localize the nodes. The approach was tested using the MATLAB-based Probabilistic Wireless Network Simulator (Prowler) to collect the artificial neural networks input data, under simulated static indoor network environment of 26×26 meters with 8 anchor nodes, i.e., nodes with awareness of their positions. The MATLAB's genetic algorithms and artificial neural networks toolboxes were used. Results using the best artificial neural network structure found after optimization had a root mean square error of 0.41 meters, a maximum error of 1.07 meters and a minimum error of 0.014 meters.

An approach to localization scheme of wireless sensor networks based on artificial neural networks and Genetic Algorithms

Node localization in wireless sensor networks (WSNs) is important for applications such as military surveillance, environmental monitoring, robotics, and many others. The sensor motes used in this type of application present low-power and low-cost profile. Hence, they require methods that compute their positions using indirect information such as Received Signal Strength Indicator (RSSI). This work presents Genetic Algorithms and Simulated Annealing optimization methods applied (independently) in artificial neural networks (ANNs) aiming node localization in WSNs. The RSSI measurements were used as the ANNs inputs to localize the nodes. This receiver-based approach was tested using MATLAB and Probabilistic Wireless Network Simulator (Prowler) to collect the ANNs input data, under simulated static indoor network environment. Results using the best ANN structure found after optimization using GA had a root mean square error of 0.39 meter against the 0.61 meter reached through the SA algorithm.

Genetic Algorithms and Simulated Annealing optimization methods in wireless sensor networks localization using artificial neural networks

Study of localization scheme base on neural network for wireless sensor networks

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Study of localization scheme base on neural network for wireless sensor networks