SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

Chen, Zhi; Li, Shuai; Yue, Wenjing

doi:10.1155/2014/121278

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…The ref. [18] investigated the latest state-of-the-art approaches in low-cost fixed relay clustering problems in energy-harvesting wireless sensor networks (EHWSNs). The author proposed random relay fixed clustering for EHWSNs.…”

Section: Related Workmentioning

confidence: 99%

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

Al-Nader,

Lasebae,

Raheem

2023

Electronics

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Technological advances in the internet of things (IoT) allowed a low-cost, yet small sensor device to operate with limited power in a dynamic harsh environment where human intervention is impossible. The wireless sensor network (WSN) is an example of the IoT in which physical devices’ software and sensors can interconnect to provide application services. It is important that such applications be dependable to meet the required quality of service (QoS) and function as expected. Consequently, the multi-objective optimization (MOO) problem in WSNs aims to address the trade-off among coverage, connectivity, and network lifetime requirements. Node scheduling is one approach of many used to optimize energy in WSNs. The contribution of this work is the proposal of a self-organizing feature map (SOFM) to enhance the node scheduling in WSNs. The proposed SOFM node-scheduling algorithm aims to spatially explore the state space domain and obtain an optimal solution. In our experiment, the proposed SOFM node-scheduling algorithm is evaluated against a comparable algorithm, namely the BAT node-scheduling algorithm, via MATLAB simulator. The results showed that the SOFM node-scheduling algorithm outperformed the latter by 27% and 28% for the maximum and minimum coverage, respectively, with similar performance of 99% of connectivity and network lifetime.

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Section: Related Workmentioning

confidence: 99%

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

Al-Nader,

Lasebae,

Raheem

2023

Electronics

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“…The cross-fusion FCN (fully convolutional neural networks) performed better than single-modality methods and other fusion approaches designed for road surface detection, which were presented by the authors. There are numerous artificial neural network solutions and models developed for the task of sensor fusion such as BPNN [43] (back propagation neural net), which has a fusion strategy that uses a fully connected neural network trained with backpropagation and Bayesian inference, SOFM [44] (self-organizing feature maps), a fusion method that uses self-organizing feature maps to fuse data for wireless sensor networks and to form a hierarchical network structure and complete cluster head selection by competitive learning among nodes, or ARTMAP [45] (adaptive resonance theory map), which describes a fusion method with a neural network model that produces one-to-many and many-to-one mappings from input vectors to output classes with the purpose of terrain and object classification from complex and conflicting data.…”

Section: Black Box Sensor Fusionmentioning

confidence: 99%

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

Muresan

Giosan

Nedevschi

2020

Sensors

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The stabilization and validation process of the measured position of objects is an important step for high-level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super-sensor. The result of the data aggregation may end up with errors such as false detections, misplaced object cuboids or an incorrect number of objects in the scene. The stabilization and validation process is focused on mitigating these problems. The current paper proposes four contributions for solving the stabilization and validation task, for autonomous vehicles, using the following sensors: trifocal camera, fisheye camera, long-range RADAR (Radio detection and ranging), and 4-layer and 16-layer LIDARs (Light Detection and Ranging). We propose two original data association methods used in the sensor fusion and tracking processes. The first data association algorithm is created for tracking LIDAR objects and combines multiple appearance and motion features in order to exploit the available information for road objects. The second novel data association algorithm is designed for trifocal camera objects and has the objective of finding measurement correspondences to sensor fused objects such that the super-sensor data are enriched by adding the semantic class information. The implemented trifocal object association solution uses a novel polar association scheme combined with a decision tree to find the best hypothesis–measurement correlations. Another contribution we propose for stabilizing object position and unpredictable behavior of road objects, provided by multiple types of complementary sensors, is the use of a fusion approach based on the Unscented Kalman Filter and a single-layer perceptron. The last novel contribution is related to the validation of the 3D object position, which is solved using a fuzzy logic technique combined with a semantic segmentation image. The proposed algorithms have a real-time performance, achieving a cumulative running time of 90 ms, and have been evaluated using ground truth data extracted from a high-precision GPS (global positioning system) with 2 cm accuracy, obtaining an average error of 0.8 m.

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“…Patra et al, (2011) proposed the application of KSOM technique to cluster nodes in a heterogeneous sensor network that enhanced the node transmission power to enhance the energy saving in the network. Chen et al, (2014) proposed that the well-constructed network topology offers a vital support for target tracking, data fusion and routing in WSNs. The Self Organization Feature Map (SOFM) technique is a type of artificial neural networks that has self-learning and self-organizing features.…”

Section: Related Workmentioning

confidence: 99%

K-Means Clustering in WSN with Koheneon SOM and Conscience Function

Bataineh¹,

Samkari²,

Abdualla³

et al. 2019

MAS

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Wireless Sensor Networks (WSNs) are broadly utilized in the recent years to monitor dynamic environments which vary in a rapid way over time. The most used technique is the clustering one, such as Kohenon Self Organizing Map (KSOM) and K means. This paper introduces a hybrid clustering technique that represents the use of K means clustering algorithm with the KSOM with conscience function of Neural Networks and applies it on a certain WSN in order to measure and evaluate its performance in terms of both energy and lifetime criteria. The application of this algorithm in a WSN is performed using the MATLAB software program. Results demonstrate that the application of K-means clustering algorithm with KSOM algorithm enhanced the performance of the WSN which depends on using KSOM algorithm only in which it offers an enhancement of 11.11% and 3.33% in terms of network average lifetime and consumed energy, respectively. The comparison among the current work and a previous one demonstrated the effectiveness of the proposed approach in this work in terms of reducing the energy consumption.

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SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

Cited by 13 publications

References 16 publications

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

K-Means Clustering in WSN with Koheneon SOM and Conscience Function

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