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Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

Raghavendra V. Kulkarni, Ganesh Kumar Venayagamoorthy
2011 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment.  ...  Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm.  ...  Their algorithm PSO-Opt-Alloc uses PSO to determine optimal power allocation in the cases of both independent and correlated observations.  ... 
doi:10.1109/tsmcc.2010.2054080 fatcat:o4ltdhqq5vfszlsaey5vy5ou2m

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Md. Adnan, Mohammd Razzaque, Ishtiaque Ahmed, Ismail Isnin
2013 Sensors  
algorithms, namely, particle swarm optimization, ant colony optimization and genetic OPEN ACCESS Sensors 2014, 14 300 algorithm.  ...  Unfortunately, most conventional or classical optimization algorithms like the Hessian matrix-based methods and gradient-based methods [3, 4] are not suitable for WSNs.  ...  Their algorithm uses PSO to determine optimal-power allocation in the cases of both independent and correlated observations in a Gaussian sensor network.  ... 
doi:10.3390/s140100299 pmid:24368702 pmcid:PMC3926559 fatcat:lndlgfl2ajflxhvninklnukww4

Research Progress on Synergistic Technologies of Agricultural Multi-Robots

Wenju Mao, Zhijie Liu, Heng Liu, Fuzeng Yang, Meirong Wang
2021 Applied Sciences  
robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation  ...  While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development  ...  The fusion algorithms are mainly based on classical EKF and particle filtering.  ... 
doi:10.3390/app11041448 fatcat:sk5eu5j62vg6rkys62qhxzhphq

A Review of Machine Learning Algorithms for estimating Critical Quality Attributes from Multi-Sensor Data

Niall O' Mahony, Trevor Murphy, Krishna Panduru, Daniel Riordan, Joseph Walsh
2016 International Journal of Sustainable Energy Development  
Several machine learning algorithms including Adaptive Neuro-Fuzzy Inference Systems, Neural Networks and Genetic Algorithms were implemented using MATLAB® Software and compared in terms of accuracy (MSE  ...  The article presents a comparison of machine learning algorithms applied to sensor data collected for a polymerisation process.  ...  Many fusion algorithms have been developed to perform tasks such as deciding on the weighting applied to sensor outputs based on their reliability/ relevance, handling asynchronous multi-rate multi-sensor  ... 
doi:10.20533/ijsed.2046.3707.2016.0038 fatcat:gikxeojptfervkrk47dhnamjfa

On computing mobile agent routes for data fusion in distributed sensor networks

Q. Wu, N.S.V. Rao, J. Barhen, S.S. Iyenger, V.K. Vaishnavi, H. Qi, K. Chakrabarty
2004 IEEE Transactions on Knowledge and Data Engineering  
Index Terms-Genetic algorithms, mobile agents, distributed sensor networks. ae 740  ...  We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest  ...  In conventional fusion architectures, all the sensor data is sent to a central location where it is fused.  ... 
doi:10.1109/tkde.2004.12 fatcat:stmvm65rnbgm3nrhptukbiz7jq

IHPG Algorithm for Efficient Information Fusion in Multi-Sensor Network via Smoothing Parameter Optimization

Wen-Tsai Sung, Ching-Li Hsiao
2013 Informatica  
This study explores the quality monitoring experiment by three existing neural network approaches to data fusion in wireless sensor module measurements.  ...  This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm.  ...  (b) PSO data fusion error probability is improved when observations are correlated with each other. (c) Comparison the general PSO-GA with IHPG algorithm in data fusion error probability.  ... 
doi:10.15388/informatica.2013.397 fatcat:7ki66fw5jbgkheicon7qzmfwym

Sensor Fusion for Mobile Robot Navigation

M. Kam, Xiaoxun Zhu, P. Kalata
1997 Proceedings of the IEEE  
We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location.  ...  The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches  ...  Kalman filter. 1) Rule-Based Sensor Fusion: To avoid the difficulty in modeling the sensor readings under a unified statistical model, several robotic applications use rule-based algorithms.  ... 
doi:10.1109/jproc.1997.554212 fatcat:adofsedn2jd2bauazbslm46jea

Hybrid Approaches to Address Various Challenges in Wireless Sensor Network for IoT Applications: Opportunities and Open Problems

Pallavi Joshi, Ajay Singh Raghuvanshi
2021 International Journal of Computer Networks And Applications  
This paper gives a lucid comparison of many state-of-the-art optimization algorithms and descriptive and statistical analysis for discussed issues and algorithms associated with them.  ...  The sensors have limited battery power and are often not rechargeable.  ...  ) which is location-based, the Bayesian fusion algorithm (high level stage).  ... 
doi:10.22247/ijcna/2021/209186 fatcat:isjbrui3wjb6bcj5ifyzt6jboi

A general framework of multiple coordinative data fusion modules for real-time and heterogeneous data sources

Shafiza Ariffin Kashinath, Salama A. Mostafa, David Lim, Aida Mustapha, Hanayanti Hafit, Rozanawati Darman
2021 Journal of Intelligent Systems  
The framework uses these features to identify three congestion periods, which are the nonpeak period with a congestion degree of 0.178 and a variance of 0.061, a medium peak period with a congestion degree  ...  of 0.588 and a variance of 0.0593, and a peak period with a congestion degree of 0.796 and a variance of 0.0296.  ...  All authors have seen and agreed with the contents of the manuscript and there is no financial interest to report.  ... 
doi:10.1515/jisys-2021-0083 fatcat:hsqlmiy3zbffrlrkpv6tvnhc5m

Countersniper system for urban warfare

Ákos Lédeczi, Miklós Maróti, Gyula Simon, András Nádas, Péter Völgyesi, György Balogh, Branislav Kusy, János Sallai, Gábor Pap, Sebestyén Dóra, Károly Molnár
2005 ACM transactions on sensor networks  
In this article, in addition to the overall system architecture, the middleware services and the unique sensor fusion algorithms are described.  ...  The localization accuracy of the system in open terrain is competitive with that of existing centralized countersniper systems.  ...  Instead, a genetic algorithm was applied for shockwave fusion. The real-time fusion algorithm receives shockwave measurements from the sensor network.  ... 
doi:10.1145/1105688.1105689 fatcat:cqqwinovargnfam2tjli5q3s4a

A Self-Synthesis Approach to Perceptual Learning for Multisensory Fusion in Robotics

Cristian Axenie, Christoph Richter, Jörg Conradt
2016 Sensors  
The system autonomously associates and combines them into a coherent representation, given incoming observations. These processes are adaptive and involve learning.  ...  Its intrinsic scalability, parallelisation, and automatic adaptation to unforeseen sensory perturbations make our approach a promising candidate for robust multisensory fusion in robotic systems.  ...  In order to obtain informative quantities into the algorithm we combine the 3 components of each sensor in derived cues relevant for each degree of freedom.  ... 
doi:10.3390/s16101751 pmid:27775621 pmcid:PMC5087536 fatcat:bbxdsc7hnbdu3o7gdx6fsmdaua

Differential Evolution in Wireless Communications: A Review

Hilary I Okagbue, Muminu O Adamu, Timothy A Anake
2019 International Journal of Online and Biomedical Engineering (iJOE)  
Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms.  ...  It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied.  ...  algorithm [44] [45] , bare bones [46] and modified bare bones swarm optimizers [47] , ant bee colony algorithm [48] and genetic algorithm [49] .  ... 
doi:10.3991/ijoe.v15i11.10651 fatcat:rd6l52wiuned7fv4epu3qtcbnq

Fusion of soft computing and hard computing: computational structures and characteristic features

S.J. Ovaska, A. Kamiya, YangQuan Chen
2006 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
The principal aim is to develop computationally intelligent hybrid systems that are straightforward to analyze, with highly predictable behavior and stability, and with computational burden that is no  ...  We classify the different fusion schemes to 12 core categories and six supplementary categories, and discuss the characteristic features of SC and HC constituents in practical fusion implementations.  ...  A descriptive example of such a loose union is an advanced elevator group dispatcher, where the primary call allocation algorithm is typically based on soft computing methods, while the backup algorithm  ... 
doi:10.1109/tsmcc.2005.855528 fatcat:hq2odhkzwbhbhdiwk5gctrun3m

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception and Active Vision

Jorge Pena Queralta, Jussi Taipalmaa, Bilge Can Pullinen, Victor Kathan Sarker, Tuan Nguyen Gia, Hannu Tenhunen, Moncef Gabbouj, Jenni Raitoharju, Tomi Westerlund
2020 IEEE Access  
development of multi-modal sensor fusion algorithms.  ...  IPP approaches have been shown to outperform more traditional planning algorithms such as greedy algorithms and genetic algorithms [135] .  ...  His current research interests include multimedia content-based analysis, indexing and retrieval, machine learning, nonlinear signal and image processing and analysis, voice conversion, and video processing  ... 
doi:10.1109/access.2020.3030190 fatcat:exigopjplzgfzlghxvr7s3l3di

Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks

Atia Javaid, Nadeem Javaid, Zahid Wadud, Tanzila Saba, Osama Sheta, Muhammad Saleem, Mohammad Alzahrani
2019 Sensors  
The decentralized classification fusion problem was the reason to use the belief function-based decision fusion approach in Wireless Sensor Networks (WSNs).  ...  With the consideration of improving the belief function fusion approach, we have proposed four classification techniques, namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM  ...  These nodes have to communicate with each other directly or indirectly. There is a central node where the data are merged and the decisions are made about the sensor data.  ... 
doi:10.3390/s19061334 fatcat:zeu557lk5vgdli3co7x3mqqqkq
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